Aloe-Vision: Robust Vision-Language Models for Healthcare
Aloe-Vision introduces a family of open-source medical vision-language models trained on a large-scale quality-filtered dataset, achieving balanced performance and exposing vulnerabilities to adversarial inputs.
[2606.27500] Aloe-Vision: Robust Vision-Language Models for Healthcare
[Submitted on 25 Jun 2026]
Title:Aloe-Vision: Robust Vision-Language Models for Healthcare
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Abstract:Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinical and biomedical applications. However, progress is constrained by the scarcity of high-quality medical multimodal data, concerns about robustness in safety-critical settings, and the narrow and potentially contaminated evaluation benchmarks that limit reliable assessment. To address these issues, the field requires state-of-the-art solutions to be fully open and reproducible systems in which all components can be inspected, evaluated, and improved. This work introduces Aloe-Vision-Data, a large-scale, quality-filtered mixture which integrates both medical and general domains across multimodal and text-only sources, designed for direct use in model fine-tuning. Building on this dataset, we train the Aloe-Vision family of medical LVLMs, openly released with full weights, training recipes and data, in two scales (7B and 72B). Through comprehensive benchmarking, we demonstrate that high quality training mixtures produce balanced LVLMs which yield significant gains over the baseline models without compromising general capabilities, achieving competitive performance with respect to state-of-the-art alternatives. To support reliable evaluation, we introduce CareQA-Vision, a carefully curated vision benchmark derived from MIR and EIR exams, the residency entrance exams for medical and nursing specialists in Spain, offering novel vision questions with low likelihood of contamination. Finally, we show that current LVLMs remain vulnerable to adversarial and misleading inputs, underscoring reliability challenges in clinical contexts.
Comments: MIDL 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
Cite as: arXiv:2606.27500 [cs.CV]
(or arXiv:2606.27500v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.27500
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Proceedings of Machine Learning Research, Vol. 315, pp. 2404-2426, 2026
Submission history
From: Jaume Guasch-Martí [view email] [v1] Thu, 25 Jun 2026 19:36:38 UTC (7,959 KB)
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