pub2postTrade-offs in Medical AI
🤖 Lack of relevant metrics hinders AI integration.
🔬Absence of clinical trials affects validation.
👥 Need for patient and physician involvement.
sayzardGoogle DeepMind (@GoogleDeepMind)
멀티모달 에이전트가 의료진과 환자를 더 잘 지원할 수 있는지 탐구하기 위한 새로운 연구 이니셔티브 'AI co-clinician'을 소개한다. 의료 분야에서 AI 에이전트의 실제 활용 가능성을 검증하는 중요한 프로젝트로 보인다.
Rohan Paul (@rohanpaul_ai)
의료 비전-언어 모델에서 강화학습(RL) 후학습은 완전히 새로운 능력을 학습시키기보다 기존 능력을 더 정교하게 다듬고, 출력 분포를 최적화해 효율성을 높이는 역할이 크다는 연구 결과를 제시한다.
https://x.com/rohanpaul_ai/status/2036653802204561594
#reinforcementlearning #medai #visionlanguage #machinelearning #research
Rohan Paul (@rohanpaul_ai) on X
This research shows that reinforcement learning (RL) in medical vision-language models mostly sharpens existing skills rather than teaching entirely new ones. Reinforcement learning post-training primarily refines output distributions to improve efficiency, while supervised
Scott McGrathBig tech is moving into your medical records. Microsoft’s Copilot Health will soon merge Apple Watch data with clinical histories to "connect the dots" in seconds. It’s an efficiency win, but the privacy stakes are high: HIPAA doesn't apply here, and AI bias hasn't been solved yet.
#MedSky #MedAI
A.I. Chatbots Want Your Health...
A.I. Chatbots Want Your Health...
A new JAMA report concludes that medicine is "flying blind" on AI. With widespread adoption but little proof of improved outcomes, it calls for standards for validation and oversight to close the gap between innovation and evidence.
#MedSky #MedAI #MLSky
JAMA: Medicine is flying blind...
JAMA: Medicine is flying blind...
Current benchmarks for medical LLMs create an "evaluation illusion." They use simplified data and tasks that don't reflect complex clinical reality. Automated metrics also fail to assess safety and utility, meaning high scores don't translate to real-world value.
#MedSky #MedAI #MLSky
The evaluation illusion of lar...
The evaluation illusion of lar...
The evaluation illusion of large language models in medicine - npj Digital Medicine
While large language models (LLMs) hold promise for transforming clinical healthcare, current comparisons and benchmark evaluations of large language models in medicine often fail to capture real-world efficacy. Specifically, we highlight how key discrepancies arising from choices of data, tasks, and metrics can limit meaningful assessment of translational impact and cause misleading conclusions. Therefore, we advocate for rigorous, context-aware evaluations and experimental transparency across both research and deployment.
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#MedAI #AI #YTeSo #AndroidApp #GeminiAI #HealthTech #Medicine
Patients are using AI chatbots like ChatGPT to interpret lab results from online portals. While this can help them understand complex data, experts warn of significant risks, including inaccurate information and privacy violations.
#MedAI #MedSky
Lab results confusing? Some pa...
Lab results confusing? Some pa...
A study finds clinicians rate peers who use generative AI for primary decision-making lower in skill and competence. Framing AI as a verification tool partially mitigates this negative perception but does not eliminate it.
#MedSky #MLSky #MedAI
Peer perceptions of clinicians...
Peer perceptions of clinicians...
Peer perceptions of clinicians using generative AI in medical decision-making - npj Digital Medicine
This study investigates how a physician’s use of generative AI (GenAI) in medical decision‑making is perceived by peer clinicians. In a randomized experiment, 276 practicing clinicians evaluated one of three vignettes depicting a physician: (1) using no GenAI (Control), (2) using GenAI as a primary decision-making tool (GenAI-primary), and (3) using GenAI as a verification tool (GenAI-verify). Participants rated the physician depicted in the GenAI‑primary condition significantly lower in clinical skill (on a 1–7 scale; mean = 3.79) than in the Control condition (5.93, p < 0.001). Framing GenAI use as verification partially mitigated this effect (4.99, p < 0.001). Similar patterns appeared for perceived overall healthcare experience and competence. Participants also acknowledged GenAI’s value in improving accuracy (4.30, p < 0.002) and rated institutionally customized GenAI more favorably (4.96, p < 0.001). These findings suggest that while clinicians see GenAI as helpful, its use can negatively impact peer evaluations. These effects can be reduced, but not fully eliminated, by framing it as a verification aid.