Generative AI in healthcare refers to the application of generative artificial intelligence techniques and models in various aspects of the healthcare industry. It involves using machine learning algorithms to generate new and original content that is relevant to healthcare, such as medical images, personalized treatment plans, and more.
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
Generative AI holds significant promise in healthcare, there are also challenges related to data privacy, model interpretability, and regulatory compliance that need to be addressed. Ethical considerations and thorough validation processes are crucial to ensure the responsible and safe application of generative AI techniques in healthcare.
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
Game changing AI Startups need great AI system. Learn the basic concepts and importance of AI to change the way you build a Startup. Its time to identify and develop skill sets to make better decisions.
Accelerate #AI workloads with Tesla V100 on #E2ECloud : http://bit.ly/E2EGPU
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
leewayhertz.com-Generative AI in manufacturing.pdfKristiLBurns
The manufacturing industry stands out as a prominent beneficiary, capitalizing on the advancements and potential of AI to enhance its processes and unlock new opportunities. Among the various types of AI, generative AI, known for its content creation and enhancement capabilities, is playing a significant and distinct role in shaping the advancement of manufacturing practices.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Understanding generative AI models A comprehensive overview.pdfStephenAmell4
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Some Preliminary Thoughts on Artificial Intelligence - April 20, 2023.pdfKent Bye
Bye, K. (2023, April 20). Some Preliminary Thoughts on Artificial Intelligence. [Presentation] The King Library Experiential Virtual Reality Lab (KLEVR) Tech Talks: AI Tools, Tips, & Traps; San Jose State University, San Jose, California via Zoom.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Exploring the applications of generative AI in healthcare.pdfStephenAmell4
Generative AI in healthcare refers to the application of generative artificial intelligence techniques and models in various aspects of the healthcare industry. It involves using machine learning algorithms to generate new and original content that is relevant to healthcare, such as medical images, personalized treatment plans, and more.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
leewayhertz.com-Generative AI in manufacturing.pdfKristiLBurns
The manufacturing industry stands out as a prominent beneficiary, capitalizing on the advancements and potential of AI to enhance its processes and unlock new opportunities. Among the various types of AI, generative AI, known for its content creation and enhancement capabilities, is playing a significant and distinct role in shaping the advancement of manufacturing practices.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Understanding generative AI models A comprehensive overview.pdfStephenAmell4
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Explainability for Natural Language ProcessingYunyao Li
Tutorial at AACL'2020 (http://www.aacl2020.org/program/tutorials/#t4-explainability-for-natural-language-processing).
More recent version: https://www.slideshare.net/YunyaoLi/explainability-for-natural-language-processing-249912819
Title: Explainability for Natural Language Processing
@article{aacl2020xaitutorial,
title={Explainability for Natural Language Processing},
author= {Dhanorkar, Shipi and Li, Yunyao and Popa, Lucian and Qian, Kun and Wolf, Christine T and Xu, Anbang},
journal={AACL-IJCNLP 2020},
year={2020}
Presenter: Shipi Dhanorkar, Christine Wolf, Kun Qian, Anbang Xu, Lucian Popa and Yunyao Li
Video: https://www.youtube.com/watch?v=3tnrGe_JA0s&feature=youtu.be
Abstract:
We propose a cutting-edge tutorial that investigates the issues of transparency and interpretability as they relate to NLP. Both the research community and industry have been developing new techniques to render black-box NLP models more transparent and interpretable. Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP researchers, our tutorial has two components: an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study of individuals working on real-world NLP projects at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability in NLP. Then, we will discuss explainability for NLP tasks and report on a systematic literature review of the state-of-the-art literature in AI, NLP, and HCI conferences. The second component reports on our qualitative interview study which identifies practical challenges and concerns that arise in real-world development projects which include NLP.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
Some Preliminary Thoughts on Artificial Intelligence - April 20, 2023.pdfKent Bye
Bye, K. (2023, April 20). Some Preliminary Thoughts on Artificial Intelligence. [Presentation] The King Library Experiential Virtual Reality Lab (KLEVR) Tech Talks: AI Tools, Tips, & Traps; San Jose State University, San Jose, California via Zoom.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Exploring the applications of generative AI in healthcare.pdfStephenAmell4
Generative AI in healthcare refers to the application of generative artificial intelligence techniques and models in various aspects of the healthcare industry. It involves using machine learning algorithms to generate new and original content that is relevant to healthcare, such as medical images, personalized treatment plans, and more.
