AI in Medicine Checklists
This is an in progress post. More updates will be coming (soon).
Artificial Intelligence / Machine learning application to other disciplines is an evolving area, including medicine. Many researchers are rushing to use machine learning without a comprehensive understanding. There are many pitfalls and errors in machine learning that even AI practitioners sometimes are not invulnerable. Many reporting guidelines and checklists were developed in an attempt to fix those problems, yet I expect many more to come as more issues appear. Some of them are for general AI research and some of them are specific to a particular field. In this blog post, I compile general information, reporting guidelines and checklists for AI-in-medicine researchers. They might not directly solve the problems, but they can help us keep the issues in concern and give more crucial information to readers. Besides researchers, healthcare stakeholders can use these resources to evaluate AI-in-medicine research. If you find any errors or missing resources, please kindly contact me.
General
Guidelines for Human-AI Interaction
Generative AI in scientific paper authoring
Community
Ethics & Society at Hugging Face
Ethical guidelines for developing the Diffusers library
Research
Model Cards for Model Reporting
AI in Peer Review
Policy on Use of Generative Artificial Intelligence in the ARCs grants programs 2023
Science funding agencies say no to using AI for peer review
Funders agree on the use of AI tools in funding applications
Government
Europe
The State of State AI Policy (2021-22 Legislative Session)
Ethics guidelines for trustworthy AI
US
National Security Commission on Artificial Intelligence’s (NSCAI)
NIST AI Risk Management Framework (AI RMF)
An Accountability Framework for Federal Agencies and Other Entities
Thailand
2021 Thailand AI Ethics Guidelines by MDES
2022 AI Ethics Guidelines by NSTDA
2022 AI Ethics Guidelines by NSTDA (Book)
Reproducibility
Leakage and the Reproducibility Crisis in ML-based Science
Medicine
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Specialty
Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework
Five critical quality criteria for artificial intelligence-based prediction models
Government
Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices
FDA-approved A.I.-based algorithms
Clinical trials
Peer review
Think Again Before Using Generative AI During Peer Review or As You Prepare an Application