8 citations · 20 across the 4 of their papers we have counts for
5 papers
Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education
Thomas P Quinn, Simon Coghlan
Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with th…
Stool Studies Don't Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning
Thomas P. Quinn
In the machine learning culture, an independent test set is required for proper model verification. Failures in model verification, including test set omission and test set leakage…
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction
Tri Minh Nguyen, Thomas P Quinn, Thin Nguyen +1
Motivation: Many high-performance DTA models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more t…
Trust and Medical AI: The challenges we face and the expertise needed to overcome them
Thomas P. Quinn, Manisha Senadeera, Stephan Jacobs +2
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. However, failures of medical AI could have serious consequences for both clinical outcomes…
DeepCoDA: personalized interpretability for compositional health data
Thomas P. Quinn, Dang Nguyen, Santu Rana +2
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust. In the healthcare setting,…