arXiv AI June 17, 2026 By Ruman Wang, Hangting Ye

When LLMs Analyze Scars: From Images to Clinically-Meaningful Features

arXiv:2606. 18063v1 Announce Type: cross Abstract: Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity.

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

arXiv Machine Learning

Jul 31

ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs

arXiv:2607. 28538v1 Announce Type: cross Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals.

By Ruman Wang, Hangting Ye

llms computer-vision multimodal

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv AI

Jun 10

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.

By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu

llms benchmarks safety

arXiv AI

Jul 1

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.

By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami

computer-vision

arXiv AI

Jun 16

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.

By Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li

llms benchmarks

arXiv Machine Learning

Jul 13

Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling

arXiv:2607. 08867v1 Announce Type: cross Abstract: Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development.

By Jason Rojas, Jiajie He, Yash Patel, Yuechun Gu, Zeyun Yu, Keke Chen

computer-vision safety

Related stories

ScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling