Large Language Model's Multi-Capability Alignment in Biomedical Domain

• URL: http://arxiv.org/abs/2508.04278v1
• Date: Wed, 06 Aug 2025 10:06:11 GMT
• Title: Large Language Model's Multi-Capability Alignment in Biomedical Domain
• Abstract summary: BalancedBio is a framework for parameter-efficient biomedical reasoning.<n>It addresses multi-capability integration in domain-specific AI alignment.<n>It achieves state-of-the-art results in its parameter class.<n>Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance.
• Score: 3.1427813443719868
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: BalancedBio is a theoretically grounded framework for parameter-efficient biomedical reasoning, addressing multi-capability integration in domain-specific AI alignment. It establishes the Biomedical Multi-Capability Convergence Theorem, proving orthogonal gradient spaces are essential to prevent capability interference for safe deployment. Key innovations include: (1) Medical Knowledge Grounded Synthetic Generation (MKGSG), extending Source2Synth with clinical workflow constraints and medical ontology validation for factual accuracy and safety; and (2) Capability Aware Group Relative Policy Optimization, deriving optimal hybrid reward weighting to maintain orthogonality in RL, using a reward model with rule-based and model-based scores adapted to biomedical tasks. Mathematical analysis proves Pareto-optimal convergence, preserving performance across capabilities. It achieves state-of-the-art results in its parameter class: domain expertise (80.95% BIOMED-MMLU, +15.32% over baseline), reasoning (61.94%, +7.75%), instruction following (67.95%, +6.44%), and integration (86.7%, +18.5%). Theoretical safety guarantees include bounds on capability preservation and clinical accuracy. Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance. This work provides a principled methodology for biomedical AI alignment, enabling efficient reasoning with essential safety and reliability, with the 0.5B model version to be released.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• An Early Warning of Emerging Biosecurity Risks in Frontier LLMs [62.01705276160883]
  We develop Intern-BioBreaker, a specialized bio-red-teaming model, together with an integrated computational-to-physical framework.<n>Within this framework, Intern-BioBreaker generates targeted jailbreak prompts to test whether aligned models can be induced to provide operational guidance.<n>Our evaluation reveals a concerning gap between text-level safeguards and the risks posed by capable scientific models.
  arXiv  Detail & Related papers  (2026-07-20T15:26:23Z)
• Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment [56.62016795093786]
  Cerebra is an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.<n>It supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete.<n>Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation.
  arXiv  Detail & Related papers  (2026-03-23T05:46:45Z)
• MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization [46.65200216642429]
  We introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs.<n>Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10%.
  arXiv  Detail & Related papers  (2026-02-01T07:56:10Z)
• CytoDINO: Risk-Aware and Biologically-Informed Adaptation of DINOv3 for Bone Marrow Cytomorphology [0.0]
  We introduce CytoDINO, a framework that achieves state-of-the-art performance on the Munich Leukemia Laboratory dataset.<n>Our primary contribution is a novel Hierarchical Focal Loss with Critical Penalties, which encodes biological relationships between cell lineages and explicitly penalizes clinically dangerous misclassifications.<n>CytoDINO achieves an 88.2% weighted F1 score and 76.5% macro F1 on a held-out test set of 21 cell classes.
  arXiv  Detail & Related papers  (2025-12-09T23:09:22Z)
• MedHE: Communication-Efficient Privacy-Preserving Federated Learning with Adaptive Gradient Sparsification for Healthcare [0.0]
  This paper presents MedHE, a novel framework combining adaptive gradient sparsification with CKKS homomorphic encryption to enable privacy-preserving collaborative learning on sensitive medical data.<n>Our approach introduces a dynamic threshold mechanism with error compensation for top-k gradient selection, achieving 97.5 percent communication reduction while preserving model utility.
  arXiv  Detail & Related papers  (2025-11-12T06:50:48Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights [54.87947751720332]
  Accurate brain tumor segmentation is significant for clinical diagnosis and treatment.<n>Mamba-based State Space Models have demonstrated promising performance.<n>We propose a dual-resolution bi-directional Mamba that captures multi-scale long-range dependencies with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-10-16T07:31:21Z)
• Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics [26.40159044419644]
  We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions.<n>Our framework combines Chaos Game Representation with a Concept Bottleneck Model (CBM), enforcing predictions to flow through biologically meaningful concepts.<n>By bridging the gap between interpretable genomic modeling and automated decision-making, this work establishes a reliable foundation for robotic and clinical automation in genomic medicine.