leewayhertz.com-Understanding generative AI models A comprehensive overview.pdfKristiLBurns
Generative AI refers to a branch of artificial intelligence that focuses on enabling machines to generate new and original content. Unlike traditional AI systems that follow predefined rules and patterns, generative AI leverages advanced algorithms and neural networks to autonomously produce outputs that mimic human creativity and decision-making.
Artificial Intelligence Service in HealthcareAnkit Jain
It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of AI startups are active launching innovative services related to healthcare.
Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare.
Innovating Medical-Education with AI Final-2.pptxsbattle
Innovating Medical Education using AI, Agents, LLMs, and deep learning. The benefits and dangers of AI are explored. Popular tools for faculty presented. The possibilities of new faculty/student interaction using Precision Education are offered. Various video excerpts from important innovators is embedded.
POTENTIAL IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE(AI) ON THE FINANCIAL I...IJCI JOURNAL
Presently, generative AI has taken center stage in the news media, educational institutions, and the world
at large. Machine learning has been a decades-old phenomenon, with little exposure to the average person
until very recently. In the natural world, the oldest and best example of a “generative” model is the human
being - one can close one’s eyes and imagine several plausible different endings to one’s favorite TV show.
This paper focuses on the impact of generative and machine learning AI on the financial industry.
Although generative AI is an amazing tool for a discriminant user, it also challenges us to think critically
about the ethical implications and societal impact of these powerful technologies on the financial industry.
It requires ethical considerations to guide decision-making, mitigate risks, and ensure that generative AI is
developed and used to align with ethical principles, social values, and in the best interests of communities.
The Revolutionary Progress of Artificial Inteligence (AI) in Health CareSindhBiotech
This Lecture is presented by our 2k23 volunteer Hina Nawaz, she is from Karachi, Pakistan, and she is covering "The Revolutionary Progress of Artificial Inteligence (AI) in Health Care".
Youtube: https://youtu.be/vhJRCj5ZgJc
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
GENERATIVE AI AUTOMATION: THE KEY TO PRODUCTIVITY, EFFICIENCY AND OPERATIONAL...ChristopherTHyatt
Generative AI Automation combines the creative prowess of generative artificial intelligence with the efficiency of automation, revolutionizing industries. From content creation and design to healthcare diagnostics and financial analysis, this synergistic technology streamlines processes, enhances creativity, and offers unprecedented insights. However, ethical considerations, including data privacy and potential job displacement, necessitate careful implementation for a responsible and sustainable future.
Shaping the future of ai for management consulting.pdfJamieDornan2
In recent years, the integration of artificial intelligence has redefined the landscape of AI management consulting, ushering in an era of unprecedented capabilities and efficiencies. For consultants, this iteration of artificial intelligence holds the power to dissect data intricacies, elevate decision-making processes, and facilitate efficient problem-solving, effectively condensing hours, if not days, of traditional work. On the client side, advanced data analysis augments the quality and depth of recommendations offered by consultants.
Blockchain in Healthcare - An Overview.pdfJamieDornan2
Blockchain technology is like a new spark in the financial industry. It isn't like the old financial systems because it's decentralized and can't be changed. This tech can change traditional methods and systems. Blockchain has several benefits. It makes things safer and more efficient. If we use blockchain technology in different parts of finance, there's the exciting chance to change the way the industry works.
Blockchain Use Cases and Applications by Industry.pdfJamieDornan2
Blockchain use cases encompass many scenarios where blockchain technology can be applied to solve specific problems or enhance existing processes. This includes not being controlled by one entity, being able to see what's going on, not being changeable easily, and its cryptographic security.
