Yale Certificate in Medical Software and Medical AI: Guest Experts

This video is part of a series of guest expert interviews that we recorded for our new Yale Certificate Program on Medical Software and Medical AI – https://online.yale.edu/medical-software-ai-program. Our guest is Donna-Bea Tillman, a principal consultant at Biologics Consulting Group. She has 30 years of medical device regulatory experience. Prior to joining Biologics Consulting she held numerous positions within FDA’s Center for Devices and Radiological Health, culminating in her 2004 appointment to the position of Director of the Office of Device Evaluation, where she oversaw the medical device premarket review program for non-IVD devices. During her 17-years tenure at FDA, she played a pivotal role in the development of guidance documents, standards, and policy frameworks for medical device software and health IT. In 2010 she joined Microsoft’s Health Solutions Group as the Director of Regulations and Policy, where she was responsible for obtaining the appropriate global premarket registrations and managing Microsoft’s postmarket safety programs. She joined Biologics Consulting in 2012 and over the past 12 years has submitted more than one hundred 510(k) submissions as well as several noteworthy de novos in the digital health space. Donna-Bea received her B.S.E. in Engineering from Tulane University, her Ph.D. in Biomedical Engineering from the Johns Hopkins University, and her Master’s in Public Administration from the American University. 00:10 Introduction 01:37 The origins of FDA’s software regulation 08:44 The PC Era and off-the-shelf (OTS) components 14:34 The Quality Systems Regulation and its antecedents. Bad design not bad manufacturing. 16:56 Software-as-a-Medical Device and the role of imaging 23:02 Medical device data systems. The FDA and EHR systems. 27:43 The role of mobile devices, phones and watches. 30:47 Digital health, wellness and medical devices 37:03 Concluding thoughts. Additional Reading Material U.S. Federal Drug Administration (FDA). FDA POLICY FOR THE REGULATION OF COMPUTER PRODUCTS. 1989. Available from https://drive.google.com/file/d/1_7d2xB3E3ngu9UWPVlqYKRxtJ93gj3cP/view?usp=drive_link U.S. Food and Drug Administration (FDA). Medical Devices; Current Good Manufacturing Practice Final Rule; Quality System Regulation. Fed Regist . 1996;61(195). Available from: https://www.fda.gov/medical-devices/postmarket-requirements-devices/quality-system-qs-regulationmedical-device-good-manufacturing-practices U.S. Food and Drug Administration (FDA). General Principles of Software Validation; Final Guidance for Industry and FDA Staff. 2002. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/general-principles-software-validation U.S. Food and Drug Administration (FDA): Center for Devices and Radiological Health. Software as Medical Device (SAMD): Clinical Evaluation. Guidance for Industry and Food and Drug Administration Staff . 2017. Available from: https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd Center for Devices and Radiological Health. General Wellness: Policy for Low Risk Devices. Guidance for Industry and Food and Drug Administration Staff . U.S. Food and Drug Administration (FDA); 2019. Available from: https://www.fda.gov/media/90652/download Center for Devices, Radiological Health. Medical Device Data Systems, Medical Image Storage Devices, and Medical Image Communications Devices . U.S. Food and Drug Administration. FDA; 2019. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/medical-device-data-systems-medical-image-storage-devices-and-medical-image-communications-devices Center for Devices, Radiological Health. Multiple function device products: Policy and considerations . U.S. Food and Drug Administration. FDA; 2020. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/multiple-function-device-products-policy-and-considerations Center for Devices, Radiological Health. Off-the-shelf software use in medical devices . U.S. Food and Drug Administration. FDA; 2023 . Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/shelf-software-use-medical-devices Apple DeNovo Clearance Photoplethysmograph analysis software for over-the-counter use https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/denovo.cfm?id=DEN180042 09/11/2018 (Ms. Tillman is listed as the contact person for this)

