Support for the keynote "Data, Ethics and Health Care,”, Keynote, Creating Value in Health Care through Innovation Management, May 16,2019, Deusto, San Sebastien
Medical research is published with tremendous speed, making it nearly impossible for a doctor to keep up. Artificial Intelligence could be the answer. The growing amounts of available data enables the use of artificial intelligence in health care, as well as the increasingly sophisticated machine learning algorithms. Yet relatively little of these methods are used in health care.
The Incredible Ways Artificial Intelligence Is Now Used In Mental HealthBernard Marr
The world is facing a mental health crisis. With a shortage of mental health professionals, individuals not seeking treatment due to lack of access or high costs, and a significant rise in mental health conditions, artificial intelligence (AI) tools are being assessed and used to create solutions to help support people’s mental health.
Medical research is published with tremendous speed, making it nearly impossible for a doctor to keep up. Artificial Intelligence could be the answer. The growing amounts of available data enables the use of artificial intelligence in health care, as well as the increasingly sophisticated machine learning algorithms. Yet relatively little of these methods are used in health care.
The Incredible Ways Artificial Intelligence Is Now Used In Mental HealthBernard Marr
The world is facing a mental health crisis. With a shortage of mental health professionals, individuals not seeking treatment due to lack of access or high costs, and a significant rise in mental health conditions, artificial intelligence (AI) tools are being assessed and used to create solutions to help support people’s mental health.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
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
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 Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
A Cognitive-Based Semantic Approach to Deep Content Analysis in Search EnginesMei Chen, PhD
We present a cognitive-based semantic approach that uses rule-based Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, and rank digital text in search engines. The goal is to improve the relevance, accuracy, and efficiency of information search. The world model represents things existing in the real world (e.g., subject-related ontologies or classifications essential for understanding the topics to be analyzed) whereas cognitive frames specify possible users’ interactions with the world, including things that people should know or do (e.g., tasks, methods, procedures, cognitive processes) in such interactions. Using a rule-based semantic approach in conjunction with a subject-related world model and task-relevant cognitive frames to understand and evaluate text is innovative approach in search engine technology. It addresses three limitations of the existing approaches: the inadequate measure of the meaningful content in web pages; a poor understanding of users’ intention and tasks in their search and, the irrelevance and inaccuracy of search results. This method has led to the successful implementation of a full-scale semantic search engine in medicine (available at Seenso.com). The method is applicable and adaptable to other disciplines and other types of computer applications.
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Healthcare delivery is becoming an increasingly complex operation. Nurses, physicians and other allied healthcare professionals are increasingly measured on their quality of work, even with increasing patient volume and patient complexity. Technology, from sensors to analytics to software based decision support and automation, have the potential to both leverage our healthcare provider workforce to mange increasing demands and to improve quality. This presentation will focus on the key areas of opportunity for technology to improve the capabilities of healthcare providers in delivering quality care.
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.
5 Powerful Real World Examples Of How AI Is Being Used In HealthcareBernard Marr
Healthcare can be transformed with the innovation and insights of artificial intelligence and machine learning. From robot-assisted surgery to virtual nursing assistants, diagnosing conditions, facilitating workflow and analyzing images, AI and machines can help improve outcomes for patients and lower costs for providers.
Artificial Intelligence in Health Care 247 Labs Inc
This presentation was shown at the Artificial Intelligence in Health Care event in Toronto Nov 16 2017. The discussion was to introduce various applications of artificial intelligence and machine learning in the health care field.
