The Locally Deployable Virtual Doctor: LLM Based Human Interface for Automated Anamnesis and Database Conversion

• URL: http://arxiv.org/abs/2511.18632v1
• Date: Sun, 23 Nov 2025 22:12:35 GMT
• Title: The Locally Deployable Virtual Doctor: LLM Based Human Interface for Automated Anamnesis and Database Conversion
• Authors: Jan Benedikt Ruhland, Doguhan Bahcivan, Jan-Peter Sowa, Ali Canbay, Dominik Heider,
• Abstract summary: MedChat is a locally deployable virtual physician framework for AI-assisted clinical anamnesis.<n>Unlike existing cloud-based systems, this work demonstrates the feasibility of a fully offline, locally deployable LLM-diffusion framework for clinical anamnesis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in large language models made it possible to achieve high conversational performance with substantially reduced computational demands, enabling practical on-site deployment in clinical environments. Such progress allows for local integration of AI systems that uphold strict data protection and patient privacy requirements, yet their secure implementation in medicine necessitates careful consideration of ethical, regulatory, and technical constraints. In this study, we introduce MedChat, a locally deployable virtual physician framework that integrates an LLM-based medical chatbot with a diffusion-driven avatar for automated and structured anamnesis. The chatbot was fine-tuned using a hybrid corpus of real and synthetically generated medical dialogues, while model efficiency was optimized via Low-Rank Adaptation. A secure and isolated database interface was implemented to ensure complete separation between patient data and the inference process. The avatar component was realized through a conditional diffusion model operating in latent space, trained on researcher video datasets and synchronized with mel-frequency audio features for realistic speech and facial animation. Unlike existing cloud-based systems, this work demonstrates the feasibility of a fully offline, locally deployable LLM-diffusion framework for clinical anamnesis. The autoencoder and diffusion networks exhibited smooth convergence, and MedChat achieved stable fine-tuning with strong generalization to unseen data. The proposed system thus provides a privacy-preserving, resource-efficient foundation for AI-assisted clinical anamnesis, also in low-cost settings.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia [1.299941371793082]
  A diffusion-based neural decoding framework is optimized for real-time imagined speech classification in individuals with aphasia.<n>A dual-criterion early stopping strategy enabled rapid convergence under limited calibration data.<n>The proposed framework advances the translation of imagined speech brain-computer interfaces toward clinical communication support.
  arXiv  Detail & Related papers  (2025-11-11T07:18:35Z)
• A Hybrid Machine Learning Approach for Synthetic Data Generation with Post Hoc Calibration for Clinical Tabular Datasets [0.9940728137241215]
  Healthcare research and development face significant obstacles due to data scarcity and stringent privacy regulations.<n>We produce artificial datasets that emulate real data statistics while safeguarding patient privacy.<n>This scalable, privacy-preserving approach matches state-of-the-art methods, sets new benchmarks for joint-distribution fidelity in healthcare.
  arXiv  Detail & Related papers  (2025-10-12T09:23:43Z)
• A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms [0.03262230127283451]
  We introduce a model driven engineering (MDE) framework designed specifically for healthcare AI.<n>The framework relies on formal metamodels, domain-specific languages, and automated transformations to move from high level specifications to running software.<n>We evaluate this approach in a multi center cancer immunotherapy study.
  arXiv  Detail & Related papers  (2025-10-10T12:00:12Z)
• Patient-Zero: A Unified Framework for Real-Record-Free Patient Agent Generation [11.75912414451272]
  We propose a realistic patient generation framework, Patient-Zero, which requires no real medical records.<n>Patient-Zero first introduces a medically-aligned multi-step generation architecture, which builds comprehensive patient records through hierarchical medical knowledge injection without real medical records.<n>Our framework enables the generation of contextually diverse patient records while maintaining strict medical coherence, supported by adaptive dialogue strategies and real-time clinical plausibility verification.
  arXiv  Detail & Related papers  (2025-09-14T03:56:00Z)
• NEARL-CLIP: Interacted Query Adaptation with Orthogonal Regularization for Medical Vision-Language Understanding [51.63264715941068]
  textbfNEARL-CLIP (iunderlineNteracted quunderlineEry underlineAdaptation with ounderlineRthogonaunderlineL Regularization) is a novel cross-modality interaction VLM-based framework.
  arXiv  Detail & Related papers  (2025-08-06T05:44:01Z)
• MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving [85.04826012938642]
  MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
  arXiv  Detail & Related papers  (2025-08-04T03:07:54Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)

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.