Jul 2026· 2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)· pp. 514-520· 0 citations· 12 references
Abstract
Clinical deployment of large language models (LLMs) for medical question answering faces three primary challenges: (1) models frequently produce unsupported or hallucinated claims because their parametric knowledge is not verified against biomedical evidence; (2) single-pass inference prevents iterative self-correction, leading to incomplete differential diagnoses; and (3) poorly calibrated confidence scores undermine clinician trust. To address these challenges, we introduce a retrieval augmented multi-agent model whose components are specifically designed to generate medical answers through three stages: hypothesis generation, adversarial debate, and independent judgment, wherein evidence conditioned hypotheses are generated, contested by an adversarial process, and finally evaluated by an independent model. Each of these models utilizes a semantic retrieval system to access medical literature from PubMed, explanations of answer benchmarks from MedQA, and medical records from MIMIC III. The model is evaluated using the MedQA USMLE benchmark and a collection of prompts created from MIMIC III data, as well as by comparing the performance of the system against a general purpose LLM and a fine tuned medical LLM. Results indicate that the approach described improves the supported sentences rate, the precision and recall of the attributed sentences, and the groundedness of the answers provided while also reducing the number of unsupported claims, hallucinations, Brier score, and expected calibration error, while maintaining the same accuracy in medical diagnosis. Thus, these findings indicate that the incorporation of adversarial and verification models during inference can improve the model’s ability to align with the evidence and to calibrate uncertainty in its predictions without requiring any adaptation to the models with supervised training data.
This work introduces MedReaMM, a benchmark specifically designed to evaluate models'ability to synthesize heterogeneous clinical evidence consisting of detailed patient histories alongside multiple medical images into accurate differential diagnoses under a complete-information paradigm.
Lai Wei, Yu-Chao Chen, Zhenbiao Cao et al.· 0 citations
By decoupling clinical information retrieval from generative chitchat, LENOHA enhances safety, preserves privacy, and markedly reduces energy use, offering a practical blueprint for sustainable and equitable medical AI deployment across diverse care settings.
Motoki Sato, Sou Nagata, Mizuho Ohnuma et al.· JMIR Medical Informatics· 0 citations
Large Language Models (LLMs) show strong potential for clinical reasoning, yet their deployment in medical decision support is hindered by hallucinations, overconfidence, and limited transparency. We propose Dialectic Diagnosis, an agentic framework in which two heterogeneous LLM agents engage in structured argumentative interaction inspired by clinical second-opinion workflows. A Clinical Reasoner proposes candidate diagnoses, while a Skeptical Critic challenges these hypotheses by identifying omissions, cognitive biases, and unsupported reasoning. Their interaction is governed by a formal finite-state machine (FSM) enforcing a disciplined proposecritique-resolve protocol, with final decisions produced by an Arbiter agent providing calibrated confidence estimates. To ensure transparency, we introduce a Diagnostic Argument Graph that explicitly represents supporting evidence, contradictions, and missing diagnoses. Evaluations on real-world clinical datasets (MIMIC-IV and eICU) show clear gains over single-agent LLM baselines, with improved diagnostic accuracy, lower calibration error, and fewer critical diagnostic omissions. These results indicate that structured argumentative interaction between LLM agents provides a principled path toward safer and more explainable clinical AI systems.
Belkacem Chikhaoui· Annual International Compute...· 0 citations
Developing artificial intelligence capable of clinical language comprehension and reliable diagnostic reasoning has remained a core challenge in biomedical engineering. While Large Language Models (LLMs) demonstrate significant potential in general natural language processing tasks, their direct application in the medical domain is severely constrained by parametric hallucinations and data silos. This paper introduces an end-to-end, resource-efficient, multilingual speech-driven Question-Answering (QA) framework optimized for localized clinical support. To accommodate deployment on consumer-grade execution environments, we implement Parameter-Efficient Fine-Tuning (PEFT) using Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) configurations across open-source 3B and 7B parameter architectures. Human preference alignment is enforced via a stateful Reinforcement Learning with Human Feedback (RLHF) loop applying Proximal Policy Optimization (PPO). Crucially, to mitigate the vulnerabilities of passive information retrieval, we introduce an Active Validation Loop powered by Corrective Retrieval-Augmented Generation (CRAG). This validation engine is decoupled from the model harness using the Model Context Protocol (MCP), standardizing asynchronous lookups across dense vector repositories, clinical guidelines, and real-time electronic health registries.
Misha Patel· International Journal of Sci...· 0 citations
A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.
A benchmark evaluation framework for measuring the faithfulness of LLM-generated clinical trial summaries across three stakeholder audiences is introduced and a knowledge-graph-augmented retrieval system was developed and evaluated, producing statistically significant improvements in NLI-based faithfulness scores.