{
  "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.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s41666-026-00242-y.",
  "abstract_shown": true,
  "affiliations": [
    "Recife, PE Brazil"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Leonides Medeiros Neto"
    },
    {
      "affs": [
        0
      ],
      "name": "Maicon Herverton Lino Ferreira da Silva Barros"
    },
    {
      "affs": [
        0
      ],
      "name": "Kayo Henrique de Carvalho Monteiro"
    },
    {
      "affs": [
        0
      ],
      "name": "Estefani Pontes Simão"
    },
    {
      "affs": [
        0
      ],
      "name": "Sebastião Rogério da Silva Neto"
    },
    {
      "affs": [
        0
      ],
      "name": "Luis Filipe Silva de Vasconcelos"
    },
    {
      "affs": [
        0
      ],
      "name": "João Pedro Pimenta"
    },
    {
      "affs": [
        0
      ],
      "name": "Iago Richard Rodrigues"
    },
    {
      "affs": [
        0
      ],
      "name": "Patricia Takako Endo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5za8ktj8czwt2.bib",
  "cited_by": 0,
  "doi": "10.1007/s41666-026-00242-y",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719915/?tool=EBI",
  "id": "5za8ktj8czwt2",
  "issns": [
    "2509-4971",
    "2509-498X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13719915",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Review"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7172314911",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719915/pdf/?tool=EBI",
  "pmcid": "PMC13719915",
  "pmid": "42964549",
  "published": "2026-08-03",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/5za8ktj8czwt2.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:37Z",
      "native_id": "PMC:PMC13719915",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719915"
    },
    {
      "first_seen": "2026-11-02T12:23:47Z",
      "native_id": "PMC13719915",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s41666-026-00242-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Generative Language Models for Disease Treatment Recommendations: A Systematic Literature Review",
  "title_text": "Generative Language Models for Disease Treatment Recommendations: A Systematic Literature Review",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:53Z",
  "url": "https://research.draconitic.com/paper/5za8ktj8czwt2",
  "venue": "Journal of healthcare informatics research",
  "versions": []
}