arXiv AI June 8, 2026 By Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra, Sandipan Sikdar, Koustav Rudra

REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference

arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.

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

arXiv AI

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FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

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arXiv Machine Learning

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arXiv AI

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arXiv Machine Learning

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One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

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computer-vision benchmarks safety

arXiv Machine Learning

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Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning

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arXiv AI

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FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

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EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses

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