Editors

Isaac Kohane, M.D., Ph.D.

Marion V. Nelson Professor and Chair, Department of Biomedical Informatics, Harvard Medical School

Isaac “Zak” Kohane joined the New England Journal of Medicine as a member of the editorial board in 2019 where he focused on the evaluation of investigations that used artificial intelligence (AI) methodologies. He is committed to making NEJM AI a trusted authority for promoting the use of safe, unbiased and efficacious artificial intelligence applications to clinical medicine by setting the standard for rigorous and transparent clinical evaluations.

Zak is the Marion V. Nelson Professor and the inaugural Chair of the Department of Biomedical Informatics at Harvard Medical School, where he works on using computational techniques to instrument healthcare systems as living laboratories. He also uses machine learning techniques and multimodal measurements to better diagnose and understand complex disorders such as autism.

Zak completed a fellowship and residency in pediatrics at Boston Children’s Hospital. His current clinical focus is undiagnosed diseases. He completed a PhD in Computer Science during an earlier heyday of AI in medicine. Zak has published over 500 papers in the medical literature and is a member of the Institute of Medicine and the American Society for Clinical Investigation.

A native of Geneva, Switzerland, Zak resides in Newton, Massachusetts and has a beautiful Belgian sheepdog to keep him company as his three children head off to college.

Andrew Beam, Ph.D.

Assistant Professor, Department of Epidemiology, Harvard T.H. Chan School of Public Health

Andrew Beam, PhD, is a founding deputy editor of NEJM AI and a co-host of the NEJM Group podcast AI Grand Rounds. Andrew is a long time AI optimist and is deeply committed to realizing an AI-enabled healthcare system that works for everyone. Andrew is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with a secondary appointment in the Department of Biomedical Informatics at Harvard Medical School. His lab develops new artificial intelligence methods by combining large scale deep learning models with techniques from causal inference to improve the safety and robustness for medical applications. He has a particular interest in using AI to improve neonatal and perinatal outcomes. Outside of the lab, Andrew lives in Waltham, MA, with his wife Kristyn, a neonatologist and frequent collaborator. They are currently focused on training their largest neural net to date: a 3-year-old named Hallie whose entropy maximization algorithm means never a shortage of things to clean up!

Arjun (Raj) Manrai, Ph.D.

Assistant Professor, Department of Biomedical Informatics, Harvard Medical School

Arjun “Raj” Manrai, Ph.D., joined the New England Journal of Medicine in 2022 as a founding Deputy Editor of NEJM AI. From Raj’s perspective, NEJM AI will help bridge the gap between the growing capabilities of artificial intelligence and the evidence needed by clinicians to improve care.

Raj is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School, where he directs a research lab that works broadly on machine learning and statistical approaches to improve medical decision-making. Focus areas for his group include the clinical use of genomic data and blood laboratory biomarkers, inherited heart disease and kidney disease, decision making across populations, and reproducibility challenges for medical AI.

Raj took the scenic route to medical AI, earning an A.B. in Physics from Harvard College followed by a Ph.D. in Bioinformatics and Integrative Genomics from the Harvard-MIT Division of Health Sciences and Technology (HST).

He resides in the Boston area and outside work he can usually be found losing home dance competitions to his 2 young daughters.

Euan Ashley, MB ChB, DPhil, Stanford University

Euan Ashley is Associate Dean and Professor of Medicine and Genetics at Stanford University in California. Over the last decade his team has focused on the application of the human genome to medicine. He was recognized by the Obama White House for his contributions to Personalized Medicine and awarded the American Heart Association Medal of Honor for Genomic and Precision Medicine. His book The Genome Odyssey – Medical Mysteries and the Incredible Quest to Solve Them was released in 2021. He is co-founder of three companies: Personalis, DeepCell and SVEXA. Father to three young Americans, in his spare time, he tries to understand American football, plays jazz saxophone, and conducts research on the health benefits of single malt Scotch whisky.

