A presentation about the algorithms and capabilities of AI in medicine, along some of the work for the presenter. Kristijan is a developer with 15 years of experience, Haskeller, ML practitioner.
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions.
Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you will learn:
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
AI in Healthcare: How to Implement Medical Imaging Using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time, and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care, and interconnected health conditions.
Through this webinar, you will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you will learn:
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
2023 ISSIP Excellence in Service Innovation Award Talk
Impact to Innovation
*DESCRIPTION*
Award Type: Impact to Innovation
https://issip.org/issip-2023-excellence-in-service-innovation-awards/
Innovation: To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
Summary: This study is the first in-depth, longitudinal investigation of how ML-based prediction tools are used for medical diagnosis decision making. AI tools may operate in similar ways technologically, but they are experienced in unique ways depending on the users, context, and work practices. This is a fundamentally new way to think about opacity and has implications for how ML tools are designed and deployed in critical decision making contexts.
Organizations:
University of Virginia @UVA
https://www.virginia.edu
Warwick University @warwickuni
https://warwick.ac.uk
New York University @nyuniversity
https://www.nyu.edu
Primary
Sarah Lebovitz (LI) (DC)
https://www.linkedin.com/in/sarah-lebovitz-23037219/
https://badgr.com/public/assertions/RWm1evX1TrqaO3qDRpq31Q?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fsarah-lebovitz-23037219%2F
Hila Lifshitz-Assaf (LI) (DC)
https://www.linkedin.com/in/hila-lifshitz-assaf-311a1a5/
https://badgr.com/public/assertions/tfuXsNsASbqKMDbORgGU9Q?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fhila-lifshitz-assaf-311a1a5%2F
Natalia Levina (LI) (DC)
https://www.linkedin.com/in/natalia-levina-bb50001/
https://badgr.com/public/assertions/jEz5KAMkT9qg05zSr2_kig?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fnatalia-levina-bb50001%2F
*SERIES*
Weekly Speaker Series
https://issip.org/events-news/events/events-weekly-speaker-series/
*SERIES_IMAGE_URL*
https://issip.org/wp-content/uploads/2023/04/Speaker-Series-via-Zoom-3000x750-1-scaled.jpg
*WHEN*
Wednesday May 31, 2023 @ 7:30 am - 8:00 am (07:30am PT, 10:30am ET, 16:30 CET, 23:30 JST)
*HOST*
Host:
Jim Spohrer (LI)
https://www.linkedin.com/in/spohrer/
*ADDITIONAL_INFORMATION*
Register
https://www.eventbrite.com/e/excellence-in-service-innovation-impact-to-innovation-award-winner-tickets-640120275977
To all who register:
Zoom link will be sent shortly before event starts.
Link to recording and slides will be sent after the event.
Announcements
Follow - ISSIP LinkedIn Company:
https://www.linkedin.com/feed/update/urn:li:activity:7066105884153692160
Discussion - ISSIP LinkedIn Group
https://www.linkedin.com/feed/update/urn:li:activity:7066103867666550784
Tweet - @The_ISSIP #ISSIP Twitter
https://twitter.com/The_ISSIP/status/1660339093455409159
Recording - ISSIP YouTube
https://youtu.be/UG2XvEd8Y5A
Slides - ISSIP Slideshare
https://www.slideshare.net/issip/20230531-impacttoinnovationawardtalk-lebovitzissip-talkmay-31pdf/edit
Apache Spark NLP for Healthcare: Lessons Learned Building Real-World Healthca...Databricks
The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization.
Auckland ux meetup Sept 13 augmented reality in healthcarehealthydigital
Ian Power from Healthy Digital presents an update of some cool AR stuff happening in healthcare, and an intro to an idea to improve patient adherence with long term medicines.
Presented to the Auckland UX meet up, September 2013
Recent advances in VR and AR technology have enabled interactive graphics applications to support healthcare professionals in training, diagnosis, planning, and treatment. This field has progressed enough to warrant a course that can inspire new ideas within the graphics community. Medical images create virtual human anatomy models, allowing for natural interaction and visualization in healthcare scenarios. VR and AR are conceptually different and suited for different types of problems. VR is immersive and suitable for learning anatomy, surgical skills, and analyzing 3D medical data. AR, on the other hand, overlays helpful information onto the physical environment, making it useful for tasks such as communication with patients and training assistants. However, several challenges, such as nonstandard equipment and disorientation, still limit the widespread use of these technologies. This course covers current advances and challenges in this area, including integrating AI techniques.
