Anindo I.R.Ul Islam Sajid, Md. AshiqMahmood M.S.Prottush N.Morol M.K.2026-08-062026-08-062025-01-01I. R. Anindo, M. A. U. Islam Sajid, M. S. Mahmood, N. Prottush and M. K. Morol, "A Dual-Mode LLM Framework for Medical and General Language Translation for Breaking Barriers in Healthcare Communication," 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS), Kushtia, Bangladesh, 2025, pp. 1-6, doi: 10.1109/COMPAS67506.2025.11381760.97983315552522-s2.0-105034642227https://hdl.handle.net/10361/28819Effective communication between healthcare providers and patients is frequently hindered by the complexity of medical language, contributing to misinterpretations and reduced health literacy. This study presents a robust, dual-mode translation framework powered by large language models (LLMs), combining neural architectures with rule-based safety layers to enable bidirectional translation between medical and general language. In addition to simplifying clinical language for patients, our system accurately reconstructs layperson descriptions into medically precise terms - empowering providers with clearer symptom narratives. We significantly enhance prior work by introducing domain-specific evaluation metrics (e.g., drug name preservation, dosage accuracy, ambiguity detection), conducting human-in-the-loop clinical validation, and benchmarking against specialized medical models such as MedPaLM, BioBERT, ClinicalBERT, and GPT-4 with medical prompting. Our curated dataset exceeds 60,000 annotated pairs across 15 specialties, ensuring generalizability across healthcare contexts. Clinical trials in outpatient settings demonstrate a 35% improvement in patient comprehension and high physician satisfaction (8.7/10), with zero safety incidents recorded. The system architecture supports real-world deployment via FHIR-compatible APIs and complies with regulatory frameworks such as HIPAA and FDA 510(k). These results indicate the model's potential to meaningfully bridge the communication gap in healthcare while setting a new standard for safe, transparent, and clinically grounded AI translation tools.6 Pagesen-USAI-assisted interactionBias mitigationHealthcare communicationMedical simplificationHealth literacy.Medical informatics.Natural language processing (Computer science).Conference Proceeding10.1109/COMPAS67506.2025.11381760