Noa Dagan, MD, PhD (Comp. Sci.), MPH, Clalit

Noa Dagan is a public health physician and researcher. She holds an MD and an MPH from the Hebrew University, and a Ph.D. in Computer Science from Ben-Gurion University. She completed her postdoc in the Department of Biomedical Informatics at Harvard Medical School. 

Dr. Dagan heads the AI-driven Medicine Department at Clalit Innovation (Clalit is Israel's largest integrated healthcare organization, covering over 50% of the Israeli population). Her responsibilities include the development and implementation of digital healthcare solutions to promote preventive, proactive, and personalized medicine. She leads the entire lifecycle of AI-driven interventions, from conception, through machine-learning modeling, to implementation in medical practice.

Dr. Dagan is also the co-director of the Digital Health Lab at the department of Software and Information Systems Engineering at Ben-Gurion University. Her research focuses on prediction models, algorithmic fairness, and causal inference based on real-world data.

Judy Wawira Gichoya, MD, Emory University

Dr. Gichoya is a multidisciplinary researcher, trained as both an informatician and a clinically active radiologist. She is an assistant professor at Emory University, and works in Interventional Radiology and Informatics. She is seconded to the National Institutes of Health as a data scholar to help with the Open Data Science Platform (OSDP) component of the DSI Africa Initiative to “Harness Data Science for Health In Africa”. Her career focus is on validating machine learning models for health in real clinical settings, exploring explainability, fairness, and a specific focus on how algorithms fail. She has worked on the curation of datasets for the SIIM (Society for Imaging Informatics in Medicine) hackathon and ML committee. She volunteers on the ACR and RSNA machine learning committees to support the AI ecosystem to advance development and use of AI in medicine.

Chris Holmes, PhD, University of Oxford

Chris Holmes is Professor of Biostatistics at the Departments of Statistics and Nuffield Department of Medicine, University of Oxford, and Programme Director for Health and Medical Sciences at the Alan Turing Institute, London, UK. Chris serves on the International Scientific Advisory Board for UK Biobank and the UK’s National Health Service (NHS) AI Award Evaluation Advisory Group. Chris is a founding member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and co-director of the ELLIS Program in Robust Machine Learning. He is previous recipient of the Research Prize from the Royal Statistical Society and a Medallion awardee from the Institute for Mathematical Statistics (IMS). Chris’ interests are in theory, methods and applications of robust statistical machine learning and causal inference to addressing real-world problems in health. 

Daphne Koller, PhD, Insitro

Daphne Koller is CEO and Founder of insitro, a machine learning-driven drug discovery and development company. Daphne is also co-founder of Engageli, was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years, the co-CEO and President of Coursera, and the Chief Computing Officer of Calico Labs. She is the author of over 300 refereed publications with an h-index of 146. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012. She received the MacArthur Foundation Fellowship in 2004, the ACM Prize in Computing in 2008, the ACM AAAI Allen Newell Award in 2019, and the AnitaB.org Technical Leadership Abie Award Winner in 2022. She was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Association for Artificial Intelligence in 2004, the American Academy of Arts and Sciences in 2014, and the International Society of Computational Biology in 2017.

Lauren Oakden-Rayner, MBBS, University of Adelaide

Dr. Lauren Oakden-Rayner (FRANZCR, PhD) is the Director of Research in Medical Imaging at the Royal Adelaide Hospital and is a senior research fellow at the Australian Institute for Machine Learning. Her research explores the safe translation of artificial intelligence technologies into clinical practice, both from a technical and clinical perspective.

Ziad Obermeyer, MD, UC Berkeley

Ziad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. He works at the intersection of machine learning and health. He is a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and was named an Emerging Leader by the the National Academy of Medicine. His papers have appeared in a wide range of journals, including Science, Nature Medicine, New England Journal of Medicine, JAMA, and ICML, and have won awards from professional societies in medicine and economics. His work on algorithmic bias is frequently cited in the public debate about artificial intelligence, and in federal and state regulatory guidance and investigations. He is a co-founder of Nightingale Open Science, a non-profit that makes massive new medical imaging datasets available for research, and Dandelion, a platform for AI innovation in health. Previously, he was a consultant at McKinsey & Co., and an Assistant Professor at Harvard Medical School. He continues to practice emergency medicine in underserved communities.

