Bridging the Gap: AI-Powered Digital Health Assistants in Men’s Preventive Care—A Narrative Review of Integration with Nursing, Laboratory Systems, and Public Health Surveillance

Hadhal Saud Qaeid Alotaibi (1), Shuwaymi Ofays Hadyan Alqahtani (2), Mazzah Majeed Salamah Alsulobi (3), Intisar Matar Alhalil Alsulobi (3), Nujud Majed Mutair Albanaqi (3), Nouf Salman Hamoud Alsulopi (3), Dahma Ali Ahmad Otayf (4), Mohammed Hussain Ali Alanazi (5), Mashhour Sinhat Abdulhadi Aldawsari (6), Abdullah Ali Amer Alshehri (7), Salem Saleh Aldamaeen (8), Hadi Jubran Ahmed Mejameme (9)
(1) Nafi General Hospital Third Health Cluster,Ministry of Health, Saudi Arabia,
(2) Riyadh First Cluster - Al-Rain Hospital - Primary Health Care Center, Al-Rabwa Ministry of Health, Saudi Arabia,
(3) Al-Uwaiqaila General Hospital, Al-Owaiqaila city,Ministry of Health, Saudi Arabia,
(4) Al-Harth General Hospital, Ministry of Health, Saudi Arabia,
(5) Qurayyat General Hospital Regional Laboratory,Ministry of Health, Saudi Arabia,
(6) Long-term care Hospital Riyadh, Ministry of Health, Saudi Arabia,
(7) Al-Majarda General Hospital,Ministry of Health, Saudi Arabia,
(8) Al Iman General Hospital, Ministry of Health, Saudi Arabia,
(9) Ahad Al Masarha General Hospital, Ministry of Health, Saudi Arabia

Abstract

Background: Men experience significant health disparities, including higher mortality from preventable causes, later diagnosis of chronic conditions, and lower engagement with preventive services. This "men’s health gap" is exacerbated by barriers to healthcare access, health literacy, and help-seeking behaviors. Concurrently, artificial intelligence (AI) has catalyzed the development of sophisticated digital health assistants (DHAs)—chatbots, virtual agents, and mobile apps—capable of delivering personalized, scalable health promotion. Aim: This narrative review synthesizes current evidence on the role of AI-powered DHAs in advancing men’s preventive care, with a specific focus on their integration with nursing practices, medical laboratory data systems, and public health surveillance infrastructures. Methods: A comprehensive search of PubMed, IEEE Xplore, CINAHL, Scopus, and ACM Digital Library was conducted.  Results: AI-DHAs show promise in improving men’s engagement with preventive screenings, mental health support, and chronic disease management through 24/7 accessibility and personalized dialogue. Effective integration hinges on secure, bidirectional data flow: DHAs can collect patient-reported outcomes, trigger nursing follow-up for high-risk cases, ingest and interpret lab results (e.g., PSA, lipid panels) to provide contextualized feedback, and contribute anonymized aggregate data to public health dashboards for monitoring men’s health trends and disparities. Conclusion: AI-DHAs represent a transformative tool for men’s preventive health but function optimally as a node within a connected care ecosystem. Success requires robust technical integration, ensuring security and interoperability, alongside a redefined nursing role that blends virtual triage with human empathy. 

Full text article

Generated from XML file

References

Abernethy, A., Adams, L., Barrett, M., Bechtel, C., Brennan, P., Butte, A., ... & Valdes, K. (2022). The promise of digital health: then, now, and the future. NAM perspectives, 2022, 10-31478. https://doi.org/10.31478/202206e

Agarwal, S., Punn, N. S., Sonbhadra, S. K., Tanveer, M., Nagabhushan, P., Pandian, K. K., & Saxena, P. (2020). Unleashing the power of disruptive and emerging technologies amid COVID-19: A detailed review. arXiv preprint arXiv:2005.11507. https://doi.org/10.48550/arXiv.2005.11507

Baker, P. (2016). Men's health: an overlooked inequality. British Journal of Nursing, 25(19), 1054-1057. https://doi.org/10.12968/bjon.2016.25.19.1054

Bennett, S., Robb, K. A., Zortea, T. C., Dickson, A., Richardson, C., & O'Connor, R. C. (2023). Male suicide risk and recovery factors: A systematic review and qualitative metasynthesis of two decades of research. Psychological Bulletin, 149(7-8), 371.

Dabla, P. K., Gruson, D., Gouget, B., Bernardini, S., & Homsak, E. (2021). Lessons learned from the COVID-19 pandemic: emphasizing the emerging role and perspectives from artificial intelligence, mobile health, and digital laboratory medicine. Ejifcc, 32(2), 224. https://pubmed.ncbi.nlm.nih.gov/34421492/

Ergin, E., Karaarslan, D., Şahan, S., & Bingöl, Ü. (2023). Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research. Journal of Robotic Surgery, 17(4), 1847-1855. https://doi.org/10.1007/s11701-023-01592-0

Gaffney, H., Mansell, W., & Tai, S. (2019). Conversational agents in the treatment of mental health problems: mixed-method systematic review. JMIR mental health, 6(10), e14166. https://doi.org/10.2196/14166

He, Y., Yang, L., Qian, C., Li, T., Su, Z., Zhang, Q., & Hou, X. (2023). Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials. Journal of Medical Internet Research, 25, e43862. https://doi.org/10.2196/43862

Jabir, A. I., Martinengo, L., Lin, X., Torous, J., Subramaniam, M., & Tudor Car, L. (2023). Evaluating conversational agents for mental health: scoping review of outcomes and outcome measurement instruments. Journal of Medical Internet Research, 25, e44548. https://doi.org/10.2196/44548

