#medical-ai

23 curated events

papersTODAY 04:00 UTC

Echo-CoPilot: Agentic Framework for Multi-View Echocardiography Interpretation

Researchers present Echo-CoPilot, a multi-perspective agentic framework designed to interpret echocardiography by combining temporal evidence from multiple views with quantitative measurements and guideline-based reasoning. The work targets a gap in existing foundation-model pipelines, which the authors say handle isolated subtasks and break down when tool outputs are incomplete or inconsistent. The paper is listed as an updated submission on arXiv (2512.09944v4).

agents/medical-ai/multimodal/research

papersTODAY 04:00 UTC

GRIN+ Method Targets Machine Unlearning in Imbalanced Medical Data

A new arXiv preprint introduces GRIN+, a machine unlearning approach aimed at removing patient data from trained medical models both quickly and effectively. The work focuses on imbalanced clinical datasets, a setting where existing unlearning methods tend to degrade model performance. It frames the problem around privacy rules such as GDPR and HIPAA that grant patients the right to have their data erased.

machine-unlearning/medical-ai/privacy/gdpr

papersTODAY 04:00 UTC

MARCUS: Agentic Multimodal Vision-Language Model for Cardiac Diagnosis

Researchers present MARCUS, a vision-language system designed to assist with cardiac diagnosis and management. Unlike earlier medical AI models that handle only one type of input and cannot interact with users, it is described as agentic and multimodal. The work targets cardiovascular disease, the leading cause of death worldwide, where interpreting complex cardiac tests remains a bottleneck.

agents/multimodal/medical-ai/vision-language

papersTODAY 04:00 UTC

Image-swap audit tests whether medical vision-language models rely on scans

A new arXiv paper introduces ModaLens, an audit method that swaps paired images to check how much a medical vision-language model actually depends on the image versus the accompanying radiology report. Because a report may already contain the answer to a clinical question, the technique measures how image sensitivity shifts when report text is available. The authors report results for MedGemma-27B among other models.

medical-ai/vision-language/evaluation/arxiv

papersTODAY 04:00 UTC

arXiv Paper Revisits Correctness Measures for Uncertainty Estimation in Clinical VLMs

A new preprint examines how correctness is defined when vision-language models are used to make clinical predictions from medical images and electronic health records. The authors argue that current uncertainty estimation methods may be evaluated in ways that do not reflect whether a prediction is actually reliable. The work targets safer deployment by improving how unreliable outputs are detected.

uncertainty-estimation/medical-ai/vision-language/research

papersTODAY 04:00 UTC

Study Probes How Misleading Context Skews Medical Question Answering

A new arXiv paper investigates why large language models can give wrong medical answers when the context they receive is misleading, even though their standalone medical accuracy is high. The authors analyze the internal mechanisms behind this susceptibility, aiming to explain how flawed context overrides a model's medical knowledge. The work is a revised cross-listing on arXiv and falls under AI and machine learning research.

medical-ai/llm-safety/research/context

papersTODAY 04:00 UTC

arXiv study explores using LLMs to simplify medical information for diabetes patients

A new arXiv paper examines how large language models can be used to make complex medical information easier for patients to understand, using diabetes as a case study. The authors argue that clearer simplification supports patient comprehension, informed decision-making, and better health outcomes. The work focuses on the challenges of translating clinical knowledge into patient-friendly language.

medical-ai/llm/diabetes/patient-education

papersTODAY 04:00 UTC

Graph-Guided Mixture of Experts Model Predicts Tumor Survival from Multi-Modal Data

A new arXiv preprint introduces a graph-guided mixture-of-experts approach for predicting tumor survival outcomes using multiple data modalities. The method aims to combine heterogeneous clinical inputs within a single predictive framework. The paper appears in the cs.LG category as a new submission.

medical-ai/multimodal/mixture-of-experts/arxiv

papersTODAY 04:00 UTC

arXiv Paper Proposes Graph-Based End-to-End Cell Detection for Pathology

A new arXiv preprint introduces an instance-aware graph modeling approach for detecting and classifying cells in pathology images. The method aims to capture complex cellular interactions within the tumor microenvironment rather than relying only on visual appearance. Accurate cell detection matters for diagnostic accuracy and treatment planning.

research/medical-ai/computer-vision/graph-neural-networks

papersTODAY 04:00 UTC

Edge AI Medical Device System Tested for Breast Cancer Team Meetings

Researchers present a system that runs AI models locally on edge hardware as a regulated medical device, aimed at supporting breast cancer multidisciplinary team meetings. The paper reports a feasibility evaluation, noting that existing AI-supported workflows for these meetings depend on cloud infrastructure. The work targets reducing documentation burden and time pressure in complex case reviews.

medical-ai/edge-ai/healthcare/research

papersTODAY 04:00 UTC

Closed-Form Occlusal Geometry Proposed for Orthodontic Report Generation

This arXiv paper notes that intraoral scan datasets such as Bite2Text arrive already aligned in occlusion, which means key occlusal measurements can be calculated directly rather than inferred by a learned captioning model. The authors argue for deriving these quantities in closed form as a basis for automatically generating orthodontic reports. The work falls under computation and language research and has not been peer reviewed.

research/medical-ai/multimodal/arxiv

papersTODAY 04:00 UTC

Paper Details Option-Aware Retrieval and VLM Tuning for Offline Medical VQA

An arXiv paper describes a submission to the MedReason 2026 challenge that handles both multiple-choice and open-ended medical visual question answering with fully offline, containerized inference. The authors report that retrieval for multiple-choice questions needs to compare candidate options in a specific way, and they also adapt a vision-language model to the clinical task. The work is listed under both cs.AI and cs.CL.

