Large Language Models as Unified Multimodal Learners for Clinical Prediction
This study proposes converting all multimodal patient data (structured measurements and free-text clinical narratives) from electronic health records into a single natural language sequence, fine-tuning a pretrained language model end-to-end without specialized fusion architectures. Evaluated on three clinical prediction tasks (in-hospital mortality, graft failure, and emergency triage), the unified serialization approach matches or exceeds task-specific multimodal baselines and outperforms a clinically deployed gradient boosting model, significantly reducing system complexity.
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[Submitted on 16 Jul 2026]
Title:Large Language Models as Unified Multimodal Learners for Clinical Prediction
View a PDF of the paper titled Large Language Models as Unified Multimodal Learners for Clinical Prediction, by Ajay Madhavan Ravichandran and 7 other authors
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Abstract:Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities. Yet most clinical prediction systems still rely on task-specific fusion architectures, pairing dedicated encoders for each modality with learned combination mechanisms that must be re-engineered for every new task and clinical setting. We propose a simpler alternative: convert all patient data, regardless of modality, into a single natural language sequence and fine-tune a pretrained language model end-to-end, with no architectural modification for fusion. We evaluate this approach across three clinically distinct prediction tasks: in-hospital mortality on MIMIC-III, graft failure prediction using longitudinal data from a German transplant center, and emergency triage classification from ambulance records - comparing encoder-based (ModernBERT) and decoder-based (Llama 3.1, Gemma, DeepSeek-R1-Qwen, Qwen3) fine-tuning against established multimodal baselines and, for graft failure, a gradient boosting model currently used in clinical practice for post-transplant patient management. Across all three tasks, unified textual serialization matches or exceeds task-specific multimodal baselines, and outperforms the clinically deployed gradient boosting system on graft failure prediction. These results indicate that a single serialization-based paradigm, without bespoke fusion architectures, is sufficient for multimodal clinical prediction - substantially reducing system complexity while matching or exceeding specialized designs.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.15380 [cs.CL]
(or arXiv:2607.15380v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.15380
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Roland Roller [view email] [v1] Thu, 16 Jul 2026 18:28:23 UTC (50 KB)
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