Web3 Use Cases in Real-World Applications.pdfJamieDornan2
Web3 is the next version of the internet. It promotes decentralised systems, enhanced safety, and a greater emphasis on user autonomy. Web3, unlike previous versions, aims to empower people by eliminating intermediaries and encouraging direct peer interactions. This ushers in a new era in which users get more control over their internet activities.
How Does Blockchain Identity Management Revolutionise Financial Sectors.pdfJamieDornan2
Conventional identity management systems often grapple with issues like data breaches, identity theft, and the inability to seamlessly communicate with other systems. With the arrival of blockchain technology, we've entered a new period. This period brings encouraging answers to tackle our age-old problems.
AI use cases in legal research - An Overview.pdfJamieDornan2
Legal research is essential in law practice, encompassing the systematic study and analysis of legal issues and statutes to address specific legal questions or contribute to the broader field of law. At its core, legal research involves a methodical process of identifying legal problems, gathering relevant facts, and finding and interpreting applicable laws and cases.
The impact of AI in construction - An Overview.pdfJamieDornan2
Artificial Intelligence (AI) has revolutionized the construction industry, ushering in a new era of efficiency and innovation. AI applications in construction, such as predictive analytics and machine learning, streamline project management by forecasting potential delays and optimizing resource allocation.
AI has made significant inroads into various fields, including project management. AI can enhance project management by automating repetitive tasks, providing data-driven insights, and improving decision-making. Here are some use cases of AI in project management, along with explanations and examples:
AI in market research involves integrating Machine Learning (ML) algorithms into traditional methods, such as interviews, discussions, and surveys, to enhance the research process. These algorithms enable real-time data collection and analysis, predicting trends and extracting valuable patterns. This process results in high-quality, up-to-date insights that transparently capture even minor market changes.
Conversational AI Transforming human-machine interaction.pdfJamieDornan2
Conversational AI is a subset of artificial intelligence that enables human-like interactions between computers and humans using natural language. It leverages natural language processing (NLP) and machine learning to allow machines to understand, process, and respond to human language in a way that mimics natural conversation.
These systems combine techniques from several domains, including NLP for understanding textual or spoken inputs, machine learning to improve response accuracy over time, and speech recognition to handle voice interactions.
Generative AI is a branch of AI that aims to enable machines to produce new and original content. Unlike traditional AI systems, which rely on predefined rules and patterns, generative AI employs advanced algorithms and neural networks to generate outputs that autonomously imitate human creativity and decision-making.
A comprehensive guide to prompt engineering.pdfJamieDornan2
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs. It involves crafting clear and specific instructions and allowing the model sufficient time to process information. By carefully engineering prompts, practitioners can harness the capabilities of LLMs to achieve different goals.
How AI in business process automation is changing the game (1).pdfJamieDornan2
Business Process Automation (BPA) stands as an essential paradigm shift in modern business operations. By melding technological advancements with strategic objectives, BPA offers a pathway to a streamlined, efficient, and strategically aligned business model. Its multifaceted applications, ranging from HR to marketing, exemplify the transformative potential of automation, setting a benchmark for the future of business innovation.
AI in trade promotion optimization.pdfJamieDornan2
Trade promotion optimization refers to the process of using advanced analytics, algorithms, and data-driven insights to enhance the planning, execution, and evaluation of trade promotions. The goal is to maximize the Return on Investment (ROI) from promotional activities while minimizing waste and inefficiencies. TPO takes a comprehensive approach, considering factors such as pricing, timing, promotion duration, product assortment, and targeting to create a well-rounded strategy that resonates with both consumers and retailers.
The Decision Transformer model, introduced by Chen L. et al. in “Decision Transformer: Reinforcement Learning via Sequence Modeling,” transforms the reinforcement learning (RL) landscape by treating RL as a conditional sequence modeling problem.
