Nigam Shah

Nigam Shah

Stanford University

H-index: 77

North America-United States

About Nigam Shah

Nigam Shah, With an exceptional h-index of 77 and a recent h-index of 62 (since 2020), a distinguished researcher at Stanford University, specializes in the field of ontology, data mining, medical informatics, Biomedical Informatics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Red Teaming Large Language Models in Medicine: Real-World Insights on Model Behavior

Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center

Ethical and regulatory challenges of large language models in medicine

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study

MedAlign: a clinician-generated dataset for instruction following with electronic medical records

Ensuring useful adoption of generative artificial intelligence in healthcare

Feasibility of Automatically Detecting Practice of Race-Based Medicine by Large Language Models

Nigam Shah Information

University

Position

Associate Professor of Medicine

Citations(all)

29336

Citations(since 2020)

17689

Cited By

18273

hIndex(all)

77

hIndex(since 2020)

62

i10Index(all)

256

i10Index(since 2020)

206

Email

University Profile Page

Google Scholar

Nigam Shah Skills & Research Interests

ontology

data mining

medical informatics

Biomedical Informatics

Top articles of Nigam Shah

Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center

Journal of Palliative Medicine

2023/11/8

Ethical and regulatory challenges of large language models in medicine

2024/4/23

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

2024/4/16

Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study

JMIR Medical Informatics

2024/4/4

Ensuring useful adoption of generative artificial intelligence in healthcare

Journal of the American Medical Informatics Association

2024/3/7

Feasibility of Automatically Detecting Practice of Race-Based Medicine by Large Language Models

2024/3/1

Standing on FURM ground--A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems

arXiv preprint arXiv:2403.07911

2024/2/27

Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare

BMC Medical Informatics and Decision Making

2024/2/14

INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms

Advances in Neural Information Processing Systems

2024/2/13

Ehrshot: An ehr benchmark for few-shot evaluation of foundation models

Advances in Neural Information Processing Systems

2024/2/13

Health AI Assurance Laboratories—Reply

JAMA

2024/2/12

Zero-Shot Clinical Trial Patient Matching with LLMs

arXiv preprint arXiv:2402.05125

2024/2/5

A Nationwide Network of Health AI Assurance Laboratories

JAMA

2023/12

Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer’s and …

JAMIA open

2023/7/1

System and Method for Rapid Informatics-Based Prognosis and Treatment Development

2023/5/18

All models are local: time to replace external validation with recurrent local validation

arXiv preprint arXiv:2305.03219

2023/5/5

A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning

Journal of the American Medical Informatics Association

2023/5/1

Nigam Shah
Nigam Shah

H-Index: 47

Evaluation of feature selection methods for preserving machine learning performance in the presence of temporal dataset shift in clinical medicine

Methods of Information in Medicine

2023/5

See List of Professors in Nigam Shah University(Stanford University)

Co-Authors

academic-engine