About the Speaker: Joaquim Jorge holds the UNESCO Chair of Artificial Intelligence & Extended Reality at the University of Lisboa, Portugal. He joined Eurographics in 1986 and ACM/SIGGRAPH in 1989. He is Editor-in-Chief of the Computers and Graphics Journal, Eurographics Fellow, ACM Distinguished Member, and member of IEEE Computer Society Board of Governors. He organized 50+ conferences, including Eurographics 2016 (IPC CO-Chair), IEEE VR 2020/21/22 as co-(papers)chair, and ACM IUI 2012 (IPC co-chair). He served on 210+ program committees and (co)authored over 360 peer-reviewed publications and five books. His research interests include graphics, virtual reality, and advanced HCI techniques applied to health technologies.
Websites:
https://en.wikipedia.org/wiki/Joaquim_Jorge_(computer_scientist)
Google Scholar:
https://scholar.google.com/citations?user=RgiMdpAAAAAJ&hl=en
D.S. Lopes, D. Medeiros, S.F. Paulo, P.B. Borges, V. Nunes, V. Mascarenhas, M. Veiga, J.A. Jorge, Interaction Techniques for Immersive CT Colonography: A Professional Assessment, In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071, Pages 629–637, Springer, Cham, 2018. DOI: 10.1007/978-3-030-00934-2_70
M. Sousa, D. Mendes, S. Paulo, N. Matela, J. Jorge, D.S. Lopes, VRRRRoom: Virtual Reality for radiologists in the reading room, Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI 2017), New York: ACM Press, 2017. DOI: 10.1145/3025453.3025566
D.S. Lopes, P.F. Parreira, S.F. Paulo, V. Nunes, P.A. Rego, M.C. Neves, P.S. Rodrigues, J.A. Jorge, On the utility of 3D hand cursors to explore medical volume datasets with a touchless interface, Journal of Biomedical Informatics, 72, Pages 140–149, 2017. DOI: 10.1016/j.jbi.2017.07.009
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
Alzheimer’s disease (AD) is a chronic neurodegenerative disease which is largely responsible for dementia in around 6% of the population aged 65 and above. The availability of human brain data generated by imaging techniques, such as Magnetic Resonance Imaging, have resulted in a growing interest in data-driven approaches for the diagnosis of neurological disorders and for the identification of new biomarkers. The knowledge discovery process typically involves complex data workflows that combine pre-processing techniques, statistical methods, machine learning algorithms, post-processing and visualisation techniques. This talk presents specific research efforts in this direction, promising results, open issues and challenges.
Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
AI in Health Care: How to Implement Medical Imaging using Machine Learning?Skyl.ai
About the webinar
According to a report “The Digital Universe Driving Data Growth in Healthcare,” published by EMC with research and analysis from IDC, Hospitals are producing 50 petabytes of data per year. Almost 90% of this data is comprised of medical imaging i.e. digital images from scans like MRIs or CTs. More than 97% of this data goes unanalyzed or unused.
The top healthcare institutions across the globe are adopting AI in medical imaging to increase speed and imaging accuracy, monitor data in real-time and eliminate the need for humans to do time-consuming and complex tasks. This has been enabling doctors to optimize treatment approaches, speed of care and interconnected health conditions.
Through this webinar, we will understand how AI can be used to automate routine processes and procedures and help radiologists to identify patterns, and help in treating patients with critical conditions quickly.
