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
Bibliographic record
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.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".