Exploratory Data Analysis - A Comprehensive Guide to EDA.pdfJamieDornan2
EDA or Exploratory Data Analysis is a method of examining and understanding data using multiple techniques like visualization, summary statistics and data transformation to abstract its core characteristics. EDA is done to get a sense of data and discover any potential problems or issues which need to be addressed and is generally performed before formal modeling or hypothesis testing.
How to build an AI-powered chatbot.pdfJamieDornan2
A chatbot is an Artificial Intelligence (AI) program that simulates human conversation by interacting with people via text or speech. Chatbots use Natural Language Processing (NLP) and machine learning algorithms to comprehend user input and deliver pertinent responses.
The Action Transformer Model represents a groundbreaking technological advancement that enables seamless communication with other software and applications, effectively bridging humanity and the digital realm.
How to build machine learning apps.pdfJamieDornan2
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
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- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
generative AI in healthcare.pdf
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Applications of generative AI in healthcare
leewayhertz.com/generative-ai-in-healthcare
The emergence of generative AI has ushered in a new era of possibilities in multiple domains
and industries. This ever-evolving technology has the potential to reshape the way we
approach and solve complex problems, offering transformative solutions and innovative
outcomes that were once unimaginable. With its ability to generate, simulate, and optimize,
generative AI opens up new horizons and propels us into an era of limitless potential.
Like many other industries embracing technological advancements, the healthcare
landscape is on the cusp of transformative progress driven by the emergence of generative
AI. As advanced machine learning algorithms continue to evolve, they are reshaping multiple
aspects of the healthcare industry, transcending the boundaries of traditional approaches.
From diagnosis and treatment to drug discovery and personalized medicine, generative AI is
poised to transform how healthcare professionals approach complex medical challenges.
By harnessing the capabilities of generative AI, the healthcare industry is poised to witness
remarkable advancements that have the capability to enhance patient outcomes, improve
medical research, and reshape the entire healthcare landscape. This article will dive deep
into the profound impact of generative AI in healthcare and delve into its applications,
benefits and other key areas.
What is generative AI?
Prominent generative AI models
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Generative AI in healthcare
Benefits of generative AI in healthcare
Applications of generative AI in healthcare
Generative AI in healthcare: Real world examples
How does generative AI help in drug discovery?
How does GENTRL work?
What is generative AI?
Generative AI, or generative artificial intelligence, refers to a branch of AI that focuses on
creating models capable of generating new and original content. Unlike traditional AI models
that rely on predefined rules and patterns, generative AI models have the ability to learn from
existing data and generate new outputs that mimic the characteristics of the training data.
At the core of generative AI is the concept of generative models. These models are designed
to learn the underlying patterns and structures within a dataset and use that knowledge to
generate new instances that resemble the original data. Generative models are trained using
large datasets and use probabilistic techniques to capture the training data distribution.
Generative AI models have various applications, including image synthesis, text generation,
music composition, and even video generation. These models have the ability to generate
new and unique content that exhibits the characteristics and style of the training data.
However, it’s important to note that generative AI models are not simply copying existing
data but learning underlying patterns and structures to generate novel outputs.
Prominent generative AI models
Several prominent generative AI models have made significant contributions to the field of
artificial intelligence. Here are a few examples and a brief overview of how they work:
Generative Adversarial Networks (GANs)
GANs consist of two components: a generator and a discriminator. The generator aims to
generate synthetic data samples, such as images or text, that resemble real data samples.
The discriminator, on the other hand, tries to distinguish between the real and generated
samples. Through an adversarial training process, the generator learns to produce
increasingly realistic samples, while the discriminator learns to become more accurate in
distinguishing between real and generated samples. GANs have been successfully applied
in various domains, including image synthesis, text generation, and video generation.
Variational Autoencoders (VAEs)
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VAEs are generative models that learn to encode and decode data samples. They consist of
an encoder network that maps input data to a latent space representation and a decoder
network that reconstructs the original data from the latent space. VAEs are trained by
maximizing the Evidence Lower Bound (ELBO), which encourages the learned latent space
to capture meaningful and continuous data representations. VAEs can generate new
samples by sampling from the latent space and decoding the samples back into the original
data space. VAEs have been widely used for image generation, text generation, and
anomaly detection tasks.