What you'll learn
- How healthcare institutions are leveraging AI to augment decision making, prevent medical errors, and reduce costs in medical imaging
- Discuss the approach to automate machine learning workflow, creating and deploying models in hours, not weeks or months
- Demo: How to detect pneumonia from chest x-rays using AI within a few minutes using skyl.ai
To explore more, visit: https://skyl.ai/form?p=start-trial
2023 ISSIP Excellence in Service Innovation Award Talk
Impact to Innovation
*DESCRIPTION*
Award Type: Impact to Innovation
https://issip.org/issip-2023-excellence-in-service-innovation-awards/
Innovation: To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis
Summary: This study is the first in-depth, longitudinal investigation of how ML-based prediction tools are used for medical diagnosis decision making. AI tools may operate in similar ways technologically, but they are experienced in unique ways depending on the users, context, and work practices. This is a fundamentally new way to think about opacity and has implications for how ML tools are designed and deployed in critical decision making contexts.
Organizations:
University of Virginia @UVA
https://www.virginia.edu
Warwick University @warwickuni
https://warwick.ac.uk
New York University @nyuniversity
https://www.nyu.edu
Primary
Sarah Lebovitz (LI) (DC)
https://www.linkedin.com/in/sarah-lebovitz-23037219/
https://badgr.com/public/assertions/RWm1evX1TrqaO3qDRpq31Q?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fsarah-lebovitz-23037219%2F
Hila Lifshitz-Assaf (LI) (DC)
https://www.linkedin.com/in/hila-lifshitz-assaf-311a1a5/
https://badgr.com/public/assertions/tfuXsNsASbqKMDbORgGU9Q?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fhila-lifshitz-assaf-311a1a5%2F
Natalia Levina (LI) (DC)
https://www.linkedin.com/in/natalia-levina-bb50001/
https://badgr.com/public/assertions/jEz5KAMkT9qg05zSr2_kig?identity__url=https:%2F%2Fwww.linkedin.com%2Fin%2Fnatalia-levina-bb50001%2F
*SERIES*
Weekly Speaker Series
https://issip.org/events-news/events/events-weekly-speaker-series/
*SERIES_IMAGE_URL*
https://issip.org/wp-content/uploads/2023/04/Speaker-Series-via-Zoom-3000x750-1-scaled.jpg
*WHEN*
Wednesday May 31, 2023 @ 7:30 am - 8:00 am (07:30am PT, 10:30am ET, 16:30 CET, 23:30 JST)
*HOST*
Host:
Jim Spohrer (LI)
https://www.linkedin.com/in/spohrer/
*ADDITIONAL_INFORMATION*
Register
https://www.eventbrite.com/e/excellence-in-service-innovation-impact-to-innovation-award-winner-tickets-640120275977
To all who register:
Zoom link will be sent shortly before event starts.
Link to recording and slides will be sent after the event.
Announcements
Follow - ISSIP LinkedIn Company:
https://www.linkedin.com/feed/update/urn:li:activity:7066105884153692160
Discussion - ISSIP LinkedIn Group
https://www.linkedin.com/feed/update/urn:li:activity:7066103867666550784
Tweet - @The_ISSIP #ISSIP Twitter
https://twitter.com/The_ISSIP/status/1660339093455409159
Recording - ISSIP YouTube
https://youtu.be/UG2XvEd8Y5A
Slides - ISSIP Slideshare
https://www.slideshare.net/issip/20230531-impacttoinnovationawardtalk-lebovitzissip-talkmay-31pdf/edit
Apache Spark NLP for Healthcare: Lessons Learned Building Real-World Healthca...Databricks
The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization.
Auckland ux meetup Sept 13 augmented reality in healthcarehealthydigital
Ian Power from Healthy Digital presents an update of some cool AR stuff happening in healthcare, and an intro to an idea to improve patient adherence with long term medicines.
Presented to the Auckland UX meet up, September 2013
Recent advances in VR and AR technology have enabled interactive graphics applications to support healthcare professionals in training, diagnosis, planning, and treatment. This field has progressed enough to warrant a course that can inspire new ideas within the graphics community. Medical images create virtual human anatomy models, allowing for natural interaction and visualization in healthcare scenarios. VR and AR are conceptually different and suited for different types of problems. VR is immersive and suitable for learning anatomy, surgical skills, and analyzing 3D medical data. AR, on the other hand, overlays helpful information onto the physical environment, making it useful for tasks such as communication with patients and training assistants. However, several challenges, such as nonstandard equipment and disorientation, still limit the widespread use of these technologies. This course covers current advances and challenges in this area, including integrating AI techniques.
