David P.W. Rastall, DO, PhD, BFA is a neurologist and physician–scientist at Johns Hopkins whose research focuses on improving diagnostic accuracy and advancing the safe use of artificial intelligence in medicine. His work spans medical decision-making, human behavior, and AI implementation, with the goal of developing intelligent systems that improve patient care in real-world clinical environments. He leads AI strategic initiatives at the Armstrong Institute Center for Diagnostic Excellence and directs the MAX Lab, where his research ranges from evaluating and governing clinical AI systems to developing computational approaches that accelerate scientific discovery by identifying high-value experiments and new biomedical knowledge.
Education
8/02—4/07 Bachelor of Arts in Cognitive Science – Artificial Intelligence, Bachelor of Fine Arts in Painting, Bachelor of Arts in Art History, Indiana University, Bloomington, IN; Graduated with Highest Distinction.
1/09—12/09 Post-Bach, Michigan State University and Lansing Community College, MI; Completed all prerequisite coursework for medical school while carrying an accelerated course load (32 credit hours in a single semester).
4/10—1/17 Ph.D. in Cell and Molecular Biology, Michigan State University, East Lansing, MI. Focus: gene therapy, immunology, and data analytics. Oversaw other graduate students’ projects and growth.
7/10—4/18 Doctor of Osteopathic Medicine and Physician Scientist, Michigan State University College of Osteopathic Medicine, Medical Scientist Training Program, East Lansing, MI; Clinical Neuroscience Outstanding Achievement Award.
7/18—6/22 Neurology Resident, Michigan State University Neurology and Ophthalmology, East Lansing, MI; Education Chief Resident.
7/22—6/23 Vestibular & Ocular Motor Oto-Neurology Fellow, The Johns Hopkins University School of Medicine, Department of Neurology, Division of Neuro-Visual & Vestibular Disorders. Fellowship training under Drs. David Zee, Daniel Gold, David Newman-Toker, John Carey, and Amir Kheradmand.
Research
Selected Publications
Rastall DPW (first), Rehman M. Why Clinical Trials Will Fail to Ensure Safe AI. J Med Syst. 2025 Jul 17;49(1):98. doi: 10.1007/s10916-025-02231-x. PMID: 40670886. (https://link.springer.com/article/10.1007/s10916-025-02231-x)
Ariello LE, Wang K, Newman-Toker D, Bang J, Rastall DPW. Physiology-Driven Inference Using Large Language Models Enables Probabilistic Assessment of Huntington’s Disease from Smartphone Eye-Movement Data. AI. 2026; 7(7):236. https://doi.org/10.3390/ai7070236 (https://www.mdpi.com/2673-2688/7/7/236)
Rastall DPW, Green K. Deep learning in acute vertigo diagnosis. J Neurol Sci. 2022 Dec 15;443:120454. doi: 10.1016/j.jns.2022.120454. Epub 2022 Oct 19. PMID: 36379134. (https://www.jns-journal.com/article/S0022-510X(22)00316-1/fulltext)
Phan P, Rastall DPW. AI-in-the-Loop to Turbocharge Learning Health Systems. Int J Qual Health Care. 2026 May 14:mzag064. doi: 10.1093/intqhc/mzag064. Epub ahead of print. PMID: 42133827. (https://academic.oup.com/intqhc/article/38/2/mzag064/8678454?login=true)
Ariello LE, Gold DR, Mu W, Schubert MC, Allen C, Paul A, Rastall DPW (senior). Ocular Motor and Vestibular Profile in Spinocerebellar Ataxia Type 27B: Toward a Practical Bedside Diagnostic Framework. Cerebellum. 2025 Sep 4;24(5):149. doi: 10.1007/s12311-025-01906-y. PMID: 40906330. (https://link.springer.com/article/10.1007/s12311-025-01906-y)
Working Papers
Rastall DPW, Liu A, Keane PA, Popper C. Complete Validity in Medical AI. Under review (Perspective).
Rastall DPW, et al. The Curiosity Engine: Computing the Highest-Value Next Experiment in Biomedical Science. In preparation.
Rastall DPW, et al. Context Adaptation Trials Detect and Mitigate Novel AI-Specific Observer Effects in Clinical Evaluation. In preparation.
Teaching
AI Essentials for Business Essentials
Honors and Distinctions
Research Funding
Leon Levy Foundation Early Career Research Award (Principal Investigator), Using Artificial Intelligence to Understand Eye Movements in the Diagnosis of Neurodegenerative Diseases (2024–2026) Johns Hopkins Nexus Awards (Convening Leader), From Validation to Vigilance: The Bloomberg Center Convening Framework for Lifecycle Evaluation of Medical AI (2026)
Honors
Johns Hopkins Early Career Leadership Program Development Award (2026–2027)
Professional Recognition
Lead for Artificial Intelligence Innovation, Armstrong Institute Center for Diagnostic Excellence Founder and Director, Armstrong AI Studio Peer reviewer for seven international journals, including JAMA Neurology, Neurology, and Scientific Reports.
Impact and Engagement
Business
Advises health systems on the evaluation, governance, and implementation of artificial intelligence in clinical practice. Founder and Director of the Armstrong AI Studio, providing executive AI education for health system leaders and faculty. Develops AI strategy and executive education programs in collaboration with the American College of Healthcare Executives (ACHE). Leads implementation of AI-enabled diagnostic technologies and tele-neurology systems across multiple health systems.
Policy
Develops frameworks for the safe evaluation and lifecycle governance of medical AI used by health systems. Research focuses on AI safety, trust, and regulatory evaluation, including methods for assessing AI behavior beyond traditional clinical trials. Works with Johns Hopkins leaders on institutional AI governance, implementation, and diagnostic safety initiatives. Invited speaker on AI governance, safety, and implementation for national and international healthcare organizations.
Community
Delivers public lectures and continuing medical education on artificial intelligence and diagnostic safety. Mentors trainees, clinicians, and interdisciplinary researchers in AI, neurology, and implementation science. Expert media commentary on responsible AI in healthcare. Develops educational programs that translate complex AI concepts into practical guidance for clinicians and healthcare leaders.
In the Media
CBS News / KFF Health News: Expert commentary on AI transparency and medication diversion monitoring systems in U.S. hospitals. (June 1, 2026)
https://www.cbsnews.com/news/tennessee-hospital-nurse-fentanyl-theft-ai/