Transformer models
Transformer models, such as the GPT (Generative Pre-trained Transformer) series, have
transformed natural language processing and text generation tasks. Transformers employ a
self-attention mechanism that allows the model to capture long-range dependencies in the
input data. These models are typically trained in an unsupervised or semi-supervised
manner on large amounts of text data to learn the statistical properties of language. Once
trained, they can generate coherent and contextually relevant text by conditioning on an
input prompt or by autonomously generating text from scratch.
Autoregressive models
Autoregressive models, including models like LSTM (Long Short-term Memory) and GRU
(Gated Recurrent Unit), generate sequences by modeling the conditional probability of each
element in the sequence given the previous elements. These models have a recurrent
structure that allows them to capture dependencies over time or sequence. During training,
the models are exposed to input sequences and learn to predict the next element in the
sequence. Autoregressive models have been used for tasks such as language modeling,
speech recognition, and music generation.
These are only a handful of prominent examples of generative AI models, each with its own
unique approach to generating new data samples. The field of generative AI is constantly
evolving, and researchers continue to develop new models and techniques for generating
realistic and creative outputs in various domains.
Generative AI in healthcare
Generative AI in healthcare refers to the application of generative artificial intelligence
techniques and models in various aspects of the healthcare industry. It involves using
machine learning algorithms to generate new and original content that is relevant to
healthcare, such as medical images, personalized treatment plans, and more.
Benefits of generative AI in healthcare
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The benefits of generative AI in healthcare are numerous, some of which include:
Improved efficiency and cost reduction
1. Automating repetitive tasks: Generative AI can automate routine and repetitive tasks
in healthcare, such as data entry, administrative processes, and image analysis. This
automation saves time for healthcare professionals, enabling them to give attention to
more complex and critical aspects of patient care.
2. Streamlined workflows and resource utilization: Generative AI can optimize
healthcare workflows by allocating resources and prioritizing tasks intelligently. It can
assist in scheduling appointments, managing patient flow, and coordinating care,
leading to smoother operations and efficient resource utilization. This can result in cost
reduction and improved patient satisfaction.
Enhanced accuracy and precision
1. Reduction of human error and biases: Human errors and biases are inherent in
healthcare, and they can impact diagnoses, treatment decisions, and patient outcomes.
Generative AI models, when properly trained and validated, can minimize these errors
by providing objective and consistent analysis. They can mitigate the effects of human
biases and ensure more accurate and unbiased predictions and diagnoses.
2. More accurate predictions and diagnoses: Generative AI models can analyze vast
amounts of patient data, including medical records, genetic information, and
environmental factors. By integrating and analyzing these data points, AI models can
identify patterns and relationships that may not be apparent to humans. This can lead
to more accurate disease progression predictions, personalized treatment plans, and
early detection of potential risks.
Facilitation of medical education and training
1. Virtual simulations and training scenarios: Generative AI can create realistic virtual
simulations and training scenarios for medical education. These simulations offer
healthcare professionals the opportunity to replicate a wide range of medical conditions
and procedures, creating a safe and controlled environment for skill practice and
refinement. It enables hands-on training without risking patient safety and provides
opportunities for repeated practice and feedback.
2. Access to diverse case studies and expert knowledge: Generative AI can generate
synthetic medical data, including patient profiles, medical images, and clinical
scenarios. This synthetic data can be used for educational purposes, providing access
to diverse case studies and rare conditions. It enables healthcare professionals to
enhance their knowledge, learn from experts, and develop expertise in specialized
areas that may be limited in real-world patient encounters.
