arXiv AI
Aug 12
Selective Prediction Reduces the Negative Effects of Automation Bias Overall but Increases False Negatives
arXiv:2508. 07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making.
Position: Medical AI Neglects Real Treatment Outcomes
arXiv:2608. 14598v1 Announce Type: new Abstract: Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions.
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arXiv AI
Jun 30
An AI agent for treatment reasoning over a biomedical tool universe
arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.
arXiv AI
Jun 17
First, do NOHARM: towards clinically safe large language models
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv AI
4d ago
Applying Language Models in Clinical Medicine: Recent Trends and Perspectives
arXiv:2609.34780v2 Announce Type: replace Abstract: The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing attention in the litera...
arXiv Machine Learning
Sep 24
Artificial intelligence surrogates for treatment effect estimation with before-and-after data
The paper proposes a method for estimating treatment effects using AI-generated predictions as surrogates, applied to paired before-and-after measurements for each treated individual. By comparing AI predictions before and after treatment, the approach can identify the average treatment effect on the treated under certain technical assumptions, even when clinical outcomes are never observed for treated subjects. When assumptions are questionable, the authors introduce prediction‑powered inference that corrects bias with a small set of observed outcomes, and validate the method with synthetic and cardio‑oncology data.
arXiv AI
Jul 15
First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations
arXiv:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv AI
Sep 10
Teaching agentic AI to generalize expert diagnostic reasoning in rare diseases
arXiv:2606.16149v5 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer. Large language models rank the correct disease first i...
arXiv AI
Sep 1
AI Can Be Easily Persuaded in Clinical Decision Making
arXiv:2608.29453v1 Announce Type: cross Abstract: As AI becomes increasingly integrated into clinical practice, it is playing a growing role in medical decision making. Medicine, however, is a high s...
arXiv AI
Jul 28
Principles and Guidelines for Randomized Controlled Trials in AI Evaluation
arXiv:2605. 02050v2 Announce Type: replace-cross Abstract: This work establishes a framework for standardizing AI evaluation RCTs (sometimes called human uplift studies).
arXiv AI
Aug 13
Teaching agentic AI to learn expert reasoning for rare disease diagnosis
arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.
MIT News AI
Aug 4
The benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
arXiv AI
Sep 2
AI Morbidity and Mortality: A Framework for Clinical AI Failure Review
AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.