About the Speaker: Joaquim Jorge holds the UNESCO Chair of Artificial Intelligence & Extended Reality at the University of Lisboa, Portugal. He joined Eurographics in 1986 and ACM/SIGGRAPH in 1989. He is Editor-in-Chief of the Computers and Graphics Journal, Eurographics Fellow, ACM Distinguished Member, and member of IEEE Computer Society Board of Governors. He organized 50+ conferences, including Eurographics 2016 (IPC CO-Chair), IEEE VR 2020/21/22 as co-(papers)chair, and ACM IUI 2012 (IPC co-chair). He served on 210+ program committees and (co)authored over 360 peer-reviewed publications and five books. His research interests include graphics, virtual reality, and advanced HCI techniques applied to health technologies.
Websites:
https://en.wikipedia.org/wiki/Joaquim_Jorge_(computer_scientist)
Google Scholar:
https://scholar.google.com/citations?user=RgiMdpAAAAAJ&hl=en
D.S. Lopes, D. Medeiros, S.F. Paulo, P.B. Borges, V. Nunes, V. Mascarenhas, M. Veiga, J.A. Jorge, Interaction Techniques for Immersive CT Colonography: A Professional Assessment, In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071, Pages 629–637, Springer, Cham, 2018. DOI: 10.1007/978-3-030-00934-2_70
M. Sousa, D. Mendes, S. Paulo, N. Matela, J. Jorge, D.S. Lopes, VRRRRoom: Virtual Reality for radiologists in the reading room, Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI 2017), New York: ACM Press, 2017. DOI: 10.1145/3025453.3025566
D.S. Lopes, P.F. Parreira, S.F. Paulo, V. Nunes, P.A. Rego, M.C. Neves, P.S. Rodrigues, J.A. Jorge, On the utility of 3D hand cursors to explore medical volume datasets with a touchless interface, Journal of Biomedical Informatics, 72, Pages 140–149, 2017. DOI: 10.1016/j.jbi.2017.07.009
How can we make a Radiologist more efficient?
Increased Imaging for Chronic Diseases and Emergencies raise the demand for radiologists globally & AI could definitely assist them in increasing their efficiency & meet the requirements.
Alzheimer’s disease (AD) is a chronic neurodegenerative disease which is largely responsible for dementia in around 6% of the population aged 65 and above. The availability of human brain data generated by imaging techniques, such as Magnetic Resonance Imaging, have resulted in a growing interest in data-driven approaches for the diagnosis of neurological disorders and for the identification of new biomarkers. The knowledge discovery process typically involves complex data workflows that combine pre-processing techniques, statistical methods, machine learning algorithms, post-processing and visualisation techniques. This talk presents specific research efforts in this direction, promising results, open issues and challenges.
Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
[DSC MENA 24] Medhat_Kandil - Empowering Egypt's AI & Biotechnology Scenes.pdfDataScienceConferenc1
In this talk, I'll journey from my time as a Research Assistant at the Bernoulli Institute, delving into the classification of neurodegenerative diseases, to my encounters with groundbreaking biotechnology and AI companies like Proteinea, AlProtein, Rology, and Natrify in Egypt. These innovative ventures are reshaping industries from their Egyptian hub. Join me as I illuminate the transformative power of this thriving ecosystem, showcasing Egypt's remarkable strides in biotech and AI on the global stage.
Building big scale data product doesn't rely only on sophisticated modeling. It also requires an agile methodology, iterative research & development process, versatile big data stack, and a value-oriented mindset. I'll discuss how we -at Dsquares- build big-scale AI product that leverages clients' data from different industries to deliver business-critical value to the end customer. I'll cover the process of product discovery, R&D tasks for unsolved problems, and mapping business requirements into big data technical requirements.
