Arjun Manrai

Also known as Arjun Kumar Manrai, Arjun (Raj) Manrai

Arjun Manrai is a biomedical informatics researcher who applies machine learning and statistical modeling to medical decision-making. He earned a physics degree at Harvard College and a PhD in Bioinformatics and Integrative Genomics through Harvard-MIT. His research covers genetic variant misclassification, laboratory reference ranges, reproducibility in health-care machine learning, and medical AI evaluation. As of October 2026, he is an associate professor at Harvard Medical School and Senior Deputy Editor of NEJM AI.

Now at: Harvard University
Works: 121
Citations: 8,322
h-index: 38

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

2015

A Harvard-MIT PhD

In 2015, he completes a PhD in Bioinformatics and Integrative Genomics at the Harvard-MIT Division of Health Sciences and Technology under Professor Isaac Kohane. His education also includes a degree in Physics from Harvard College.
2016–2017

Fellowship and genetic misdiagnoses

From 2016 to 2017, he is a Biomedical Informatics Research Training Fellow in the Kohane Lab at Harvard Medical School. His research draws on statistical genetics, decision theory, and mathematical models for scientific reproducibility. In 2016, he and coauthors report that patients of African or unspecified ancestry receive positive reports for genetic variants later reclassified as benign; they call for sequencing diverse populations to prevent such misclassifications.
2018

Laboratory reference ranges

In 2018, he coauthors the JAMA article “In the Era of Precision Medicine and Big Data, Who Is Normal?” with Chirag Patel and John Ioannidis. It examines how laboratory test interpretation can vary by demographic traits such as age, race, and sex.
2020

Reproducibility in health care

In 2020, he coauthors a JAMA article with Andrew Beam and Marzyeh Ghassemi on reproducibility challenges for machine-learning models in health care.
July 2026

Evaluating medical AI

In July 2026, he writes that advances in medical AI raise the question of how to evaluate what works as the technology grows more capable.
As of October 2026

Professor and NEJM AI editor

As of October 2026, he is an Associate Professor of Biomedical Informatics in Harvard Medical School's Department of Biomedical Informatics and Senior Deputy Editor of NEJM AI. He leads a lab that applies machine learning and statistical modeling to medical decision-making, and is listed among faculty for HMS's Fall 2026 course AI in Medicine I.