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Generative AI has shown significant potential in various applications within the healthcare
industry. Let us explore each of these applications in detail:
Medical imaging
Generative AI techniques have been extensively used to enhance medical imaging analysis
and diagnostics. Prominent image-generating models like DALL.E and Stable Diffusion,
among others, can be effectively used for this purpose. Here are some key aspects of the
impact of generative AI in medical imaging:
1. Improved diagnostic accuracy: Generative models, such as Generative Adversarial
Networks (GANs), can generate synthetic medical images that closely resemble real
patient images. This helps in training models to improve accuracy in diagnosing
diseases, such as cancer or abnormalities in MRI or CT scans.
2. Faster and more precise image analysis: Generative AI can assist in automating
image analysis tasks, such as segmentation, lesion detection, or organ identification.
Utilizing generative models makes it possible to process images more quickly and with
higher precision, reducing the workload on radiologists and improving efficiency.
Drug discovery and development
Generative AI techniques can potentially reinvent the drug discovery and development
process, leading to faster and more efficient drug development pipelines. Here is how
generative AI is applied:
1. Accelerated identification of potential drug candidates: Generative models can
generate novel molecules with desired properties, helping researchers explore the vast
chemical space efficiently. These generated molecules can be further analyzed for
drug-like properties, potentially leading to the discovery of new drug candidates.
2. Optimization of drug formulations and dosages: Generative AI can assist in
optimizing drug formulations and dosages by simulating the interactions between drugs
and the human body. This allows for personalized medicine and tailoring treatments
based on individual patient characteristics, improving therapeutic outcomes.
Personalized medicine
Generative AI is crucial in advancing personalized medicine, which aims to provide tailored
treatment plans based on individual patient data. Here is how generative AI is utilized:
1. Tailored treatment plans: Generative models can analyze patient data, including
genetic information, medical history, and clinical data, to generate personalized
treatment plans. This can aid in selecting the most effective therapies and predicting
individual patient responses.
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2. Predictive analytics for disease progression and treatment response: By
analyzing large datasets and integrating various patient factors, generative AI can
generate predictive models that estimate disease progression and treatment outcomes.
This helps healthcare professionals make informed decisions regarding treatment
strategies and optimize patient care.
Medical research and data analysis
Generative AI techniques have immense potential in medical research and data analysis.
Here are two key aspects:
1. Mining large datasets for insights and patterns: Generative models can analyze
vast amounts of medical data, including electronic health records, research papers,
and clinical trials, to extract valuable insights and identify patterns that may lead to new
discoveries or medical breakthroughs.
2. Generating synthetic data for privacy-preserving research: To address privacy
concerns, generative AI can generate synthetic medical data that closely resembles
real patient data while preserving privacy. This synthetic data can be utilized for
research purposes without exposing sensitive patient information.
Clinical decision-making processes
Generative AI in healthcare holds significant potential to enhance clinical decision-making
processes and assist healthcare professionals in making accurate and informed diagnoses,
as demonstrated by solutions like Glass.Health. By analyzing vast amounts of patient data,
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including medical records, lab results, previous treatments, and medical imaging such as
MRIs and X-rays, generative AI algorithms can identify patterns and correlations that may
not be immediately apparent to human clinicians.
Generative AI algorithms can assist in detecting potential problem areas, highlighting
concerns and suggesting further diagnostic tests or treatment approaches. By considering a
broader range of patient data and drawing upon extensive medical knowledge, generative AI
systems can act as valuable decision-support tools, empowering healthcare professionals
with additional information and recommendations.
Population health management
Generative AI in healthcare can enhance population health management strategies greatly.
By leveraging generative AI, policymakers can access more detailed demographic
information, enabling them to gain deeper insights into specific populations’ health profiles
and needs. They can analyze large datasets and identify these populations’ patterns, trends,
and disparities. This level of granularity enables the design and implementation of targeted
public health initiatives, like preventive measures and early intervention programs, that
address the unique challenges faced by underserved communities. By understanding the
specific health needs and social determinants of health affecting different populations,
policymakers can allocate resources more efficiently and effectively to improve population
health outcomes.