Lily Peng, MD, PhD, Verily

Dr. Peng is a physician-scientist and a director of product management at Verily, where she works on accelerating evidence generation through Verily’s Clinical Studies Platforms. Before Verily, she co-led Google Health AI in applying AI to enable better and more equitable care, particularly for diabetic eye disease, cardiovascular disease, and cancer. Her resulting papers have been published in JAMA, Nature, Nature Medicine, and Nature Biomedical Engineering.

She graduated with an MD/PhD in Bioengineering from the University of California, San Francisco and a B.S. in Chemical Engineering from Stanford. She co-founded Nano Precision Medical, a drug delivery device start-up, and was a product manager at Doximity. She has been named in Fortune magazine’s 40 under 40 in Health, and Wired magazine’s list of 20 People Who Are Creating the Future.

Pranav Rajpurkar, PhD, Harvard Medical School

Pranav Rajpurkar is an Assistant Professor at Harvard Medical School leading a research lab working on developing artificial intelligence technologies for medical applications. His lab has developed label-efficient deep learning algorithms that can read medical images at the level of experts, built large-scale open medical datasets, and demonstrated the effects of AI on medical decision making. Prof. Rajpurkar co-hosts The AI Health Podcast and co-edits the Doctor Penguin AI Health Newsletter. He instructed the Coursera course series on AI for Medicine, and leads the joint Harvard-Stanford Medical AI Bootcamp Program. Previously, Prof. Rajpurkar received his B.S., M.S., and Ph.D. degrees, all in Computer Science from Stanford University.

Krishna Yeshwant, MD, MBA, Google Ventures

Krishna helps steward the GV investing team and co-leads GV’s life sciences group. Krishna led GV’s early investments in Flatiron Health, Foundation Medicine, Relay Therapeutics, Beam Therapeutics, insitro, One Medical, and Aledade. He also established GV’s incubation program, which helped start companies like ROME Therapeutics and Verve Therapeutics. 

Krishna was part of a team that helped in the early days of GV’s founding and led the fund’s early commitment to invest in life sciences. Earlier in his career, he helped start an electronic data interchange company acquired by Hewlett-Packard and a network security company acquired by Symantec. 

Krishna graduated from Harvard’s MD/MBA program where he wrote software to use imaging systems to guide surgical procedures. He went on to serve as an attending in internal medicine at Brigham and Women’s Hospital.

Marinka Zitnik, PhD, Harvard Medical School

Marinka Zitnik is an Assistant Professor of Biomedical Informatics at Harvard Medical School with additional appointments at Harvard Data Science Initiative and Broad Institute of Harvard and MIT. Dr. Zitnik works on infusing knowledge, structure, and geometry into machine learning models. She is especially interested in using the methods to produce actionable representations for therapeutic development and precision medicine. Dr. Zitnik is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems Society. Her research won paper and research awards from the International Society for Computational Biology, Bayer Early Excellence in Science, Amazon Faculty Research, Roche Alliance with Distinguished Scientists, Rising Star Award in Electrical Engineering and Computer Science, and Next Generation in Biomedicine Recognition. Dr. Zitnik leads Therapeutics Data Commons, an initiative to access and evaluate AI capability across therapeutic modalities and stages of drug development, and the AI4Science initiative to examine opportunities in AI and capture grand challenges to realizing those opportunities. 

James Zou, PhD, Stanford University

James Zou is an assistant professor of Biomedical Data Science at Stanford University. He develops machine learning methods for biology and medicine. He works on both improving the foundations of ML–-by making models more trustworthy and reliable–-as well as in-depth scientific and clinical applications. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, and Adobe.