ArticleDigital health
Application of artificial intelligence in medical risk prediction: Bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Development and Validation of an Interpretable Machine Learning Model Based on Routine Blood Biomarkers: For Predicting Age-Related Hearing Loss.Diagnostics (Basel, Switzerland) · 2026Article
- Facilitators and Barriers of Using an Artificial Intelligence Agent in Chronic Disease Management: A Normalization Process Theory-Guided Qualitative Study of Older Patients with COPD.Healthcare (Basel, Switzerland) · 2026Article
- From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015-2025).Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has played an important role in the field of medical risk prediction with its strong learning ability and data processing capabilities. With the rapid development of research in this field, it is necessary to conduct quantitative literature analysis to understand the development trends and research hotspots of AI in the field of medical risk prediction. Objective: Through a comprehensive bibliometric analysis, this paper summarizes the development stage and key research hotspots of the application of AI in the field of medical risk prediction in the past 20 years. Additionally, we provide a thorough analysis of emerging trends and future directions, offering insights into how advancements in AI are likely to shape risk prediction methodologies and their clinical applications in the years to come. Methods: Relevant articles from the establishment of the database to 2024 were retrieved through Science Citation Index and Social Sciences Citation Index of the Web of Science Core Collection. Citespace, VOSviewer, Scimago Graphica, Pajek, and other software were used for bibliometric and visual analysis. Result: A total of 2080 articles were included. From 1986 to 2004, this field experienced a slow development period, with the number of papers published per year less than 10. From 2005 to 2020, the number of papers published increased with a linear trend, and entered an exponential rapid growth stage after 2020, with the development entering a mature stage. The United States was the country with the most extensive cooperation and the largest number of publications (652 articles, 31.35%). The diseases, AI technologies, and functions that have received the most attention in this field are cancer, machine learning, and prediction, respectively. Conclusions: Artificial intelligence in medical risk prediction has transitioned from technical exploration to a critical component of clinical practice, expanding from single-disease forecasts to complex, multimodal assessments. Advances in machine learning and personalized medicine have integrated AI into medical decision-making and management, yet widespread adoption requires addressing challenges related to interpretability, privacy, ethics, reliability, and standardization. In the future, AI is expected to significantly enhance prediction accuracy, optimize health management, and advance personalized medicine.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.