[DSC MENA 24] Asmaa_Eltaher_-_Innovation_Beyond_Brainstorming.pptxDataScienceConferenc1
Innovation thrives at the intersection of data and creativity. While brainstorming has traditionally fueled the generation of new ideas, leveraging data alongside creative techniques empowers organizations to develop more effective and impactful innovations
[DSC MENA 24] Basma_Rady_-_Building_a_Data_Driven_Culture_in_Your_Organizatio...DataScienceConferenc1
In today's fast-paced and competitive business environment, harnessing the power of data is essential for staying ahead. Building a data-driven culture within an organization is not just a strategic advantage, but a necessity for those who wish to thrive and innovate. In this insightful talk, our esteemed speaker, a Chief Data Scientist with a decade of experience in the financial services sector, will unravel the complexities of embedding data into the DNA of your organization. The speaker will explore the key tenets of establishing a data-centric mindset, the importance of executive support, and the need for enhancing data literacy across the company. Practical solutions and real-world examples will be provided, demonstrating how to overcome obstacles and successfully integrate a data-driven approach. Attendees will learn strategies for empowering every team member to use data effectively and how to leverage technology to facilitate this cultural shift. The session promises to be a guide for those looking to champion data within their organizations, offering actionable insights for transformation.
[DSC MENA 24] Ahmed_Muselhy_-_Unveiling-the-Secrets-of-AI-in-Hiring.pdfDataScienceConferenc1
The use of Artificial Intelligence (AI) is rapidly transforming the recruitment landscape. This talk explores the various ways AI is being used in hiring, from candidate sourcing and screening to skills assessments and interview preparation. We'll discuss the benefits of AI, such as increased efficiency and reduced bias, but also address potential drawbacks like ethical considerations and the human touch.
[DSC MENA 24] Ziad_Diab_-_Data-Driven_Disruption_-_The_Role_of_Data_Strategy_...DataScienceConferenc1
In today's business landscape, data strategy plays a pivotal role in driving innovation within business models. This talk explores how organizations can leverage data effectively to transform their operations, products, and services.
[DSC MENA 24] Mohammad_Essam_- Leveraging Scene Graphs for Generative AI and ...DataScienceConferenc1
Delve into the unexplored potential of scene graphs in the realms of Generative AI and innovative data product development. This session unveils the intricate role of scene graphs in generating realistic content and driving advancements in computer vision, and automated content creation. Join us for a journey into the intersection of scene graphs and cutting-edge AI, gaining insights into their pivotal role in reshaping the landscape of data-centric innovation. This talk is your gateway to understanding how structured visual representations are shaping the future of AI and revolutionizing the creation of data-driven solutions.
This presentation will delve into the transformative role of Artificial Intelligence in reshaping social media landscapes. We'll explore cutting-edge AI technologies that are integrating with social media platforms, altering how we interact, consume content, and perceive digital communities. The talk will also cast a visionary eye towards future trends, discussing potential impacts on user experience, content creation, digital marketing, and privacy concerns. Join us to uncover how AI is not just a tool but a game-changer in the evolving narrative of social media.
Supercharge your software development with Azure OpenAI Service! Azure cloud platform provides access to cutting-edge AI models for diverse tasks. Explore different models for generating content, translating languages, and even generating code. Leverage data grounding to fine-tune models for your specific needs. Discover how Azure OpenAI Service accelerates innovation and injects intelligence into your software creations.
[DSC MENA 24] Nezar_El_Kady_-_From_Turing_to_Transformers__Navigating_the_AI_...DataScienceConferenc1
In this insightful talk, we'll embark on a journey from the origins of programming in 1883 and the conceptualization of AI in the 1950s, to the current explosion of AI applications reshaping our world. We'll unravel why AI has surged to prominence in the last decade, driven by unprecedented data generation and significant hardware advancements. With examples ranging from individual email filtering to complex supply chain optimizations, we'll explore AI's pervasive impact across various sectors including finance, manufacturing, healthcare, and media. The talk will address the challenges of AI implementation, such as the high cost of AI teams and the quest for universally applicable models, while highlighting the promising horizon of no-code AI platforms democratizing access. Furthermore, we'll delve into the ethical dimensions of AI, from biases to privacy concerns, and the pressing question of AI's potential to replace human roles. Lastly, we'll discuss the transformative potential of language models and generative AI, underscoring the importance of understanding and integrating AI into our lives and businesses for a future that's both scalable and sustainable.
