The article discusses how Clinical AI can predict outcomes based on data generated by existing clinical judgment, but such performance alone does not prove usefulness before recognition or across care processes. It proposes evidence requirements tailored to the intended use and information conditions, including state‑reference and proxy‑relative timing tests, to strengthen recognition claims. The framework aims to help hospitals determine whether the conditions that support performance are present when assistance is most needed, making questions that current evaluation practices overlook explicit and specifying the direct evidence required to answer them.
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
arXiv:2603. 25821v2 Announce Type: replace-cross Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient interactions.
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.
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
arXiv:2610.33592v1 Announce Type: new Abstract: Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility r...
arXiv:2609.14819v1 Announce Type: cross Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and...
arXiv:2607. 15166v1 Announce Type: new Abstract: Most medical AI benchmarks measure whether a model knows the correct answer.
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:2508. 07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making.
The paper introduces PrecepTron, a 32‑billion‑parameter language model fine‑tuned with low‑rank adaptation to evaluate clinical reasoning in large language models (LLMs) at a physician level. It also releases GRAND‑ROUNDS, a benchmark of 9,217 scored responses from 160 clinicians across seven studies. Using PrecepTron, the authors replicate key findings from major medical AI studies and explore new questions about LLM diagnostic accuracy, demonstrating that fine‑tuned models can provide consistent, scalable physician‑level scoring.
The paper introduces a clinician‑grounded evaluation platform called InterviewPlayground, which uses a memory‑augmented patient simulator to assess AI‑assisted psychiatric intake systems. It supports comparison across different interviewing styles, reduces clinician workload, and measures clinically relevant performance. In a pilot study, a GPT‑based intake interviewer captured more relevant items but made more unfounded inferences and missed safety concerns compared to clinicians.
Synthetic Hospital is an open, fully synthetic longitudinal electronic health record benchmark created from public medical education material. It contains 1,268 patients and 5,602 encounters, with every diagnosis, finding, and temporal relation grounded in standard ontologies and traceable back to its source. Physicians could not reliably distinguish its records from real charts, and current frontier AI models perform significantly below human experts on tasks such as reconstructing problem lists and summarizing charts.