Risk prediction of pandemic preparedness
Generative AI models have become invaluable resources for scientists studying the societal-
scale effects of catastrophic events, such as pandemics. By leveraging large datasets and
advanced algorithms, generative AI can simulate and model the spread of infectious
diseases, providing insights into potential outbreak scenarios and their implications. These
models can help identify key factors that contribute to the rapid escalation of a virus, allowing
policymakers and healthcare organizations to develop targeted preventive measures and
response strategies.
One significant application of generative AI in pandemic preparedness is the training of
models on vast amounts of protein sequences. By analyzing these sequences, generative AI
algorithms can identify and generate new antibodies or antiviral compounds that can
potentially address infectious diseases. This approach enables researchers to expedite the
development of targeted treatments and therapeutic interventions for emerging pathogens,
bolstering the arsenal against future pandemics.
These applications of generative AI in healthcare demonstrate its potential to improve
diagnostics, drug development, personalized medicine, and medical research, among others.
By leveraging generative AI techniques, healthcare professionals can enhance decision-
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making, optimize treatment strategies, and ultimately improve patient outcomes.
Generative AI in healthcare: Real-world examples
Some examples of solutions built using generative AI in healthcare are:
Chest-gan
A generative AI model that utilizes GANs to produce realistic chest X-rays that can be used
to train other machine learning models used for diagnosing chest pathologies. The project
aims to augment the existing dataset by generating synthetic chest X-ray images and
providing more training examples for classification models. This could enhance the
performance of the models in accurately classifying different chest pathologies. The focus is
on five specific pathology classes: Atelectasis, effusion, pneumothorax, cardiomegaly, and
no finding (Normal).
GENTRL
GENTRL (Generative Tensorial Reinforcement Learning) model is a variational autoencoder
that combines generative models and reinforcement learning to optimize molecules with
desired properties. The main objective of GENTRL is to generate novel molecules with
specific characteristics or properties that can be used in various applications like drug
discovery. Unlike traditional generative models, GENTRL takes into account both the
chemical structure and the desired properties of the molecules. It uses a combination of
reinforcement learning algorithms and deep neural networks to optimize the generation
process. By training on a dataset of molecules with known properties, the model acquires the
ability to generate novel molecules that optimize the desired properties.
Med-PaLM
Med-PaLM and Med-PaLM 2 are large language models developed by Google for answering
medical questions and providing accurate information in the medical domain.
The original Med-PaLM model was introduced in 2022 and was the first AI system to surpass
the pass mark on US Medical License Exam (USMLE) style questions. It utilizes Google’s
powerful LLMs, which have been trained and fine-tuned using expert demonstrations from
the medical field. Med-PaLM can generate comprehensive and reliable answers to consumer
health questions, as evaluated by panels of physicians and users.
Med-PaLM 2, the latest version of the model, achieves an impressive accuracy of 85.4% on
USMLE questions, which is comparable to the performance of “expert” test takers.
Additionally, it became the first AI system to achieve a passing score on the MedMCQA
dataset, which consists of Indian AIIMS and NEET medical examination questions, with a
score of 72.3%.
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Glass.Health
Glass.Health is an advanced platform that utilizes AI-assisted diagnosis and clinical decision-
making to assist healthcare practitioners. Through their generative AI tool, they have created
a system capable of generating diagnoses and clinical plans based on input symptoms. By
leveraging generative AI, this tool can process patient symptoms and compare them with a
vast knowledge base, providing physicians with additional insights and potential treatment
options.
How does generative AI help in drug discovery?
With the remarkable progress of generative AI in healthcare, its impact on drug discovery
cannot be overlooked. In this context, we delve into the application of the pre-trained
GENTRL model, which enables the generation and visualization of valid molecules. Let us
explore its detailed mechanism to gain a comprehensive understanding of GENTRL’s
functioning.
How does GENTRL work?
GENTRL consists of an encoder network that encodes the molecular structure into a latent
space, a decoder network that generates molecules from the latent space, and a
reinforcement learning module that guides the generation process based on the desired
properties. The reinforcement learning module provides rewards or penalties to the model
based on how well the generated molecules match the desired properties.
