arXiv AI

Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities

arXiv:2604. 14514v2 Announce Type: replace Abstract: Healthcare disparities persist across socioeconomic boundaries, often attributed to unequal access to screening, diagnostics, and therapeutics.

arXiv AI

Aug 11

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

arXiv:2505. 16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability.

By Vincent Jeanselme, Zilin Jing, Aparajita Kashyap, Chao Pang, Florent Pollet, Young Sang Choi, Xinzhuo Jiang, Yuta Kobayashi, Yanwei Li, Sara Matijevic, Karthik Natarajan, Shalmali Joshi

benchmarks safety

arXiv AI

Jun 2

Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.

By Qiao Jin, Yin Fang, Lauren He, Yifan Yang, Guangzhi Xiong, Zhizheng Wang, Nicholas Wan, Joey Chan, Donald C. Comeau, Robert Leaman, Charalampos S. Floudas, Aidong Zhang, Michael F. Chiang, Yifan Peng, Zhiyong Lu

llms nlp benchmarks safety

arXiv AI

1d ago

Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic

arXiv:2608. 16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research.

By Simon Ellershaw, Christopher Tomlinson, Zeljko Kraljevic, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Angela M. Wood, Anoop D. Shah, Richard Dobson

llms benchmarks

arXiv AI

Jun 2

A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models

arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.

By Kara Liu, Maggie Wang, Russ B. Altman

safety

arXiv AI

Jul 14

NVAITC AI Scientist: A Governed End-to-End Research System -- A Hypertension GWAS Case Study

arXiv:2607. 11084v1 Announce Type: new Abstract: Agentic research systems are emerging as a new paradigm for coordinating scientific workflows beyond isolated model inference, code generation, or statistical analysis.

By Eddie Huang (NVIDIA AI Technology Center, NVIDIA Corporation), Ken Liao (NVIDIA AI Technology Center, NVIDIA Corporation), Iven Fu (NVIDIA AI Technology Center, NVIDIA Corporation), Yang-Hsien Lin (NVIDIA AI Technology Center, NVIDIA Corporation), Chao-Shun Zhan (NVIDIA AI Technology Center, NVIDIA Corporation), Andy Liao (NVIDIA AI Technology Center, NVIDIA Corporation), Virginia Chen (NVIDIA AI Technology Center, NVIDIA Corporation), Johnson Sun (NVIDIA AI Technology Center, NVIDIA Corporation), Pika Wang (NVIDIA AI Technology Center, NVIDIA Corporation), Richard Huang (NVIDIA AI Technology Center, NVIDIA Corporation), Jiun-Cheng Jiang (NVIDIA AI Technology Center, NVIDIA Corporation), Ting-Yuan Liu (Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, Master Program for Digital Health Innovation, China Medical University, Taichung, Taiwan), Hsing-Fang Lu (Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan), Ray Y. Lee (AI-Driven Genomic Medicine and Drug Discovery Lab, China Medical University Hospital, Taichung, Taiwan), Chi-Chou Liao (Department of Medical Research, China Medical University Hospital, Taichung, Taiwan), Simon See (NVIDIA AI Technology Center, NVIDIA Corporation), Fuu-Jen Tsai (Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, Taiwan)

agents multimodal safety

arXiv AI

Jul 8

Whose fairness? Structural concentration in AI bias research

arXiv:2607. 05574v1 Announce Type: cross Abstract: Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias.

By Abhash Shrestha, Subigya Gautam, Anu Sapkota, Sanju Tiwari, Tek Raj Chhetri

benchmarks safety

arXiv AI

Aug 7

CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction

arXiv:2604. 16742v2 Announce Type: replace Abstract: Scientists have long sought to accurately predict outcomes of real-world events before they happen.

By Jianyou Wang, Youze Zheng, Longtian Bao, Hanyuan Zhang, Qirui Zheng, Yuhan Chen, Yang Zhang, Matthew Feng, Maxim Khan, Aditya K. Sehgal, Christopher D. Rosin, Ramamohan Paturi, Umber Dube, Leon Bergen

llms benchmarks

arXiv AI

Jun 11

BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts

arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.

By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 19

EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning

arXiv:2510. 19893v2 Announce Type: replace Abstract: Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations.

By Shiqi Dai, Wei Dai, Jiaee Cheong, Paul Pu Liang

reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 7

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.

By Chama Bensmail

safety

arXiv Machine Learning

Jun 5

Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction

arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.

By Amirhossein Zare, Amirhessam Zare, Herlock Rahimi, Reza Salarikia, Mohammad Kashkooli

benchmarks

arXiv AI

Jul 2

SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification

arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.

By Jose D. Posada, David Love, Somalee Datta, Priya Desai

llms efficiency benchmarks

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