Charles Friedman hosts our March Seminar Series
March 5, 2025
We were thrilled to welcome Charles Friedman from the University of Michigan Medical School,
who delivered our March Seminar Series with his talk,
"Why AI and Learning Health Systems Need Each Other".
Charles began by advancing the idea that, while both are extremely important: AI is a means and Learning Health Systems (LHS) are an end--and why it is most important to maintain that distinction. He introduced the socio-technical infrastructure required for high-functioning learning systems and argue that this infrastructure provides a framework, actually a schematic, for successfully implementing AI into healthcare.
Charles Friedman is Professor of Learning Health Sciences at the University of Michigan Medical School, where he directs the Knowledge Systems Laboratory. He was formerly Founding Chair of the Department of Learning Health Sciences and the Josiah Macy Jr. Professor of Medical Education. He holds joint appointments in the Schools of information and Public Health. He is editor-in-chief of the open-access journal Learning Health Systems and co-chair of the multi-national movement to Mobilize Computable Biomedical Knowledge.
Throughout his career, Friedman has developed and studied methods to improve health, education, and research through innovative applications of information technology. Most recently, Friedman has focused his academic interests and activities on the concept of Learning Health Systems that improve health by marrying discovery to implementation, and the socio-technical infrastructure required to sustain these systems.
Friedman is a Distinguished Fellow of the American College of Medical Informatics, and a founding fellow of the International Academy of Health Sciences Informatics. He holds an honorary doctorate from the University of Lucerne in Switzerland for his contributions to the science of Learning Health Systems. Prior to coming to Michigan, Friedman held executive positions at the Office of the National Coordinator for Health IT (ONC) in the U.S. Department of Health and Human Services. Immediately prior to his work in the government, he was Associate Vice Chancellor for Biomedical Informatics, and Founding Director of the Center for Biomedical Informatics at the University of Pittsburgh.
Seminar Series Event: "Why AI and Learning Health Systems Need Each Other"
Date and Time:
Wednesday 26 March 2025, 10:00 – 11.00 hrs (BST)
Location: The Anatomy Museum, King's Building, Room K6.36, Strand Campus, Strand, London, WC2R 2LS
Attendance:
Mandatory for all DRIVE-Health students, therefore please accept the calendar invitation.
Registration: Alumni and wider King's College London research community all welcome - please email
drive-health-cdt@kcl.ac.uk
to let us know if you would like to attend.
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We are delighted to welcome Srinivasan Vairavan , Director of Digital Health and AI Biomarkers at Johnson & Johnson Innovative Medicine R&D and Visiting Lecturer at King's College London , to deliver his seminar, "Precision Psychiatry through Digital Phenotyping: Lessons Learned from Three Longitudinal Studies in major depressive disorder" , as part of the EPSRC DRIVE-Health Seminar Series. Abstract: Major Depressive Disorder (MDD) remains diagnosed and monitored almost entirely through episodic, subjective self-report, limiting both clinical care and trial sensitivity. This lecture presents evidence that passively collected digital signals can objectively characterize core MDD symptom domains and track an individual's mental state continuously between clinic visits. Drawing on more than 1,000 patients across three major longitudinal cohorts — RADAR-CNS, ORBIT (NCT02489305), and CANBIND — we describe methods that translate subtle behavioral and physiological data into clinically interpretable biomarkers. Paralinguistic analysis of speech is used to quantify psychomotor retardation, while fractal signatures derived from actigraphy index depression severity and impending relapse. Building on these markers, we present a personalized, N-of-1 anomaly detection framework that fuses passive digital streams with intermittent self-report to flag elevated relapse risk at the individual level. Results support the clinical validity of this approach. Digital measures, including speech intensity, showed significant associations with clinical outcomes. The relapse prediction model achieved a balanced accuracy exceeding 71%, with a median detection lead time of two to three weeks prior to clinical onset and a low false alarm rate — a profile compatible with real-world deployment. The lecture will also address principal sources of measurement variability, including seasonal effects, and the emerging relationship between cognitive performance and digital biomarkers. Together, these findings indicate a shift from episodic subjective assessment toward objective, unobtrusive, and continuous monitoring in mental healthcare — with near-term implications for patient selection and endpoint sensitivity in clinical trials, and longer-term potential for personalized diagnosis and treatment in MDD. Seminar Series Event : "Precision Psychiatry through Digital Phenotyping: Lessons Learned from Three Longitudinal Studies in major depressive disorder" Date and Time: Thursday 15 October 2026, 13:30 – 14:30 (BST) Location: The River Room, King’s Building, 2nd Floor, Room KIN 227, Strand Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and the wider King's College London research community are welcome - no booking required. Biography Srinivasan Vairavan , PhD, is Director of Digital Health and AI Biomarkers within Neuroscience Data Science and Digital Health at Johnson & Johnson Innovative Medicine R&D, and a Visiting Lecturer in the Department of Biostatistics and Health Informatics at the Institute of Psychiatry, Psychology and Neuroscience, King's College London. He has over 15 years of experience building and scaling AI, machine learning and advanced analytics capabilities across pharma and healthcare, with prior roles at Proteus Digital Health and Philips Research. His work translates large-scale clinical, digital health, molecular and EHR data into regulatory-grade endpoints, including engagement through FDA Type C meetings and EMA Qualification Advice, and co-development of the industry-wide V3+ framework. He leads and contributes to major international consortia including RADAR-CNS, RADAR-AD and AMP-SCZ. He holds patents in relapse prediction in major depressive disorder and speech-based detection of cognitive decline, sits on the editorial board of BMC Digital Health, and serves on the DRIVE-Health advisory board.
We are looking forward to welcoming Professor Honghan Wu, Professor of Health Informatics and AI at the University of Glasgow, who will deliver his talk “Large language model and Radiology: how to facilitate human and AI collaboration? " as part of our Seminar Series. Abstract: In this upcoming talk, Professor Honghan Wu explores the essential shift from viewing AI as a potential replacement for radiologists to recognizing it as a critical collaborative partner. Moving beyond basic tasks like detection and triage, the presentation highlights how AI can address practical clinical "pain points," such as reducing automated protocoling time by up to 60% and decreasing the time spent communicating with providers and patients by 30%. Professor Wu will present recent research on using knowledge-retrieval and Large Language Models for clinical report error correction and generation. The session concludes with an examination of the real-world deployment lifecycle, discussing the challenges of monitoring the over 700 FDA-cleared radiology AI devices currently in practice Seminar Series Event : “Large language model and Radiology: how to facilitate human and AI collaboration?" Date and Time: Thursday 25 November 2026, 15:00 – 16.00 hrs (GMT) Location: Venue to be confirmed. Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.