[DSC MENA 24] Omar_Ossama - My Journey from the Field of Oil & Gas, to the Ex...DataScienceConferenc1
Transitioning to a career in data science requires careful planning and smart choices. In this session, I'll help you understand how to switch to data science. Using my own experiences and what I've learned from the industry, we'll break down the important steps for a successful transition. We'll cover everything from figuring out which skills you can carry over to learning the technical stuff and connecting with other professionals. By the end, you'll have the knowledge and tools you need to start your journey into data science, whether you're a seasoned professional looking for something new or just starting out in the field.
[DSC MENA 24] Ramy_Agieb_-_Advancements_in_Artificial_Intelligence_for_Cybers...DataScienceConferenc1
With the continuous growth of the digital environment, the risks in the online realm also increase. This calls for strong security measures to safeguard valuable information and essential systems. Artificial Intelligence (AI) has become a powerful weapon in the fight against cyber threats. This talk presents a thorough examination of the most recent algorithms and applications of artificial intelligence in the field of cybersecurity.
[DSC MENA 24] Sohaila_Diab_-_Lets_Talk_Gen_AI_Presentation.pptxDataScienceConferenc1
What is Generative AI and how does it work? Could it eventually replace us? Let's delve deep into the heart of this groundbreaking technology and uncover the truths and myths surrounding Generative AI and how to make the most of it.
Background: The digital twin paradigm holds great promise for healthcare, most importantly efficiently integrating many disparate healthcare data sources and servicing complex tasks like personalizing care, predicting health outcomes, and planning patient care, even though many technical and scientific challenges remain to be overcome. Objective: As part of the QUALITOP project, we conducted a comprehensive analysis of diverse healthcare data, encompassing both prospective and retrospective datasets, along with an in-depth examination of the advanced analytical needs of medical institutions across five European Union countries. Through these endeavors, we have systematically developed and refined a formal Personal Medical Digital Twin (PMDT) model subjected to iterative validation by medical institutions to ensure its applicability, efficacy, and utility. Findings: The PMDT is based on an interconnected set of expressive knowledge structures that are calibrated to capture an individual patient’s psychosomatic, cognitive, biometrical and genetic information in one personal digital footprint in a manner that allows medical professionals to run various models to predict an individual’s health issues over time and intervene early with personalized preventive care.Conclusion: At the forefront of digital transformation, the PMDT emerges as a pivotal entity, positioned at the convergence of Big Data and Artificial Intelligence. This paper introduces a PMDT environment that lays the foundation for the application of comprehensive big data analytics, continuous monitoring, cognitive simulations, and AI techniques. By integrating stakeholders across the care continuum, including patients, this system enables the derivation of insights and facilitates informed decision-making for personalized preventive care.
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
The Importance of Community Nursing Care.pdfAD Healthcare
NDIS and Community 24/7 Nursing Care is a specific type of support that may be provided under the NDIS for individuals with complex medical needs who require ongoing nursing care in a community setting, such as their home or a supported accommodation facility.
Empowering ACOs: Leveraging Quality Management Tools for MIPS and BeyondHealth Catalyst
Join us as we delve into the crucial realm of quality reporting for MSSP (Medicare Shared Savings Program) Accountable Care Organizations (ACOs).
In this session, we will explore how a robust quality management solution can empower your organization to meet regulatory requirements and improve processes for MIPS reporting and internal quality programs. Learn how our MeasureAble application enables compliance and fosters continuous improvement.
LGBTQ+ Adults: Unique Opportunities and Inclusive Approaches to CareVITASAuthor
This webinar helps clinicians understand the unique healthcare needs of the LGBTQ+ community, primarily in relation to end-of-life care. Topics include social and cultural background and challenges, healthcare disparities, advanced care planning, and strategies for reaching the community and improving quality of care.
Deep Leg Vein Thrombosis (DVT): Meaning, Causes, Symptoms, Treatment, and Mor...The Lifesciences Magazine
Deep Leg Vein Thrombosis occurs when a blood clot forms in one or more of the deep veins in the legs. These clots can impede blood flow, leading to severe complications.
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2. Who am I?
