Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similarity measures behind such claims are fragile. We present a controlled dissection across 18 image and 7 text encoders, all open-weight and run locally, spanning 7M to 27B parameters and five imaging modalities, including 650,982 chest radiographs from six datasets. To isolate cause, we train encoders that vary only the objective under fixed data, architecture, and scale, and reproduce the effect in a synthetic model. Convergence is modest but above a random floor, driven by the self-supervised objective, not clinical supervision: matched self-supervised encoders aligned most (40.4% on chest radiography), with label-supervised (21.1%) and image-text (3.3%) far lower, and did not grow with size (Spearman 0.302, p=0.223) or capability. It is within-modality, does not reach clinical language, and does not reproduce how radiologists judge case similarity. Yet a linear classifier transfers across encoders and to five held-out hospitals, retaining about 85% of within-encoder performance. Convergence in medical imaging is therefore set by the pretraining objective, not inherited from scale or clinical supervision. Interoperability is accordingly something to design for through that objective, and to validate where the shared geometry is weakest, across patient subgroups and against clinical judgment.
Soroosh Tayebi Arasteh, S. Ziegelmayer, Mahshad Lotfinia et al.· arXiv.org· 0 citations
Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases. Here we show that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier (eight thoracic findings, 137,593 training images), provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions. Adding this signal to the point prediction raised error-detection AUROC from 0.74 to 0.77 ($\Delta$AUROC +0.023, 95% CI [+0.014, +0.033]). In a controlled 2x2 factorial experiment, a clinical-decision-support agent exploited this uncertainty only when it was delivered as a binary error-risk flag rather than as raw scores, cutting confident misdiagnoses on unreliable findings from 8.5% to 2.7%. Epistemic uncertainty estimation thus carries decision-relevant information beyond point predictions, but its value for downstream agents depends on how it is communicated.
Frederik Hauke, P. Wienholt, Christiane Kuhl et al.· 0 citations
The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely affected. Here we pair Evo~2-based genome analysis with routine clinical imaging to identify gene--phenotype associations at genome-wide scale. For every somatic mutation across three TCGA cohorts (cRCC=clear cell renal cell carcinoma, HCC=hepatocellular carcinoma, and BC=breast cancer; $n = 340$ total), Evo~2 predicts a severity score, with no task-specific training. Per-gene severity summaries are then correlated with radiomic features extracted from paired tumor segmentations, controlling for total mutation burden. In TCGA-cRCC ($n = 162$), this sweep recovers established renal-cancer drivers and identifies 46 additional genes reaching false discovery rate (FDR) significance absent from curated cancer-gene panels, several of which are Mendelian ciliopathy and cytoskeletal-disease genes. These results demonstrate that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.
Frederik Hauke, Jeremias Krause, P. Wienholt et al.· arXiv.org· 0 citations
These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk, and show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk.
T. Chanda, C. Wies, Franziska Schramm et al.· 0 citations
Abstract Background Maintenance of oncology clinical practice guidelines (CPGs) is increasingly challenged by the rapid growth of trial data and therapeutic complexity. While large language models (LLMs) have shown promise in information retrieval, their utility in the rigorous, end-to-end workflow of guideline maintenance remains underexplored. Objective This case study aimed to systematically evaluate the performance of frontier LLMs in supporting oncology guideline maintenance. We sought to determine their reliability in predicting necessary guideline updates based on new evidence, their accuracy in extracting data from clinical trials, and their effectiveness as automated auditors for detecting errors in established guidelines. Methods Using the Onkopedia peripheral T-cell lymphoma (PTCL) guideline as a prospective case study, we tasked frontier models with deep-research modes and autonomous web-search capabilities (Gemini 2.5 Pro and GPT o4-mini-high) to predict a guideline update in August 2025 based on the 2021 version. Predictions were validated against the official 2025 revision published in October 2025. Next, we benchmarked evidence extraction accuracy across 80 pivotal trials using models of varying scale (27B-671B parameters vs frontier). Finally, we deployed a stacked LLM workflow to audit 28 recently updated Onkopedia guidelines for linguistic and content-related errors. Results In the predictive task, models captured 36.7% to 40% of substantive updates, often identifying landmark approvals, but frequently overstating evidence. An independent, model-blinded rescoring yielded substantial agreement (weighted Cohen κ=0.75) and confirmed predictive accuracies of 35% to 38.3%. While frontier models demonstrated high accuracy (up to 99.2%) in extracting data from individual studies, substantially outperforming smaller open-source models, this precision declined during multisource synthesis. We observed a position-dependent performance drop in long-form generation, with GPT’s endpoint accuracy dropping from 84.6% in the first half of the drafted guideline to 38.5% in the second half. As automated auditors of existing CPGs, the models successfully identified a median of 16.5 (IQR 13.8-20.3) formal errors per document and detected several clinically relevant inconsistencies (eg, invalid scoring formulas and incorrect staging definitions). Conclusions LLMs currently lack the reasoning stability for autonomous guideline authoring due to deficits in complex synthesis. However, they are effective tools for high-fidelity evidence extraction and automated quality assurance, supporting a human-led, AI-augmented workflow for efficient guideline maintenance.
M. Knauer, Julian Greß, J. Kather et al.· JMIR AI· 0 citations
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