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., ... & Coiera, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9), 1248-1258. https://doi.org/10.1093/jamia/ocy072

Luger, T. M., Hogan, T. P., Richardson, L. M., Cioffari-Bailiff, L., Harvey, K., & Houston, T. K. (2016). Older veteran digital disparities: examining the potential for solutions within social networks. Journal of medical Internet research, 18(11), e296. https://doi.org/10.2196/jmir.6385

Mahmud, N., Asch, D. A., Sung, J., Reitz, C., Coniglio, M. S., McDonald, C., ... & Mehta, S. J. (2021). Effect of text messaging on bowel preparation and appointment attendance for outpatient colonoscopy: a randomized clinical trial. JAMA network open, 4(1), e2034553-e2034553. doi:10.1001/jamanetworkopen.2020.34553

Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016). SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899-908. https://doi.org/10.1093/jamia/ocv189

Marcos-Marcos, J., Mateos, J. T., Gasch-Gallén, À., & Álvarez-Dardet, C. (2021). Men’s health across the life course: A gender relational (critical) overview. Journal of Gender Studies, 30(7), 772-785. https://doi.org/10.1080/09589236.2019.1703657

Meszaros, J., Minari, J., & Huys, I. (2022). The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Frontiers in Genetics, 13, 927721. https://doi.org/10.3389/fgene.2022.927721

Milne-Ives, M., Lam, C., De Cock, C., Van Velthoven, M. H., & Meinert, E. (2020). Mobile apps for health behavior change in physical activity, diet, drug and alcohol use, and mental health: systematic review. JMIR mHealth and uHealth, 8(3), e17046. https://doi.org/10.2196/17046

Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., ... & Sander, L. B. (2021). Digital interventions for the treatment of depression: A meta-analytic review. Psychological bulletin, 147(8), 749.

Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2016). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of behavioral medicine, 1-17. https://doi.org/10.1007/s12160-016-9830-8

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future?. International journal of nursing sciences, 6(1), 106-110. https://doi.org/10.1016/j.ijnss.2018.09.013

Pepito, J. A., Ito, H., Betriana, F., Tanioka, T., & Locsin, R. C. (2020). Intelligent humanoid robots expressing artificial humanlike empathy in nursing situations. Nursing Philosophy, 21(4), e12318. https://doi.org/10.1111/nup.12318

Robert, N. (2019). How artificial intelligence is changing nursing. Nursing management, 50(9), 30-39. DOI: 10.1097/01.NUMA.0000578988.56622.21

Robertson, S., & Baker, P. (2021). Men and health promotion in the 21st century: A critical perspective. *Health Promotion International, 36**(4), 1150-1159.

Shan, Y., Ji, M., Xie, W., Qian, X., Li, R., Zhang, X., & Hao, T. (2022). Language use in conversational agent–based health communication: Systematic review. Journal of Medical Internet Research, 24(7), e37403. https://doi.org/10.2196/37403

Smith, J. A., Braunack-Mayer, A., & Wittert, G. (2022). What do we know about men’s help-seeking and health service use? Medical Journal of Australia, 216(1), 16-20.

Torous, J., Bucci, S., & Bell, I. H. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318-335.

Verma, R., & Domingo, N. (2023). An updated trend in nursing-Artificial intelligence A review Article. IP Journal of Paediatrics and Nursing Science. https://doi.org/10.18231/j.ijpns.2023.001

Wesley, D. B., Blumenthal, J., Shah, S., Littlejohn, R. A., Pruitt, Z., Dixit, R., ... & Ratwani, R. M. (2021). A novel application of SMART on FHIR architecture for interoperable and scalable integration of patient-reported outcome data with electronic health records. Journal of the American Medical Informatics Association, 28(10), 2220-2225. https://doi.org/10.1093/jamia/ocab110

WHO, G. (2011). Global status report on noncommunicable diseases 2010.

Yang, H. S., Wang, F., Greenblatt, M. B., Huang, S. X., & Zhang, Y. (2023). AI chatbots in clinical laboratory medicine: foundations and trends. Clinical chemistry, 69(11), 1238-1246. https://doi.org/10.1093/clinchem/hvad106

Authors

Hadhal Saud Qaeid Alotaibi
hadhalsa@moh.gov.sa (Primary Contact)
Shuwaymi Ofays Hadyan Alqahtani
Mazzah Majeed Salamah Alsulobi
Intisar Matar Alhalil Alsulobi
Nujud Majed Mutair Albanaqi
Nouf Salman Hamoud Alsulopi
Dahma Ali Ahmad Otayf
Mohammed Hussain Ali Alanazi
Mashhour Sinhat Abdulhadi Aldawsari
Abdullah Ali Amer Alshehri
Salem Saleh Aldamaeen
Hadi Jubran Ahmed Mejameme
Alotaibi, H. S. Q., Shuwaymi Ofays Hadyan Alqahtani, Mazzah Majeed Salamah Alsulobi, Intisar Matar Alhalil Alsulobi, Nujud Majed Mutair Albanaqi, Nouf Salman Hamoud Alsulopi, … Hadi Jubran Ahmed Mejameme. (2024). Bridging the Gap: AI-Powered Digital Health Assistants in Men’s Preventive Care—A Narrative Review of Integration with Nursing, Laboratory Systems, and Public Health Surveillance. Saudi Journal of Medicine and Public Health, 1(2), 1535–1542. https://doi.org/10.64483/202412440

Article Details

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.