SynthesisBMC medical informatics and decision making2025
Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Expert consensus on pre-eclampsia risk screening tools for low- and middle-income countries: Development of a new Target Product Profile.PLOS global public health · 2026Article
- Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity.Nursing reports (Pavia, Italy) · 2025Review
- The role of AI-driven art therapy in supporting autism, mental health, and emotional well-being: An umbrella review.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI), which emulates human intelligence through knowledge-based heuristics, has transformative impacts across various industries. In the global healthcare sector, there is a pressing need for advanced risk assessment tools due to the shortage of healthcare workers to manage the health needs of the growing population effectively. AI-based tools such as triage systems, symptom checkers, and risk prediction models are poised to democratize healthcare. This systematic review aims to comprehensively assess the current landscape of AI tools in healthcare and identify areas for future research, focusing particularly on sexual reproductive and mental health.
methodsAdhering to PRISMA guidelines, this review utilized data from seven databases: Science Direct, PubMed, SAGE, ACM Digital Library, Springer, IEEE Xplore, and Wiley. The selection process involved a rigorous screening of titles, abstracts, and full-text examinations of peer-reviewed articles published in English from 2018 to 2023. To ensure the quality of the studies, two independent reviewers applied the PROBAST and QUADAS-2 tools to evaluate the risk of bias in prognostic and diagnostic studies, respectively. Data extraction was also independently conducted.
resultsOut of 1743 peer-reviewed articles screened, 63 articles (3.61%) met the inclusion criteria and were included in this study. These articles predominantly utilized clinical vignettes, demographic data, and medical data from online sources. Of the studies analyzed, 61.9% focused on sexual and reproductive health, while 38.1% addressed mental health assessment tools. The analysis revealed an increasing trend in research output over the review period and a notable disparity between developed and developing countries. The review highlighted that AI-based systems could outperform traditional clinical methods when implemented correctly.
conclusionsThe findings indicate that integrating AI-based models into existing clinical systems can lead to substantial improvements in healthcare delivery and outcomes. However, future research should prioritize obtaining larger and more diverse datasets, including those from underrepresented populations, to reduce biases and disparities. Additionally, for AI-based healthcare interventions to be widely adopted, transparency and ethical considerations must be addressed, ensuring these technologies are used responsibly and effectively in practical scenarios.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.