What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. - Sana Tonekaboni; Shalmali Joshi; Melissa D. McCradden; Anna Goldenberg (MLHC, 2019)

When Your Only Tool Is A Hammer: Ethical Limitations of Algorithmic Fairness Solutions in Healthcare Machine Learning. - Melissa D. McCradden; Mjaye Mazwi; Shalmali Joshi; James A. Anderson (AIES, 2020)

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. - Melissa D. McCradden; Shalmali Joshi; James A. Anderson; Mjaye Mazwi; Anna Goldenberg; Randi Zlotnik Shaul (J. Am. Medical Informatics Assoc., 2020)

How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU. - Sana Tonekaboni; Gabriela Morgenshtern; Azadeh Assadi; Aslesha Pokhrel; Xi Huang; Anand Jayarajan; Robert Greer; Gennady Pekhimenko; Melissa D. McCradden; Mjaye Mazwi; Anna Goldenberg (CHIL, 2022)

Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine - The Bethesda Report (AI Summit 2022). - Arman Rahmim; Tyler J. Bradshaw; Irène Buvat; Joyita Dutta; Abhinav K. Jha; Paul E. Kinahan; Quanzheng Li; Chi Liu; Melissa D. McCradden; Babak Saboury; Eliot L. Siegel; John J. Sunderland; et al. (CoRR, 2022)

An integration engineering framework for machine learning in healthcare. - Azadeh Assadi; Peter C. Laussen; Andrew J. Goodwin; Sebastian Goodfellow; Will Dixon; Robert W. Greer; Anusha Jegatheeswaran; Devin Singh; Melissa D. McCradden; Sara N. Gallant; Anna Goldenberg; Danny Eytan; et al. (Frontiers Digit. Health, 2022)

The silent trial - the bridge between bench-to-bedside clinical AI applications. - Jethro C. C. Kwong; Lauren Erdman; Adree Khondker; Marta Skreta; Anna Goldenberg; Melissa D. McCradden; Armando J. Lorenzo; Mandy Rickard (Frontiers Digit. Health, 2022)

Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare. - Viknesh Sounderajah; Melissa D. McCradden; Xiaoxuan Liu; Sherri Rose; Hutan Ashrafian; Gary S. Collins; James A. Anderson; Patrick M. Bossuyt; David Moher; Ara Darzi (Nat. Mach. Intell., 2022)

What's fair is... fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB. - Melissa D. McCradden; Oluwadara Odusi; Shalmali Joshi; Ismail Akrout; Kagiso Ndlovu; Ben Glocker; Gabriel Maicas; Xiaoxuan Liu; Mjaye Mazwi; Tee Garnett; Lauren Oakden-Rayner; Myrtede Alfred; et al. (FAccT, 2023)

No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging. - Charles Jones; Daniel C. Castro; Fabio De Sousa Ribeiro; Ozan Oktay; Melissa D. McCradden; Ben Glocker (CoRR, 2023)

The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. - Paula Dhiman; Rebecca Whittle; Ben Van Calster; Marzyeh Ghassemi; Xiaoxuan Liu; Melissa D. McCradden; Karel G. M. Moons; Richard D. Riley; Gary S. Collins (Nat. Mac. Intell., 2023)

A normative framework for artificial intelligence as a sociotechnical system in healthcare. - Melissa D. McCradden; Shalmali Joshi; James A. Anderson; Alex John London (Patterns, 2023)

Can an accurate model be bad? - Melissa D. McCradden; Mjaye L. Mazwi; Lauren Oakden-Rayner (Patterns, 2025)

Global Health in the Age of AI: Charting a Course for Ethical Implementation and Societal Benefit. - Jessica Morley; Emmie Hine; Huw Roberts; Renee Sirbu; Hutan Ashrafian; Charlotte Blease; Marisha Boyd; John L. Chen; Alexandre Chiavegatto Filho; Enrico W. Coiera; Glenn I. Cohen; Amelia Fiske; et al. (Minds Mach., 2025)