Liam G. McCoy

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

2025 · 221 Zit. · Nature Medicine

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

2020 · 220 Zit. · npj Digital Medicine

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

2021 · 109 Zit. · Journal of Clinical Epidemiology

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

2021 · 104 Zit. · BMJ Health & Care Informatics

Peer review of GPT-4 technical report and systems card

2024 · 79 Zit. · PLOS Digital Health

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

2024 · 63 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 · 25 Zit. · BMC Medical Education

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

2024 · 20 Zit.

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

2024 · 19 Zit. · PLOS Digital Health

MIT COVID-19 Datathon: data without boundaries

2020 · 19 Zit. · BMJ Innovations

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration

2023 · 13 Zit. · Critical Care Explorations

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

2025 · 12 Zit. · NEJM AI

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

2025 · 8 Zit. · BMC Global and Public Health

Ethical debates amidst flawed healthcare artificial intelligence metrics

2024 · 7 Zit. · npj Digital Medicine