medical-ai/vlm/retrieval/benchmark

papersTODAY 04:00 UTC

Paper Proposes Inverse Constitutional Fine-Tuning for Radiology Report Style

A new arXiv preprint examines how automatically generated radiology reports often differ from real radiologist writing in structure, word choice, and expressions of uncertainty. The authors propose characterizing report corpora and using inverse constitutional fine-tuning to make generated text better match authentic clinical style.

radiology/fine-tuning/medical-ai/text-generation

papersTODAY 04:00 UTC

EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse

Researchers propose EMR, a medical multi-agent framework built on large language models that improves over time by mining and reusing experience from earlier cases. The work targets a common limitation of clinical AI agents, which typically rely on fixed strategies and cannot retain a persistent memory of past diagnostic successes. By storing and reusing prior reasoning, the system aims to support self-evolution rather than static performance.

agents/medical-ai/llm/research

papersTODAY 04:00 UTC

FICAug: Clustering and Augmentation for Facial-Expression Parkinson's Screening

A new arXiv paper introduces FICAug, a method that combines feature-informed clustering with data augmentation to improve facial-expression-based screening for Parkinson's disease. The approach targets the problem of small clinical datasets, which limits how well such screening models generalize. It is presented as an updated preprint on arXiv (2409.17685v3) in the cs.AI and cs.LG categories.

parkinsons/medical-ai/data-augmentation/computer-vision

papersTODAY 04:00 UTC

Causal multi-modal AI model predicts chemotherapy sensitivity in breast cancer

A new arXiv preprint describes a causal, multi-modal AI approach for predicting which breast cancer patients will benefit from chemotherapy. The authors argue that current reliance on recurrence scores as a stand-in for treatment benefit may drive unnecessary chemotherapy use. Their method aims to give clinicians a more personalized estimate of chemosensitivity.

medical-ai/multi-modal/causal-inference/breast-cancer

papersSEP 10 04:00 UTC

Study proposes perturbation-sensitive selection for medical QA rationales

A new arXiv paper addresses the scarcity of high-quality rationales in medical question-answering datasets, where answer labels are plentiful but explanations are expensive to validate. The authors reframe the data acquisition problem as deciding which already-labeled questions warrant rationales, using a perturbation-sensitive selection criterion. The approach aims to improve QA robustness by targeting rationale annotation where it has the greatest effect.

medical-ai/datasets/question-answering/robustness

papersSEP 10 04:00 UTC

Study Quantifies Text Branch Sensitivity in Medical Vision-Language Segmentation

Researchers on arXiv examine whether clinical text inputs genuinely drive pixel-level predictions in pretrained vision-language models for medical image segmentation. They propose an evidence-decoupling approach to characterize the sensitivity of the text branch, aiming to clarify the real contribution of textual information to segmentation outputs.

medical-ai/vision-language/segmentation/interpretability

papersSEP 10 04:00 UTC

Reliability-Aware Hybrid-K Ensemble Selection Proposed for Cervical Cytology Classification

A new arXiv preprint introduces a hybrid ensemble selection framework for multiclass cervical cytology image classification that weighs discriminative performance alongside calibration and selective prediction. The authors argue that raw accuracy is not enough for clinical image analysis, and that the method is meant to deliver trustworthy confidence scores and uncertainty flags so users know when to distrust a prediction. The work appears in the cs.AI category as an early-stage research contribution.

medical-ai/computer-vision/calibration/uncertainty

papersSEP 10 04:00 UTC

Vision-language reinforcement learning enables scalable segmentation for clinical tasks

A new arXiv paper describes a vision-language reinforcement learning framework for on-demand analysis of medical images across a range of clinical applications. It focuses on delineating tumors and nearby organs at risk, a step central to radiotherapy planning, surgery, and treatment response assessment that currently demands significant expert time. The authors position the method as addressing the limitations of existing AI systems in this domain.

medical-ai/healthcare/segmentation/vision-language

papersSEP 12 04:00 UTC

Benchmark Tests Logical Consistency of LLMs in Risk-of-Bias Assessment

Researchers introduced LogiMed-RoB, a benchmark that evaluates whether large language models follow expert medical reasoning hierarchies when performing Cochrane-style risk-of-bias assessments, rather than just matching final labels. The work argues that existing evaluations of LLMs in evidence-based medicine reward superficial answers over genuine logical consistency. The benchmark targets hierarchical reasoning across the structured judgments required in systematic reviews.

benchmarks/medical-ai/reasoning/evaluation

papersSEP 11 04:00 UTC

Federated Learning Challenge Reports Results for Surgical Appendicitis Classification

A paper summarizes the FedSurg EndoVis 2024 Challenge, which tested federated learning methods on surgical video for appendicitis classification without centralizing patient data. The work addresses the difficulty of building generalizable surgical AI when hospitals cannot share video directly, and reports benchmark outcomes from participating teams. It positions federated training as a viable approach for privacy-sensitive, spatiotemporal surgical tasks.

federated-learning/medical-ai/benchmark/computer-vision

papersSEP 12 04:00 UTC

VeriSim framework tests medical AI robustness against noisy patient communication

Researchers released VeriSim, a patient simulation framework that introduces controllable, clinically grounded noise into medical AI evaluations. The tool aims to move testing beyond idealized patient cases toward more realistic communication conditions. The work is described in an arXiv paper (2604.10441v2).

medical-ai/evaluation/safety/benchmarks