Public articles linked to the same research event.
Journal of medical Internet research Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.
Following PRISMA 2020 guidelines and searching PubMed/MEDLINE, Scopus, and Web of Science (January 2018 through May 2026), this systematic review screened 1890 records and included 35 studies published between 2024 and 2026, grouping them into fine-tuning or parameter-efficient fine-tuning (7/35, 20%), retrieval-augmented generation (17/35, 48.6%), and hybrid approaches (11/35, 31.4%) for descriptive synthesis, finding that fine-tuning performed strongly on narrow task-specific applications (area under the receiver operating characteristic curve up to 0.912 for cancer detection and area under curve 0.892 for major depressive disorder prediction), that retrieval-augmented generation improved guideline adherence and diagnostic accuracy (from 71.1% to 92.1% and from 78.9% to 94.