This video is part of a series of guest expert interviews that we recorded for our new Yale Certificate Program on Medical Software and Medical AI – https://online.yale.edu/medical-software-ai-program. Our guest is Eric Henry. Mr. Henry is the Senior Quality Systems & Compliance Advisor at the Law Firm King & Spalding and works from his home in the Cleveland area. He joined King & Spalding in 2018 after 30 years managing global technical and regulatory compliance organizations in various industries and in medical devices in particular over the last 22 years. Eric currently provides advisory and consulting services to corporate management, boards, and staff regarding regulatory compliance, enforcement, and policy matters for regulated life sciences companies. Mr. Henry is a member of the AFDO/RAPS Healthcare Products Collaborative AI Strategic Committee and co-chairs their Good Machine Learning Practices Working Team. He also advises the Coalition for Health AI in their Predictive AI and Assurance Lab Certification Work Groups. 00:10 Introduction. Who is Eric Henry? 06:28 The FDA and AI. 14:45 The state of affairs outside the United States. China and the EU. 19:44 The general state of upheaval in Medical Devices/AI in the EU. 23:44 The current discussion on medical AI at the FDA. Potential issues with a new administration. 29:33 AI Tools development inside Health Systems. Challenges, fears and opportunities 38:34 Concluding Thoughts Additional Readings: European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence [Internet]. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202401689 U.S. Food and Drug Administration, Health Canada, Medicine & Helathcare products Regulatory Agency. Good Machine Learning Practice for Medical Device Development. 2021 Oct. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles U.S. Food and Drug Administration, Health Canada, Medicine & Helathcare products Regulatory Agency. Transparency for machine learning-enabled medical devices: Guiding principles. 2024 Jun. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/transparency-machine-learning-enabled-medical-devices-guiding-principles U.S. Food and Drug Administration, Health Canada, Medicine & Helathcare products Regulatory Agency. Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles 2023. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/predetermined-change-control-plans-machine-learning-enabled-medical-devices-guiding-principles Readings from Mr. Henry’s own work: AI/ML in Medical Devices: US & EU Regulatory Perspectives (https://array.aami.org/content/news/ai-ml-medical-devices-us-eu-regulatory-perspectives ) Medical Device Cybersecurity for Engineers and Manufacturers, Second Edition (Chapter 3: Global Regulations and Standards) (https://us.artechhouse.com/Medical-Device-Cybersecurity-for-Engineers-and-Manufacturers-Second-Edition-P2416.aspx ) “Bias in Artificial Intelligence In Healthcare Deliverables” (https://healthcareproducts.org/ai/aighi/aio/whitepaper-bias-in-ai-healthcare/ ) Software Under the Regulatory Microscope: The Current and Future State of Enforcement for Regulated Computer Systems (https://www.americanpharmaceuticalreview.com/Featured-Articles/574553-Software-Under-the-Regulatory-Microscope-The-Current-and-Future-State-of-Enforcement-for-Regulated-Computer/ ) You can find a full list of Mr. Henry’s publications, conference presentations, and media interviews on his LinkedIn profile: https://www.linkedin.com/in/eric-henry-519bb48/

This video is part of a series of guest expert interviews that we recorded for our new Yale Certificate Program on Medical Software and Medical AI – https://online.yale.edu/medical-software-ai-program. Our guest is Ian Sutcliffe, who is Principal Solutions Architect at AWS. Prior to that he has many years in the health sciences and tech industry in various software roles as both a developer and architect covering the solution, business and enterprise domains. He also serves on the AAMI Working Group that is developing guidance for the use of public cloud services in medical devices. The video was recorded on September 10, 2024. 00:10 Introduction 02:24 Creating a cloud service at “cloud scale.” 07:22 Dealing with changes in cloud software. 11:25 Automating testing and testing in production 15:00 Distributed systems 16:35 Compliance boundaries, medical device functions and computing environment 20:25 Designing with change in mind (non-monolithic systems) 22:17 Migrating existing systems to the cloud 25:41 Concluding thoughts Links: Association for the Advancement of Medical Instrumentation (AAMI). AAMI/CR510:2021; Appropriate use of public cloud computing for quality systems and medical devices. 2021. Report No.: CR510. Available from: https://array.aami.org/doi/book/10.2345/9781570208225