10 Common Applications of Artificial Intelligence in HealthcareTechtic Solutions
List of 10 Common Applications of Artificial Intelligence that explain how artificial intelligence is used in healthcare and why it is necessary? To read briefly all common applications of artificial intelligence in healthcare then visit at https://www.techtic.com/blog/applications-of-ai-in-healthcare/
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
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 Medical IndustryDataMites
Medical artificial intelligence (AI) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. AI has the capability of detecting meaningful relationships in a data set and has been widely used in many clinical situations to diagnose, treat, and predict the results.
visit : https://datamites.com/artificial-intelligence-course-training-pune/
A Cognitive-Based Semantic Approach to Deep Content Analysis in Search EnginesMei Chen, PhD
We present a cognitive-based semantic approach that uses rule-based Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, and rank digital text in search engines. The goal is to improve the relevance, accuracy, and efficiency of information search. The world model represents things existing in the real world (e.g., subject-related ontologies or classifications essential for understanding the topics to be analyzed) whereas cognitive frames specify possible users’ interactions with the world, including things that people should know or do (e.g., tasks, methods, procedures, cognitive processes) in such interactions. Using a rule-based semantic approach in conjunction with a subject-related world model and task-relevant cognitive frames to understand and evaluate text is innovative approach in search engine technology. It addresses three limitations of the existing approaches: the inadequate measure of the meaningful content in web pages; a poor understanding of users’ intention and tasks in their search and, the irrelevance and inaccuracy of search results. This method has led to the successful implementation of a full-scale semantic search engine in medicine (available at Seenso.com). The method is applicable and adaptable to other disciplines and other types of computer applications.
Patients are about to see a new doctor: artificial intelligence by EntefyEntefy
The health care industry has already seen advanced artificial intelligent systems make an impact in areas like medical diagnosis and patient care. But the long-term big-picture importance of AI in medicine may be something else entirely: a potential fix for the intractable problem of too few doctors and nurses worldwide. And as part of that, a solution to health care’s public enemy number one—paperwork.
Entefy curated a presentation based on our article about the impact of artificial intelligence in medical care. These slides provide a snapshot of how AI is at use in medical care today, the advances and limits of current AI systems, and AI’s potential in patient care. The presentation contains useful data and analysis for anyone interested in the intersection of AI and medical care.
For additional analysis and links to our background sources, read “Patients are about to see a new doctor: artificial intelligence" on our blog at https://blog.entefy.com/view/298/Patients-are-about-to-see-a-new-doctor-artificial-intelligence.
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
The primary goal of this workshop is to help health professionals gain a critical understanding of the various types of AI technologies available so they can make wise decisions and invest AI for healthcare improvement.
Healthcare delivery is becoming an increasingly complex operation. Nurses, physicians and other allied healthcare professionals are increasingly measured on their quality of work, even with increasing patient volume and patient complexity. Technology, from sensors to analytics to software based decision support and automation, have the potential to both leverage our healthcare provider workforce to mange increasing demands and to improve quality. This presentation will focus on the key areas of opportunity for technology to improve the capabilities of healthcare providers in delivering quality care.
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.
Precision medicine and AI: problems aheadNeil Raden
The promise of personalized medicine has sparked a proliferation of AI hype. But the obstacles AI faces in the healthcare industry are daunting. Look no further than data silos - and the factors that spawned them.
The Why And How Of Machine Learning And AI: An Implementation Guide For Healt...Health Catalyst
Join Kenneth Kleinberg, Health IT Strategist, and Eric Just, Senior Vice President, Health Catalyst, as they discuss the What, Why, and How of Machine Learning and AI for healthcare leaders.
Attendees will learn:
Practical steps, timeframes and skills as well as real-time data and moving targets associated with the Implementation of ML and AI
How to deal with challenges inherent in ML and AI implementation
What the future holds for ML and AI
Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
Artificial intelligence in healthcare quality and its impact by Dr.Mahboob al...Healthcare consultant
Artificial intelligence (AI) has enormous potential to improve the safety of healthcare, from increasing diagnostic accuracy, to optimising treatment planning, to forecasting outcomes of care.However, integrating AI technologies into the delivery of healthcare is likely to introduce a range of new risks and amplify ...
Artificial intelligence (AI) has numerous applications for the healthcare industry. Machine learning, natural language processing, and robotics can predict an individual's risk of contracting HIV, assess a patient’s risk of inpatient violence, and assist in surgeries.
