Generative Language Models for Disease Treatment Recommendations: A Systematic Literature Review
- 1Recife, PE Brazil
Abstract
The rapid evolution of Generative Artificial Intelligence (GenAI) presents significant opportunities to transform healthcare, particularly in generating personalized treatment recommendations. This systematic literature review explores the current state of GenAI language models applications in various medical domains, assessing their effectiveness, applicability, and limitations. The review addresses nine specific research questions to understand the potential and challenges of integrating GenAI into clinical practice. We use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From a pool of 3237 studies, 42 were selected based on inclusion and exclusion criteria. These studies were analyzed to evaluate the use of generative language models, such as GPT-3 and GPT-4, in various medical domains including oncology, cardiovascular, gastrointestinal, and ophthalmological care. The analysis revealed that most GenAI applications in healthcare rely on general purpose LLMs to provide treatment recommendations. Fine-tuning with domain-specific data and prompt engineering were found to significantly improve output quality and reliability. However, persistent challenges include lack of clinical validation, ethical concerns such as bias, and issues related to transparency and regulatory compliance. While GenAI demonstrates strong potential to support clinical decision-making, real-world deployment remains limited due to unresolved ethical and validation issues. Future research should prioritize the development of interpretable, domain-specific models and rigorous clinical trials to ensure safe and effective integration into healthcare settings.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00242-y.
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BibTeX
@article{neto2026generative,
title = {{Generative Language Models for Disease Treatment Recommendations: A Systematic Literature Review}},
author = {Leonides Medeiros Neto and Maicon Herverton Lino Ferreira da Silva Barros and Kayo Henrique de Carvalho Monteiro and Estefani Pontes Simão and Sebastião Rogério da Silva Neto and Luis Filipe Silva de Vasconcelos and João Pedro Pimenta and Iago Richard Rodrigues and Patricia Takako Endo},
journal = {Journal of healthcare informatics research},
year = {2026},
month = aug,
doi = {10.1007/s41666-026-00242-y},
url = {https://europepmc.org/article/PMC/PMC13719915},
note = {CC BY},
}
RIS
TY - JOUR
TI - Generative Language Models for Disease Treatment Recommendations: A Systematic Literature Review
AU - Leonides Medeiros Neto
AU - Maicon Herverton Lino Ferreira da Silva Barros
AU - Kayo Henrique de Carvalho Monteiro
AU - Estefani Pontes Simão
AU - Sebastião Rogério da Silva Neto
AU - Luis Filipe Silva de Vasconcelos
AU - João Pedro Pimenta
AU - Iago Richard Rodrigues
AU - Patricia Takako Endo
PY - 2026
DA - 2026/08/03
JO - Journal of healthcare informatics research
DO - 10.1007/s41666-026-00242-y
UR - https://europepmc.org/article/PMC/PMC13719915
AB - The rapid evolution of Generative Artificial Intelligence (GenAI) presents significant opportunities to transform healthcare, particularly in generating personalized treatment recommendations. This systematic literature review explores the current state of GenAI language models applications in various medical domains, assessing their effectiveness, applicability, and limitations. The review addresses nine specific research questions to understand the potential and challenges of integrating GenAI into clinical practice. We use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From a pool of 3237 studies, 42 were selected based on inclusion and exclusion criteria. These studies were analyzed to evaluate the use of generative language models, such as GPT-3 and GPT-4, in various medical domains including oncology, cardiovascular, gastrointestinal, and ophthalmological care. The analysis revealed that most GenAI applications in healthcare rely on general purpose LLMs to provide treatment recommendations. Fine-tuning with domain-specific data and prompt engineering were found to significantly improve output quality and reliability. However, persistent challenges include lack of clinical validation, ethical concerns such as bias, and issues related to transparency and regulatory compliance. While GenAI demonstrates strong potential to support clinical decision-making, real-world deployment remains limited due to unresolved ethical and validation issues. Future research should prioritize the development of interpretable, domain-specific models and rigorous clinical trials to ensure safe and effective integration into healthcare settings. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00242-y.
SN - 2509-4971
SN - 2509-498X
ER -