THE EVOLUTION OF AI MEDICAL CONSULTANTS AND THEIR IMPACT ON PATIENT EDUCATION: A LITERATURE REVIEW

Agnieszka Olejnik; Maria Możdżan; Laura Biskup; Edward Zheng; Beata  Huszcza; Konrad  Gronek; Julia Kaczmarek; Jakub Grandos; Dominik Gajewski; Zuzanna Głowacka; Kinga Kościołek

doi:10.31435/ijitss.1(49).2026.5058

Authors

Agnieszka Olejnik Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0002-4180-5270
Maria Możdżan Teaching Hospital No. 2 of Medical University of Lodz, Faculty of Medicine, Medical University of Lodz, Łódź, Poland https://orcid.org/0009-0001-3113-9158
Laura Biskup Teaching Hospital No. 2 of Medical University of Lodz, Faculty of Medicine, Medical University of Lodz, Łódź, Poland https://orcid.org/0009-0000-9336-3705
Edward Zheng Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0009-0496-293X
Beata Huszcza Municipal Medical Centre of Karol Jonscher in Łódź, Łódź, Poland https://orcid.org/0009-0002-2168-5275
Konrad Gronek Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0008-7812-5432
Julia Kaczmarek Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0006-3781-3066
Jakub Grandos St. John of God Brothers Hospitaller Hospital in Lodz, Łódź, Poland https://orcid.org/0009-0002-5859-6532
Dominik Gajewski St. John of God Brothers Hospitaller Hospital in Lodz, Łódź, Poland https://orcid.org/0009-0003-8611-4518
Zuzanna Głowacka Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0006-6594-1444
Kinga Kościołek Independent Public Healthcare Institution MSWiA in Lodz, Łódź, Poland https://orcid.org/0009-0000-6235-7513

DOI:

https://doi.org/10.31435/ijitss.1(49).2026.5058

Keywords:

Artificial Intelligence, Health Literacy, Patient Education, Social Innovation, Large Language Models

Abstract

Background: The rapid development of Artificial Intelligence (AI) has led to its transition from a theoretical concept in informatics to a functional clinical tool. This study evaluates the impact of AI-based consultants and Large Language Models (LLMs) on patient health literacy and the quality of medical education within the framework of contemporary healthcare standards.

Methods: A systematic literature review was conducted in accordance with PRISMA guidelines. Databases including PubMed, Embase, and the Cochrane Library were searched for peer-reviewed publications from 2021 to 2025. Following a rigorous selection process based on Evidence-Based Medicine (EBM) criteria, 31 key sources were identified and synthesized.

Results: LLMs significantly improve the comprehensibility of clinical terminology by adapting medical content to a 9th-11th-grade literacy level. Digital feedback systems, such as the KidneyOnline platform, demonstrate superior efficacy in improving patient adherence compared with standard protocols. However, several critical limitations were identified, including hallucination bias (fabrication of clinical data), a “readability floor” phenomenon associated with up to 83% content reduction, and the risk of professional deskilling among clinicians. Furthermore, wearable devices such as the Apple Watch show high correlation (r=0.92) with medical-grade pulse oximetry, offering new perspectives for real-time health monitoring.

Conclusions: AI represents a transformative yet socially consequential innovation in patient education and health literacy. Its responsible implementation requires a strict human-in-the-loop model to ensure clinical safety, ethical accountability, and equitable access. Systematic verification of AI-generated medical information by qualified healthcare professionals remains essential to maintain both high standards of care and public trust in digital health innovations.

References

Brązert, M., & Polak, G. (2024). Sztuczna inteligencja w chirurgii – czy to już rzeczywistość? Polski Przegląd Chirurgiczny, 96(1), 1–8. https://doi.org/10.5604/01.3001.0053.9857

Czerwoniuk, M. (2024). Sztuczna inteligencja w medycynie: Możliwości zastosowania a wyzwania etyczne i prawne. Studia Prawnoustrojowe, 1, 135–155. https://doi.org/10.31648/sp.9705

Thapa, S., Adhikari, A., Sapkota, S., et al. (2025). Artificial intelligence in health literacy: A systematic review. Healthcare, 13(15), 1829. https://doi.org/10.3390/healthcare13151829

Zhu, L., et al. (2025). Accuracy of large language models in answering patient questions. JMIR Medical Informatics, 13(1), e64963. https://doi.org/10.2196/64963

Kuwabara, A., et al. (2024). Readability of AI-generated patient education materials. European Journal of Cardiovascular Nursing, 23(2), 122–128. https://doi.org/10.1093/eurjcn/zvad072

Zhang, Y., et al. (2024). Effectiveness of the KidneyOnline AI system in chronic kidney disease management. Journal of Medical Internet Research, 26, e54206. https://doi.org/10.2196/54206