Artificial Intelligence (AI) is shaping and reshaping every industry under the sun. The Healthcare industry is not any exception.
In this presentation, I have discussed the basics of AI as well as how it is being used in various branches of the healthcare industry. I presented this topic in my departmental seminar in October 2021 and received appreciation as well as positive feedback in this regard.
Support for the presentation • “Does AI Improve Managerial Decision-Making?”at the International Conference Airport Operational Excellence, Jan. 28-30 2019
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
3. Health analytics harnesses data
science to improve decision-
making.
• EHR improves patient safety
and outcomes
• Machine learning greatly
reduces misdiagnosis
• Robotics improves the
precision of micro-surgery
• Algorithms can significantly
reduce fraud
NetObjex
4. AI
The Road to Artificial Intelligence
Machine Learning Artificial Intelligence
Nature Knowledge Intelligence
Vision Self-learning
Algorithms
Mimic human
Behavior
Use Scenario Learns from data Solve Complex
Problem
Aim Sufficient Solution Optimal Solution
Metrics Accuracy Success
What do we mean by
Artificial
Intelligence?
5. Being Human
What does it mean to be human?
• Autonomy: our capacity to make informed decisions
• Agency: the capacity to act independently
• Empathy - the capability to understand and to relate
• Ethics – shared values to differentiate right from wrong
• Intelligence - our ability to acquire and apply knowledge
Well-being
How will health
analytics condition how
the medical profession
defines well-being?
realboxsite.tk
7. • AI will move the goal posts of the medical profession
• Descriptive and prescriptive analytics are growing
exponentially
• We now leverage AI diagnoses without having to bear
the costs and time constraints
• The time to study the model, the code, nor the training
data
• New definitions of “well-being”, “confidentiality”,
“truthfulness” and “trust”
7
Game Changer
To what extent does the
medical profession need
to understand how AI
changes medical
practice?
8. Implicit Bias
• Algorithms learn by processing past
experience
• The importance of profiling and
classification
• These profiles reflect several human
biases
• Bias in the data, in AI, in teaching AI
human rules, in evaluating cases
Who will be ultimately
held responsible for
the implicit bias of
artificial intelligence?
Medium
9. • AI doesn’t fuel innovation
• AI learns from variables that can be
empirically measured
• AI can’t explore all the possible
features
• AI at best mimics rational intellilgence
Innovation
Which types of
intelligence will be vital
to future innovation in
healthcare?
CMO.com
10. • Value in improving medical imagery, targeting
treatment plans, and accelerating the development
of new pharmaceuticals.
• Can we bank on methodologies we don’t
understand?
• Does reinforced learning make us prisoners of the
past?
• Do we understand that the inherent logic of these
platforms can be gamed?
In Sum
“Artificial
Intelligence
alone poorly
illuminates the
future of health
analytics.”
11. Boer, R. ( 2014), Foucault’s Care
…, Deep Learning in Healthcare
Evans, R.S. (2016), Electronic Health Records: Then, Now, and in the Future
Goldhill, D. (2018), Why are we living longer than ever?.
Kontzer, T., (2016), Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85%
Lee, J. et al. (2018), Holistic Quantified Self Framework for Augmented Human
Martin, (2017), Types of Intelligence and How to Find The One You Are Best In
McCaffrey, T. and Spector, L. (2012), Behind every innovative solution lies an obscure
feature
Mittelstadt, B. and Fioridi, L, (2016), The Ethics of Big Data, Current and foreseeable
Issues in the biomedical contexts
Saposnik, G. et al. (2016), Cognitive biases associated with medical decisions: a systematic
review
Sennaar, K., (2019), How America’s Top 4 Insurance Companies are Using Machine
Learning
Bibliograhy
Further Reading
Editor's Notes
Judgement : How much is it worth to respond quickly? How costly is it to not respond if it turns out that there was an intruder in the home?