This video is part of a series of guest expert interviews that we recorded for our new Yale Certificate Program on Medical Software and Medical AI – https://online.yale.edu/medical-software-ai-program. Our guest is Prof Frangi is the Bicentennial Turing Chair in Computational Medicine at the University of Manchester, Manchester, UK, with joint appointments at the Schools of Computer Science and Health Sciences. He is also the Royal Academy of Engineering Chair in Emerging Technologies, with a focus on Precision Computational Medicine for in silico trials of medical devices. He is the Director of the Christabel Pankhurst Institute for Health Technology Research and Innovation (www.pankhurst.manchester.ac.uk). He conducts research in computational medical imaging and computational image-based medicine. Prof Frangi obtained his undergraduate degree in Telecommunications Engineering from the Technical University of Catalonia (Barcelona) in 1996. He pursued his PhD in Medicine at the Image Sciences Institute of the University Medical Centre Utrecht University on model-based cardiovascular image analysis. He leads the InSilicoUK Pro-Innovation Regulations Network (www.insilicouk.org). The video was recorded on July 19, 2024. 00:10 Introduction 10:07 From deep data to deep insights 20:35 Who was Christabel Panhurst? 23:33 In-silico trials: an introduction. 42:30 Regulators and in-silico trials. 50:11 Training people to work in this space and concluding thoughts. Links Frangi AF | Machine Learning for Computational Phenomics and In-Silico Trials: https://youtu.be/K8X9T7wSDqE?si=OdV1lRj0T_CZMYaa Frangi AF | Computational Medicine & Digital Twins Improving Medical Care: https://www.youtube.com/watch?v=afPmHkOjAWo&feature=youtu.be InSilicoUK Pro-Innovation Regulations Network www.insilicouk.org or join LinkedIn Group https://www.linkedin.com/groups/9169266/ Sarrami-Foroushani A, Lassila T, MacRaild M, Asquith J, Roes KCB, Byrne JV, Frangi AF. In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials. Nat Commun [Internet]. Springer Science and Business Media LLC; 2021 Jun 23 [cited 2022 Aug 19];12(1):3861. Available from: https://www.nature.com/articles/s41467-021-23998-w PMCID: PMC8222326 https://www.nature.com/articles/s41467-021-23998-w and https://vimeo.com/578167974 Liu Q, Sarrami-Foroushani A, Wang Y, MacRaild M, Kelly C, Lin F, Xia Y, Song S, Ravikumar N, Patankar T, Taylor ZA, Lassila T, Frangi AF. Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study. APL Bioeng. 2023 Jul 7;7(3):036102. doi: 10.1063/5.0144848. https://pubs.aip.org/aip/apb/article/7/3/036102/2900843/Hemodynamics-of-thrombus-formation-in-intracranial Redrup E, Mitchell C, Myles P, Branson R, Frangi AF. Cross-Regulator Workshop: Journeys, experiences and best practices on computer modelled and simulated regulatory evidence— Workshop Report [Internet]. InSilicoUK Pro-Innovation Regulations Network; 2023 [cited 2024 Sep 16]. Available from: https://zenodo.org/records/10121103 Frangi AF, Denison T, Myles P, Ordish J, Brown P, Turpin R, Kipping M, Palmer M, Flynn D, Afshari P, Lane C, de Cunha M, Horner M, Levine S, Marchal T, Bryan R, Tunbridge G, Pink J, Macpherson S, Niederer S, Shipley R, Dall’Ara E, Maeder T, Thompson M. Unlocking the power of computational modelling and simulation across the product lifecycle in life sciences: A UK Landscape Report [Internet]. Zenodo; 2023 [cited 2024 Sep 16]. Available from: https://zenodo.org/records/8325274 and https://vimeo.com/894224258

This video is part of a series of guest expert interviews that we recorded for our new Yale Certificate Program on Medical Software and Medical AI – https://online.yale.edu/medical-software-ai-program. Our guest is Dr. Vishisht Mehta who is the Director of Interventional Pulmonology at the Lung Center of Nevada, a division of Comprehensive Cancer Centers and also the Department Chair of Pulmonology at MountainView Hospital, both in Las Vegas. He is fellowship trained in Interventional Pulmonology, which specializes in the minimally invasive diagnosis and treatment of lung conditions. His interest and expertise lies in the application of artificial intelligence in pulmonology. He is also the founder of the webpage Pulmonary.ai. The video was recorded on July 26, 2024. 00:10 Introduction 02:56 Dr. Mehta’s initial interactions with AI vendors. 06:15 A doctor’s experience talking to engineers. 10:15 Where will we be in 5 years? 13:23 Patients’ reaction to the use of AI. 17:05 Training doctors to use AI 19:58 Presenting results to patients 22:40 Integrating AI technology into healthcare provider systems 30:27 Concerns 35:50 Concluding thoughts Links:Pulmonary.AI