Large language models are increasingly being built specifically for medicine, and a new systematic review maps how these specialized systems are being designed for diagnosis, where they are being applied, and what still stands in the way of safe clinical use.
The review, published in BMC Medical Informatics and Decision Making, was carried out following PRISMA methodology. The authors searched the SCOPUS database with predefined inclusion and exclusion criteria covering publication type, language, period and country. From 1,911 records identified, 75 peer-reviewed articles published between 2021 and 2025 survived multi-stage screening and eligibility evaluation.
According to the review, the field has seen significant advancements in the design and application of medical language models for diagnosis. The reported trends include hybrid architectures, multi-modal learning, prompt optimization techniques, diffusion-based imaging models, and multi-agent diagnostic system models.
The motivation for building domain-specific models rather than relying on general-purpose systems is set out in the review’s background. General language models, the authors write, have weaknesses that matter in clinical settings, so specialized medical models are constructed for clinical precision, contextual understanding and safety in risky diagnostic applications.
Despite the progress, the review identifies a set of persistent challenges that remain unresolved. These include hallucination, bias, and poor probability calibration in language models, as well as difficulties with explainability and ineffectiveness of these systems in real clinical environments.
The authors conclude that the review highlights both the fast pace of development and the limitations of domain-specific medical language models. While impressive improvements in diagnostic performance have been made, they argue that future work must address reliability, safety and clinical validation so the models can be incorporated into practice responsibly.
It is worth noting the scope of the evidence base. The findings summarize 75 peer-reviewed studies published over a five-year window, drawn from a single database, and the review describes trends and challenges rather than reporting the performance of any single model. The published version is also an early-release, peer-reviewed accepted article that remains subject to further edits before the final version of record.
For clinicians and researchers following the field, the review’s value lies in its consolidated picture: the architectural directions now dominating medical language model research, and the safety and validation gaps that the authors say must be closed before diagnostic deployment becomes routine.
Subject of Research: A systematic review of specialized large language models designed for medical diagnosis
Article Title: Designing of specialized language model for medical diagnosis: a systematic review of architectures, applications, and clinical challenges
Article References: Sharma, H., & Agnihotri, N. (2026). Designing of specialized language model for medical diagnosis: a systematic review of architectures, applications, and clinical challenges. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-026-03799-1
Image Credits: AI Generated
DOI: 10.1186/s12911-026-03799-1
Keywords: large language models, medical diagnosis, systematic review, PRISMA, SCOPUS, hybrid architecture, multi-modal learning, prompt optimization, hallucination, bias, explainability, clinical validation
