Authors : Shihab Ahmed Hameed; Afnan Sh. Ahmed; Akram M. Zeki

Volume/Issue : Volume 11 - 2026, Issue 10 - October

---

Google Scholar : https://tinyurl.com/cj4ksjv2

DOI : https://doi.org/10.38124/ijisrt/26Oct1515

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Artificial intelligence (AI) is transitioning from accuracy studies on historical data to prospective, real-world testing of performance in everyday care, but whether this will lead to better quality of care is debated. We review latest peerreviewed evidence (2023-26) by six quality domains-effectiveness, safety, efficiency, patient-centeredness, cost and equity across set of validated articles.

Keywords : Artificial Intelligence, Healthcare Quality, Clinical Decision Support, Large Language Models, Evidence Synthesis, Health Equity, Digital Health.

References :

1. G. Morone et al., “Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting,” Front. Digit. Health, vol. 7, Art. no. 1550731, 2025, doi: 10.3389/fdgth.2025.1550731.
2. E. Alghareeb and N. Aljehani, “AI in health care service quality: systematic review,” JMIR AI, vol. 4, Art. no. e69209, 2025, doi: 10.2196/69209.
3. S. Bedi et al., “Testing and evaluation of health care applications of large language models: a systematic review,” JAMA, vol. 333, pp. 319–328, 2025, doi: 10.1001/jama.2024.21700.
4. V. Hernström et al., “Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomized, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study,” Lancet Digit. Health, vol. 7, no. 3, pp. e175–e183, Mar. 2025, doi: 10.1016/S2589-7500(24)00267-X.
5. K. Lång et al., “Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomized, controlled, non-inferiority, single-blinded, screening accuracy study,” Lancet Oncol., vol. 24, no. 8, pp. 936–944, 2023, doi: 10.1016/S1470-2045(23)00298-X.
6. J. Gommers et al., “Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomized, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial,” Lancet, 2026, doi: 10.1016/S0140-6736(25)02464-X.
7. M. Chen et al., “Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage,” npj Digit. Med., vol. 9, Art. no. 222, 2026, doi: 10.1038/s41746-026-02409-8.
8. H. Takita et al., “A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians,” npj Digit. Med., vol. 8, Art. no. 175, 2025, doi: 10.1038/s41746-025-01543-z.
9. E. Goh et al., “GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trial,” Nat. Med., vol. 31, pp. 1233–1238, 2025, doi: 10.1038/s41591-024-03456-y.
10. E. Goh et al., “Large language model influence on diagnostic reasoning: a randomized clinical trial,” JAMA Netw. Open, vol. 7, Art. no. e2440969, 2024, doi: 10.1001/jamanetworkopen.2024.40969.
11. A. Agweyu et al., “Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial,” Nat. Med., 2026, doi: 10.1038/s41591-026-04503-6.
12. K. D. Olson et al., “Use of ambient AI scribes to reduce administrative burden and professional burnout,” JAMA Netw. Open, vol. 8, no. 10, Art. no. e2534976, 2025, doi: 10.1001/jamanetworkopen.2025.34976.
13. R. A. El Arab and O. A. Al Moosa, “Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare,” npj Digit. Med., vol. 8, Art. no. 548, 2025, doi: 10.1038/s41746-025-01722-y.
14. M. Liu et al., “A scoping review and evidence gap analysis of clinical AI fairness,” npj Digit. Med., vol. 8, 2025, doi: 10.1038/s41746-025-01667-2.
15. D. Henzler et al., “Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels,” BMC Health Serv. Res., vol. 25, Art. no. 633, 2025, doi: 10.1186/s12913-025-12664-2.