● 15+ years of experience with programming (Java, Haskell, …) and the last 5+ years experience with AI/ML
● Products using NLP (entity recognition, sentiment analysis):
○ https://www.emprovio.com/
○ https://alenn.ai/
○ https://contetino.com/
● Mobile application for recognizing the sign language alphabet (for “deaf” people)
● Application for recognizing breast cancer
● Application for detecting epilepsy (in progress, cooperation with doctors in Croatia)?
● Application for chest radiograph diagnosis (CheXpert, in progress, cooperation with a doctor)
● Non-medical:
○ Web application for detecting parking spaces, working on a small device (think Raspberry Pi)
○ Application for recognizing roads and road signs, "Mini Tesla" project on a small car
○ Application for automatic fault detection in automation
● https://exact-byte.com/en-blog/
● Not a ML researcher, but ML practitioner - “take” things that really smart people did and try to use them to build
something practical
3. Why this presentation?
● A series of free applications/articles to promote myself and my company
● An opportunity to open up doors with a hospital or somebody interested to cooperate
● Taking a relatively known/researched problem(s) and create a ML solution that can
actually bring value and show you the result
● Create something that can actually help people and learn something along the way?
4. What I want to emphasize before diving in
● I’m not a doctor, even though I work and have worked with them
● My focus is on ML and not on the medical aspects, if you ask me a medical question, I
probably won’t know it
● I don’t believe doctors can be replaced, ever
● I don’t believe cars can drive on their own for another 10 years and wouldn’t let one
drive me
● I believe we can create tools that can help people, even doctors to do their job easier
● Any complex questions/ideas regarding ML used here after the talk, please, this
should be a general and light(er) presentation
5. Where do I live?
● Pula, city in Croatia, on the coast, near Italy - https://en.wikipedia.org/wiki/Pula
13. Solution
Program that help in
detection/diagnosis
The user presents the same images
a radiologist would look at and
based on that images, presents the
result back with his own opinion
where the suspect area is and what
should be considered
15. Result
Program that interprets images from
mammography and shows critical
regions for each of the mammography
images (L-CC, R-CC, L-MLO, R-MLO)
16. OB Pula
A doctor checked the results of the
program after training (fine-tuning) the
program on 1000 images (OB Pula
has around 6000 images yearly)
After the analysis of the results I got after analyzing around
30 patients on the program you provided,
I consider that the program can help in the detection of
the shadows that the radiologists needs to analyze, but
not in (more) certain differentiation of benign and malignant
shadows.
The current program thus cannot be of a significant help to
the radiologist.
17. Implementation of the program
● Ideally, we would like to have a team of doctors who would annotate our data set
● Realistically, you first need to gain their trust and show them something that works
● We want to be as time efficient as possible, not waste the precious time doctors have and
show them something valuable
● So, a lot of data (images), deep neural network to exclude feature extraction as much as
possible, and a result which can be visually interpretable
● Based on the work of “An interpretable classifier for high-resolution breast cancer screening
images utilizing weakly supervised localization” - https://arxiv.org/abs/2002.07613
● We requested images from patients, BI-RADS grading
● Wrapped the application in a web application, made some minor modifications/tweaks
18. What is the state of the program?
● Doctors (in OB Pula) weren’t that enthusiastic about the program, the general comments were
that it was a good idea, most/all of them were aware of the program, and thought that it
might be useful, but nobody actually used it or tested it
● They requested that the program I made be integrated into an existing program they used
since it was hard to download 4 images and the upload them into the web interface to see
the results
● The software that they used is a medical software and requires all of their components to be
certified - the certification is about 7000 EUR, and the program first needs to be tested
● Not one doctor actually tested the program until I personally paid for their time
● I contacted everybody I could think of in Croatia, including KBC Rijeka, KBC Zagreb, different
associations for cancer support (Gea), newspapers, private polyclinics, …
27. CheXpert
● Cooperation with a doctor, dr. Marko Bukna
● Decent results, comparable to SOTA (State Of The Art)
● Model has achieved radiologist-level performance (even on baseline)
● Very big dataset (400+ GB)
● Takes long to train/test/modify
● Ensembles take even longer
● Ended up using GPU/TPU (Tensor Processing Unit) for training the model
○ 320, BS=256, NVIDIA V100 -> 3551.679718017578 seconds ~ 1 hour / epoch
○ 320, BS=512, 4 X NVIDIA T4 -> 4221.291954755783 seconds ~ 70 min / epoch
○ 320, BS=128, NVIDIA GeForce RTX 2080 Super with Max-Q Design -> 3449.3205976486206 seconds ~ 57.5 min / epoch
○
○ 224, BS=256, NVIDIA GeForce RTX 2080 Super with Max-Q Design -> 1529.4829297065735 seconds ~ 25 min / epoch
○ 224, BS=256, NVIDIA GeForce RTX 3080 -> 1100.0 seconds ~ 16 min / epoch
37. Existing work, research
● https://arxiv.org/pdf/1901.07031.pdf
● https://arxiv.org/pdf/2012.03173v2.pdf
● Deep learning for chest radiograph diagnosis: A retrospective
comparison of the CheXNeXt algorithm to practicing radiologists.
