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This paper presents an automated diagnostic system for grading knee osteoarthritis severity using an optimized ResNet-18 model deployed on edge devices via TensorFlow Lite. It integrates an LLM interface using Gemini 2.0 Flash to provide structured interpretive findings while maintaining offline capability for resource-constrained environments.
This paper investigates whether linearly decodable failure signals in LLM hidden states can be corrected via residual-stream steering. It finds that while 'overthinking' failures are decodable, fixed linear steering fails to correct them due to representational entanglement with task-critical computations, though the probes effectively support selective abstention.
The paper introduces CXR-MAX, a large-scale benchmark for evaluating reasoning alignment in non-stationary environments using X-ray data from multiple MLLMs.
Introduces CGM-JEPA, a self-supervised pretraining framework for continuous glucose monitor data that improves cross-modal and cross-cohort performance through masked latent prediction and distributional objectives.
Researchers propose a surrogate modeling framework to quantify and interpret latent medical knowledge encoded in black-box LLMs, revealing both valid associations and persistent racial biases.
This paper introduces MedSkillAudit, a domain-specific framework for auditing the safety and quality of medical research AI agent skills before deployment. The study demonstrates that the system achieves reliable assessment consistency comparable to or better than human expert review.
EmpirischTech released Chaperone-Thinking-LQ-1.0, a 4-bit GPTQ + QLoRA fine-tuned DeepSeek-R1-32B that achieves 84% on MedQA in ~20GB, enabling on-prem healthcare deployment.
A user evaluated Hugging Face's ml-intern tool by requesting it to fine-tune SAM on a medical dataset and produce both a Jupyter notebook tutorial and a blog post.
Researchers from Fordham University introduce Reciprocal Co-Training (RCT), a framework that couples LLMs and Random Forest classifiers via reinforcement learning, creating an iterative feedback loop where each model improves using signals from the other. Experiments on three medical datasets show consistent performance gains for both models, demonstrating a general mechanism for integrating incompatible model families.
MEDSYN is a multilingual multimodal benchmark for evaluating MLLMs on complex clinical cases with up to 7 distinct visual evidence types per case. The study reveals that while frontier models match human experts on differential diagnosis generation, all MLLMs show significant gaps in final diagnosis selection due to poor synthesis of heterogeneous clinical evidence.
MedFocusLeak introduces the first transferable black-box adversarial attack on medical vision-language models, using imperceptible background perturbations to mislead clinical diagnoses across six imaging modalities.
MIT researchers propose a framework for 'humble' AI in healthcare that encourages systems to express uncertainty and act as collaborative co-pilots rather than authoritative oracles.
Researchers at the University of Pennsylvania are using AI models like DINO and SAM to automate and modernize medical triage in emergency response.
Hulu-Med is a transparent medical vision-language model that unifies understanding across text, 2D/3D images, and video, achieving state-of-the-art performance on 30 benchmarks while being fully open-source.
OpenAI introduces HealthBench, a new benchmark for evaluating AI systems in healthcare contexts, created with 262 physicians across 60 countries. The benchmark includes 5,000 realistic health conversations with physician-written rubrics to assess model performance on meaningful, trustworthy, and improvable metrics.
Color Health has developed an AI copilot using GPT-4o's reasoning capabilities to help oncologists identify missing diagnostic information and streamline cancer care workflows. The tool enables physicians to find 4x more missing labs and imaging results in ~5 minutes versus weeks, with initial validation underway at UCSF.
Lifespan health system used GPT-4 to simplify surgical consent forms from three pages to one page at a 6th grade reading level, improving patient understanding and physician adoption. The initiative, deployed in September 2023, has received positive feedback from both patients and clinicians.