Abbott, M., et al. (2024). Google Search vs ChatGPT: Impact on patient understanding. Patient Preference and Adherence, 18, 1145–1156. https://doi.org/10.2147/PPA.S441235

Sallam, M. (2023). ChatGPT utility in healthcare education and research: A systematic review. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887

Miller, J., et al. (2025). Risk assessment of LLM-based triage systems. Digital Health, 11, 20552076251388140. https://doi.org/10.1177/20552076251388140

Wang, S., et al. (2024). AI in clinical documentation: Efficiency and accuracy. Electronics, 13(15), 2961. https://doi.org/10.3390/electronics13152961

Mishra, A. (2024). Promoting inclusive prompt engineering in healthcare. International Journal of AI and Machine Learning, 4(2), 231–245.

Johnson, R., et al. (2024). Translation accuracy of generative AI in medical contexts. JMIR Medical Informatics, 12(1), e54345. https://doi.org/10.2196/54345

Brown, C., et al. (2025). The impact of AI on professional skill erosion. Artificial Intelligence Review, 58, 11352. https://doi.org/10.1007/s10462-025-11352-1

Nieminen, M., et al. (2024). The vicious circles of skill erosion: A case study of cognitive augmentation. Aalto University Research.

Larsson, A., et al. (2020). Cognitive augmentation and the risk of deskilling. Anesthesiology, 133(3), 495–502. https://doi.org/10.1097/ALN.0000000000003426

Genovese, S., et al. (2025). Large language models for medical translation: A comparative study. Scientific Reports, 15, 11729812. https://doi.org/10.1038/s41598-025-11729812

Aljamaan, F., et al. (2024). Hallucinations in AI chatbots: Challenges in medical accuracy. Journal of Medical Internet Research, 26, e10941792. https://doi.org/10.2196/10941792

Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-the-art and setting a research agenda. Journal of Business Research, 123, 557–568. https://doi.org/10.1016/j.jbusres.2020.10.030

Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2020). Health intelligence: How artificial intelligence transforms population health management. Journal of Medical Internet Research, 22(10), e18820. https://doi.org/10.2196/18820

Wentao, S., Ruixiao, L., Tianxiang, S., et al. (2023). Large language models: Principles, implementation, and progress. Journal of Computer Research and Development, 2, 351–361. https://doi.org/10.7544/issn1000-1239.202330303

Wolff, R. F., Moons, K. G. M., Riley, R. D., et al. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1), 51–58. https://doi.org/10.7326/M18-1376

Levin, C., Kagan, T., Rosen, S., & Saban, M. (2024). An evaluation of the capabilities of language models and nurses in providing neonatal clinical decision support. International Journal of Nursing Studies, 155, 104771. https://doi.org/10.1016/j.ijnurstu.2024.104771

Schoenfeld, P. (2014). Evidence-based medicine and information retrieval. Patient Education and Counseling, 94(2), 285–286. https://doi.org/10.1016/j.pec.2013.11.011

Galanter, R., et al. (2016). Developing an intelligent voice assistant for the elderly. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 121–124). https://doi.org/10.1145/2899475.2899482

Alvarado, J., et al. (2024). Accessible applications for people with visual disabilities through voice assistants: A systematic review of the literature. ResearchGate.

Kulyukin, V., et al. (2008). Enhancing independence and safety for blind and deaf-blind public transit riders. In CHI ’08 Extended Abstracts on Human Factors in Computing Systems. https://doi.org/10.1145/1357054.1357143

Daraz, L., Morrow, A. S., Ponce, O. J., et al. (2019). Can patients trust online health information? A meta-narrative systematic review addressing the quality of health information on the internet. Journal of General Internal Medicine, 34(9), 1884–1891. https://doi.org/10.1007/s11606-019-05109-0

Dubin, J. A., Bains, S. S., Chen, Z., et al. (2023). Using a google web search analysis to assess the utility of ChatGPT in total joint arthroplasty. Journal of Arthroplasty, 38(7), 1195–1202. https://doi.org/10.1016/j.arth.2023.04.007

Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., & Pounders, K. (2016). Health literacy and health information technology adoption: The potential for a new digital divide. Journal of Medical Internet Research, 18(10), e264. https://doi.org/10.2196/jmir.6349

Aminololama-Shakeri, S., & Lopez, J. E. (2019). The doctor-patient relationship with artificial intelligence. American Journal of Roentgenology, 212(2), 308–310. https://doi.org/10.2214/AJR.18.20509

Smith, A., et al. (2025). Long-term clinical outcomes of AI-driven patient monitoring. Journal of Clinical Medicine, 14(4), 1122. https://doi.org/10.3390/jcm14041122