Designing of specialized language model for medical diagnosis: a systematic review of architectures, applications, and clinical challenges

Background Large Language Models have become impactful in medicine by facilitating the processing of difficult medical data and aiding diagnosis-making. Given general LLM’s weaknesses, specialized medical LLMs are constructed for clinical precision, contextual understanding and safety in risky diagnostic applications. This review summarizes recent trends in medical LLMs for diagnosis, reported during 2021–2025. Methods A systematic literature review was performed following the PRISMA methodology. A search was designed and systematically carried out in the SCOPUS database using criteria of inclusion and exclusion (type of publication, language, period and country) and 75 peer-reviewed articles were chosen from a database of 1,911 records, after a multi-stage screening and eligibility evaluation. Results In summary, there have been significant advancements in the design and application of medical LLMs, with trends in hybrid architecture, multi-modal learning, prompt optimization techniques, diffusion-based imaging models, and multi-agent diagnostic system models. However, a number of persistent challenges that need to be overcome remain in the development and implementation of these models; namely, hallucination, bias, and poor probability calibration in LLMs, issues with explainability of LLMs, and ineffectiveness in a clinical environment. Conclusions This review highlights the fast pace of development and limitations of domain-specific medical LLMs. While impressive improvements in diagnostic performance have been made, the future directions must address improvements in reliability, safety and clinical validation in order to be seamlessly and responsibly incorporated in the practice.