  arXiv  Detail & Related papers  (2025-10-02T02:51:34Z)
• Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection [0.0]
  Clinical machine learning faces a critical dilemma in high-stakes medical applications.<n>A paradox becomes particularly acute in non-invasive prenatal testing (NIPT), where missed chromosomal abnormalities carry profound clinical consequences.<n>We introduce Medical Priority Fusion (MPF), a constrained multi-objective optimization framework that resolves this fundamental trade-off.
  arXiv  Detail & Related papers  (2025-09-22T15:49:20Z)
• Addressing High Class Imbalance in Multi-Class Diabetic Retinopathy Severity Grading with Augmentation and Transfer Learning [1.5939351525664014]
  This paper presents a robust deep learning framework for both binary and five-class Diabetic retinopathy (DR) classification.<n>For binary classification, our proposed model achieves a state-of-the-art accuracy of 98.9%, with a precision of 98.6%, recall of 99.3%, F1-score of 98.9%, and an AUC of 99.4%.<n>In the more challenging five-class severity classification task, our model obtains a competitive accuracy of 84.6% and an AUC of 94.1%, outperforming several existing approaches.
  arXiv  Detail & Related papers  (2025-07-23T01:52:27Z)
• An Explainable AI-Enhanced Machine Learning Approach for Cardiovascular Disease Detection and Risk Assessment [0.0]
  Heart disease remains a major global health concern.<n>Traditional diagnostic methods fail to accurately identify and manage heart disease risks.<n>Machine learning has the potential to significantly enhance the accuracy, efficiency, and speed of heart disease diagnosis.
  arXiv  Detail & Related papers  (2025-07-15T10:38:38Z)
• Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration [17.11245701879749]
  Generalist Medical AI (GMAI) systems have demonstrated expert-level performance in biomedical perception tasks.<n>Here, we present XMedGPT, a clinician-centric, multi-modal AI assistant that integrates textual and visual interpretability.<n>We validate XMedGPT across four pillars: multi-modal interpretability, uncertainty quantification, and prognostic modeling, and rigorous benchmarking.
  arXiv  Detail & Related papers  (2025-05-11T08:32:01Z)
• Toward Automated Regulatory Decision-Making: Trustworthy Medical Device Risk Classification with Multimodal Transformers and Self-Training [3.439579933384111]
  Transformer-based framework integrates textual descriptions and visual information to predict device regulatory classification.<n>Our approach achieves up to 90.4% accuracy and 97.9% AUROC, significantly outperforming text-only (77.2%) and image-only (54.8%) baselines.
  arXiv  Detail & Related papers  (2025-05-01T09:41:41Z)
• Stabilizing Reasoning in Medical LLMs with Continued Pretraining and Reasoning Preference Optimization [0.06554326244334867]
  This paper introduces Preferred-MedLLM-Qwen-72B, a 72B- parameter model optimized for the Japanese medical domain.<n>We employ a two-stage fine-tuning process on the Qwen2.5-72B base model to achieve both high accuracy and stable reasoning.
  arXiv  Detail & Related papers  (2025-04-25T05:15:31Z)
• MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts [54.915060471994686]
  We propose MAST-Pro, a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation.<n>Specifically, text and anatomical prompts provide domain-specific priors guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning.<n>Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average improvement while reducing trainable parameters by 91.04%, without compromising accuracy.
  arXiv  Detail & Related papers  (2025-03-18T15:39:44Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Simulated patient systems are intelligent when powered by large language model-based AI agents [32.73072809937573]
  We developed AIPatient, an intelligent simulated patient system powered by large language model-based AI agents.<n>The system incorporates the Retrieval Augmented Generation framework, powered by six task-specific LLM-based AI agents for complex reasoning.<n>For simulation reality, the system is also powered by the AIPatient KG (Knowledge Graph), built with de-identified real patient data.
  arXiv  Detail & Related papers  (2024-09-27T17:17:15Z)
• BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers [48.21255861863282]
  BMRetriever is a series of dense retrievers for enhancing biomedical retrieval. BMRetriever exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger.
  arXiv  Detail & Related papers  (2024-04-29T05:40:08Z)
• BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion [16.83901927767791]
  We present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-16T14:19:33Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.