Artificial intelligence in geriatric medicine: potential and challenges. Systematic review
Bibliographic record
Abstract
BACKGROUND. The growing global aging population increases the demand for innovative solutions in geriatric medicine to address complex health challenges. AI offers promising tools for enhancing care, but faces unique challenges in this area. OBJECTIVE. To evaluate the potential of AI to enhance diagnosis, monitoring and care for elderly patients in geriatric medicine and to identify key challenges to its implementation. MATERIALS AND METHODS. A systematic review was conducted according to PRISMA guidelines. Literature searches were conducted in PubMed, Scopus and RSCI databases (2020-2025), using keywords related to AI and geriatrics. Studies focused on clinical applications of AI in patients aged 60 years and over were included. After removing duplicates and irrelevant publications from 1,243 records, 50 studies were analyzed. The quality of the studies was assessed using the AMSTAR-2 and the Newcastle-Ottawa scales. RESULTS. AI demonstrates high efficacy in early diagnosis of dementia (up to 90 % accuracy), osteoporosis (89 %), and cardiovascular diseases (91 %), as well as in monitoring falls (92 %) and nutritional status (90 %). Key challenges include ethical concerns (privacy, algorithmic bias), limited technology access (40 % in rural areas), and insufficient staff training (only 30 % of geriatricians are AI-proficient). CONCLUSIONS. AI holds transformative potential for geriatric medicine but requires adaptation to the unique needs of older adults, development of ethical and technical standards, and enhanced training programs for healthcare professionals. This review underscores the need to integrate AI as part of a person-centered care ecosystem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".