GENTRL improves its ability to generate molecules with the desired properties by iteratively
generating and evaluating molecules. It can be used in various healthcare applications,
including drug discovery, where the goal is to find molecules with specific drug-like properties
or optimize existing molecules to enhance their efficacy or safety.
Now that we have understood the working mechanism of GENTRL, let us go through the
step-by-step process of molecule generation and visualization using it.
First, we must load the dataset, perform data preprocessing, and initialize and pre-train the
GENTRL model using the dataset. For this, you can run the code from this Github file. Then,
we must initialize and load the pre-trained GENTRL model, train it using the RL approach
with a specific reward function, and save the model. The codes for this can be accessed in
this Github file. We can now use the saved GENTRL model for molecule generation and
visualization.
Import the necessities
Begin by importing the required modules and setting the CUDA device for GPU acceleration.
import gentrl
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import torch
from rdkit.Chem import Draw
from moses.metrics import mol_passes_filters, QED, SA, logP
from moses.metrics.utils import get_n_rings, get_mol
torch.cuda.set_device(0)
torch.cuda.set_device(0)
Model initialization
Next, initialize an RNN-based encoder (enc) and a dilated convolutional decoder (dec).
These components are used to build the GENTRL model.
enc = gentrl.RNNEncoder(latent_size=50)
dec = gentrl.DilConvDecoder(latent_input_size=50)
model = gentrl.GENTRL(enc, dec, 50 * [('c', 20)], [('c', 20)], beta=0.001)
model.cuda();
Loading the trained model
Load the pre-trained GENTRL model that has been previously saved in the
‘saved_gentrl_after_rl/’ directory and move it to the CUDA device for GPU acceleration.
model.load('saved_gentrl_after_rl/')
model.cuda();
Define the utility functions
Now, we have to define two helper functions for calculating the number of rings with more
than six atoms in a molecule and computing a penalized LogP value for a given molecule or
SMILES string. These functions can be part of a broader pipeline for molecule analysis,
property optimization, or generating molecules that satisfy certain criteria.
def get_num_rings_6(mol):
r = mol.GetRingInfo()
return len([x for x in r.AtomRings() if len(x) > 6])
def penalized_logP(mol_or_smiles, masked=True, default=-5):
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mol = get_mol(mol_or_smiles)
if mol is None:
return default
reward = logP(mol) - SA(mol) - get_num_rings_6(mol)
if masked and not mol_passes_filters(mol):
return default
return reward
Molecule generation loop
Next, we need to generate a list of 1000 valid molecules using the ‘model’ object. The loop
should continue until the desired number of valid molecules is reached. This process can be
part of a molecule generation or optimization pipeline, where the objective is to obtain a set
of valid molecules for further analysis, screening, or other purposes.
generated = []
while len(generated) < 1000:
sampled = model.sample(100)
sampled_valid = [s for s in sampled if get_mol(s)]
generated += sampled_valid
Molecule visualization
Finally, to visualize the generated molecules, run the following command. This will generate
a grid image of molecules and their corresponding penalized logP values.
Draw.MolsToGridImage([get_mol(s) for s in sampled_valid],
legends=[str(penalized_logP(s)) for s in sampled_valid])
Endnote
The advent of generative AI has brought forth transformative advancements and
opportunities in the healthcare field. With its ability to generate novel data, improve
diagnostics, optimize treatment strategies, and expedite drug discovery, generative AI is
reshaping the healthcare landscape. From enhancing medical imaging and patient care to
enabling personalized medicine and streamlining drug development, this powerful
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technology holds immense potential for improving healthcare outcomes. As we continue to
harness the capabilities of generative AI and address the associated challenges, we are
poised to witness a new era of innovation and breakthroughs in the realm of healthcare.
Partner with LeewayHertz to build robust generative AI solutions tailored to your business-
specific use case in healthcare and stay at the forefront of technological advancements for
improved healthcare delivery.