The paper compares the open‑source MedGemma-4b-it model, fine‑tuned with LoRA, to the proprietary GPT‑4 on a six‑disease medical image classification task. MedGemma achieved a mean test accuracy of 80.37%, outperforming GPT‑4’s 69.56%, and showed higher sensitivity for critical conditions such as cancer and pneumonia. Detailed confusion matrices and classification reports illustrate the performance differences across all disease categories.
arXiv:2608.22323v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus...
arXiv:2607. 25933v1 Announce Type: cross Abstract: Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practice, including progressive disclosure of multimodal information, dynamic updating of diagnostic hypotheses, and continuous refinement of clinical reasoning.
arXiv:2606. 28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging.
arXiv:2510. 17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models.
arXiv:2506. 17337v5 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings.
arXiv:2603.22179v2 Announce Type: replace Abstract: Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Curr...
arXiv:2610.35704v1 Announce Type: new Abstract: Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, parti...
The paper introduces MedUAG, a unified medical multimodal model that supports both understanding and generation tasks. It presents MedUAGCorpus, the largest dataset of over 6 million instances across 14 imaging modalities, and MedUAGBench, a benchmark covering 12 diverse generation tasks with standardized protocols. Experiments show that MedUAG performs strongly across many medical understanding and generation tasks, setting a competitive baseline for future medical multimodal systems.
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Yet pathological asses...
LUCAID is an agentic multimodal AI system designed for precision lung cancer pathology, integrating nine modules that cover the entire routine workflow—from quality control and tumor detection to histological subtyping, microenvironment profiling, cellularity quantification, and biomarker scoring (PD‑L1, MET, TROP‑2). The system generates automated structured reports and allows interactive querying of module outputs. In prospective clinical validation, LUCAID achieved 93.0% concordance with an expert‑panel reference standard for clinically actionable decisions, outperforming five experienced thoracic pathologists who ranged from 68.3% to 81.1% concordance.
Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, particularly for predicting biopsy outcomes remains li...
The paper introduces the Cross‑Modal Triage Network (CMTN), a multimodal deep‑learning model that fuses a Swin Transformer V2 visual encoder with a PubMedBERT text encoder to perform severity‑based triage, pathology detection, and generate visual explanations for chest radiographs. Trained on 34,639 image‑text pairs from MIMIC‑CXR‑JPG, the CMTN achieves high ordinal agreement with reference labels (QWK = 0.9341) and excellent pathology detection (macro‑AUROC = 0.9970) while operating with 34 ms latency. However, a blinded clinical audit revealed low agreement with expert radiologists (QWK = 0.1399) and only modest spatial‑semantic concordance in heatmaps, underscoring the gap between algorithmic performance and clinical judgment.