MediMate AI: An AI Assistant for General Practitioners

Authors

  • Jan Saro Department of Systems Engineering, Faculty of Economics and Management CZU Prague, Czech University of Life Sciences Prague
  • Jana Mazancová Department of Sustainable Technologies, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
  • Helena Brožová Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic

DOI:

https://doi.org/10.34190/icair.5.1.4333

Keywords:

Artificial Intelligence, AI healthcare, Adaptive Language Models, Support application

Abstract

Primary care remains one of the most demanding areas of medicine, with general practitioners (GPs) facing rising consultation volumes, complex patient presentations, and increasing administrative tasks. These pressures contribute to cognitive overload, diagnostic delays, and burnout. Large language models (LLMs), such as GPT-4, show potential to reduce documentation workload, enhance diagnostic reasoning, and improve overall workflows. However, their integration must be carefully managed to ensure data protection, patient safety, compliance with clinical guidelines (e.g., WHO, CDC), and ethical standards. This paper introduces MediMate AI, a GPT-4–powered prototype assistant designed to support GPs in real time. Implemented as a web-based, mobile-accessible platform, the system integrates multimodal inputs—including speech, text, and images—to transcribe consultations, extract key symptoms, and generate structured summaries. Beyond documentation, MediMate AI provides differential diagnoses with confirmatory test recommendations, evaluates geographic epidemiological risks, and produces tailored hospital routing plans. The prototype was tested in a digital innovation incubator using synthetic patient records and simulated consultations. This approach enabled safe experimentation without breaching patient confidentiality while providing early insights into feasibility, usability, and workflow integration. By combining transcripts, symptom extraction, dermatology image data, and standardized checklists, the prototype reflects the heterogeneous nature of real-world primary care. Results indicate that MediMate AI can reduce documentation workload by an estimated 25–30%, deliver clinically coherent summaries, and generate guideline-aligned differential diagnoses with improved clarity for decision-making. Physicians testing the prototype highlighted its ability to consolidate fragmented data streams, improve continuity of care, and enhance patient–clinician communication. While not intended to replace medical expertise, MediMate AI demonstrates the promise of generative AI to augment decision-making, improve efficiency, and support more patient-centred care. Future work will include prospective clinical validation, integration with electronic health record systems, and the introduction of explainability and bias detection modules. Addressing these aspects will be essential to ensure safe, ethical, and sustainable deployment in healthcare environments.

Author Biographies

Jan Saro, Department of Systems Engineering, Faculty of Economics and Management CZU Prague, Czech University of Life Sciences Prague

Jan Saro holds an MSc in Open Informatics from the Czech Technical University in Prague and is a PhD candidate at the Czech University of Life Sciences Prague. His research focuses on applied artificial intelligence across multiple scientific domains, including agriculture, healthcare, and sustainability, with emphasis on AI-driven decision-support systems.

Jana Mazancová, Department of Sustainable Technologies, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic

Jana Mazancová is Vice-Dean for Development and a researcher at the Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague. Her research focuses on sustainable rural development, women’s empowerment, and adoption of green technologies in Sub-Saharan Africa and Southeast Asia. She has co-authored over 25 WoS-indexed papers.

Helena Brožová, Department of Systems Engineering, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic

Helena Brožová is a Professor at the Faculty of Economics and Management, Czech University of Life Sciences Prague. Her research focuses on operations research, decision analysis, and optimization in agriculture and education.

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Published

2025-12-04