arXiv AI August 17, 2026 By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig

Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice

arXiv:2608. 14399v1 Announce Type: cross Abstract: Patients increasingly ask large language model (LLM) assistants which doctor to see, making these systems AI infomediaries: algorithms that intermediate one person's choice among other people and thereby decide, silently and at scale, which physicians become visible.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jul 22

Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety

arXiv:2607. 18828v1 Announce Type: new Abstract: Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks.

By Koyar Afrasyab

llms multimodal benchmarks safety

arXiv AI

Jun 3

AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making

arXiv:2606. 03198v1 Announce Type: cross Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized.

By Sangwon Baek, Kyu Yeon Hur, Kyunga Kim

llms

arXiv Machine Learning

Jun 5

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

arXiv:2604. 07709v4 Announce Type: replace-cross Abstract: A heavily safety-trained model will hand a physician the full, patient-followable benzodiazepine taper and refuse it to the patient who needs it, over identical clinical facts; the knowledge is present either way.

By David Gringras

llms benchmarks safety

arXiv AI

2d ago

Demographic Injection in Medical Language Models under Diversity, Equity, and Inclusion Prompts

arXiv:2608. 15254v1 Announce Type: new Abstract: Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI).

By Diego Mardian, Frank Liu

llms benchmarks

arXiv Machine Learning

Aug 11

Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutions

arXiv:2608. 07550v1 Announce Type: cross Abstract: Vision-language models return structured chest-radiograph findings through interfaces exposing no confidence score, so a receiving institution cannot read off how far to trust an individual judgment.

By Pengyang Yu, Yiou Wang, Zhongping Dong, Sahraoui Dhelim, Chun-Mei Feng, M. Tahar Kechadi

llms multimodal

arXiv AI

Jun 15

Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?

arXiv:2604. 14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators.

By Amy Rouillard, Sitwala Mundia, Linda Camara, Ziyaad Dangor, Michael Cameron Gramanie, Ismail Kalla, Shabir A. Madhi, Kajal Morar, Marlvin T. Ncube, Haroon Saloojee, Bruce A. Bassett

llms agents benchmarks safety

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Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety

AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures

Demographic Injection in Medical Language Models under Diversity, Equity, and Inclusion Prompts

Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutions

Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?