● Stanford Baseline AUC = 0.9065
● True positive rate (TPR), recall, sensitivity, probability of detection =
TP/P
● False positive rate (FPR), probability of false alarm = FP/N
● AUC = Area Under Curve (ROC)
39. Existing work, research
● Research at Stanford
● 9 radiologists
● The average time for radiologists to complete labeling of 420 chest
radiographs - 240 minutes
● The deep learning algorithm labeled the same 420 chest radiographs
in 1.5 minutes and produced heat maps highlighting areas of the
image
● What do you think the result was?
41. Single model results
● Much worse than expected
● Tried quite a number of them:
○ DenseNet (121, 161, 201)
○ Resnet (34, 50)
○ AttentionNet (CNN + “attention”)
○ EfficientNet
○ …
● Multiple resolutions (320X320, 512X512, 1024X1024), 400GB+ DATA
● Multiple transformations (to see if it will affect generality)
● Different optimizers
● Different loss functions (BCE, BCEWithLogits, …)
● Custom dataset split
45. What are the real issues?
● Unless you have connections or people that you know in the “industry”, nobody cares
● Doctors have too much work but are unable to accept help when it could save their time or make their lives easier
(plenty of areas to improve on here)
● Even if they do find the program useful, and the use of it interesting, they are rarely willing to invest time into it
● Chicken - egg problem
● Finding space to innovate with doctors willing to innovate and create a tool that has the potential to save people’s
lives
● Maybe find a group of young people to work on their PhDs?
● Associations for survivors of breast cancer (Gea), and others, which are covered on the TV and which literally
promote “Breast cancer is curable if detected early” don’t have the time to investigate an actual program that is
used to detect breast cancer (early), too busy organising marathons and other “awareness” activities (EU funds,
mind you) - I guess we are all about “awareness” nowadays (I put a flag of Ukraine on my profile, therefore I’m
helping)
● If people don’t care about this, and this literally saves lives, even though it’s mostly a re-used model from research,
what can I expect people will care about? Detecting EEG from epilepsy? Detecting cancer from histopathological
samples? Detecting brain cancer? Improving chances for surviving cancer? Researching the protein “zoo” for
finding some magical cure? Who needs to care about this? Who does care? What is the point of all this if you don’t
have support? Am I insane because I think this is important?
46. Conclusion
● Utter frustration
● Do we have research institutions that actually research? Where does this fit in? Why not?
● Like in most other industries, “best practice” is usually 10 years late, promoted by some health industry behemoth
or somebody who has the connections with one, no actual innovation
● Just looking into the potential to innovate in the health industry is mind-blowing and can be seen on every corner,
but unless somebody takes the first step, that innovation is impossible
● (Most of the) doctors that I talked with are looking for solutions that they don’t have to test, work on, invest time in
and are free - in other words, they want to keep doing what they do, until somebody tells them otherwise
● To implement (all) these innovations, a smaller private health company should take the first step, innovate and
promote, others will follow, and money seems to be the only thing that will motivate this transition
● Let me extend my hand here - if you are that company or know people willing to use tools like these in the health
industry, please contact me, I will do the work for free if it’s useful
● If you are a doctor/medical staff and want to join me in this project in any way - do contact me, I’m considering
actually partnering to implement ideas like these ones - radiology, cytology/pathology