Liam G. McCoy

The TRIPOD-LLM reporting guideline for studies using large language models

2025 · 312 Zit. · Nature Medicine

What do medical students actually need to know about artificial intelligence?

2020 · 230 Zit. · npj Digital Medicine

Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based

2021 · 112 Zit. · Journal of Clinical Epidemiology

Equity in essence: a call for operationalising fairness in machine learning for healthcare

2021 · 107 Zit. · BMJ Health & Care Informatics

Peer review of GPT-4 technical report and systems card

2024 · 91 Zit. · PLOS Digital Health

A toolbox for surfacing health equity harms and biases in large language models

2024 · 70 Zit. · Nature Medicine

Ensuring machine learning for healthcare works for all

2020 · 34 Zit. · BMJ Health & Care Informatics

Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review

2024 · 31 Zit. · BMC Medical Education

Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking Study

2025 · 24 Zit. · NEJM AI

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

2024 · 23 Zit. · PLOS Digital Health

The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models Use

2024 · 22 Zit. · medRxiv

MIT COVID-19 Datathon: data without boundaries

2020 · 19 Zit. · BMJ Innovations

Building health systems capable of leveraging AI: applying Paul Farmer’s 5S framework for equitable global health

2025 · 10 Zit. · BMC Global and Public Health

Ethical debates amidst flawed healthcare artificial intelligence metrics

2024 · 7 Zit. · npj Digital Medicine

Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study

2025 · 7 Zit. · Medical Science Educator