ArticleJournal of personalized medicine2023
Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses 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
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Cost-Effectiveness of Virtual Emergency Care Models: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- Perceptions of, Barriers to, and Facilitators of the Use of AI in Primary Care: Systematic Review of Qualitative Studies.Journal of medical Internet research · 2025Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
- Digital chat-based care assessments in primary healthcare: nurses' work experiences and training needs.Scandinavian journal of primary health care · 2025Article
- Understanding Acceptability of AI Triage Tools Amongst Underserved Populations: Lessons From the Early Phases of Co-Production With Bangladeshi Communities in Birmingham, UK.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Cost-effectiveness of virtual emergency care models: A protocol for a systematic review.PloS one · 2025Article
- Examining South Tyrol's Experience: Digital Health Adoption and Workforce Issues in Implementing Italy's Primary Care Reform Under Ministerial Decree No. 77/2022.Epidemiologia (Basel, Switzerland) · 2024Review
- [Digital health apps : How to achieve the successful integration into healthcare].Innere Medizin (Heidelberg, Germany) · 2024Review
- Mixed methods assessment of the influence of demographics on medical advice of ChatGPT.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration.Journal of personalized medicine · 2024Article
- Personalized Treatments Based on Laryngopharyngeal Reflux Patient Profiles: A Narrative Review.Journal of personalized medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern healthcare is facing a juxtaposition of increasing patient demands owing to an aging population and a decreasing general practitioner workforce, leading to strained access to primary care. The coronavirus disease 2019 pandemic has emphasized the potential for alternative consultation methods, highlighting opportunities to minimize unnecessary care. This article discusses the role of artificial-intelligence-driven symptom checkers, particularly their efficiency, utility, and challenges in primary care. Based on a study conducted in Italian general practices, insights from both physicians and patients were gathered regarding this emergent technology, highlighting differences in perceived utility, user satisfaction, and potential challenges. While symptom checkers are seen as potential tools for addressing healthcare challenges, concerns regarding their accuracy and the potential for misdiagnosis persist. Patients generally viewed them positively, valuing their ease of use and the empowerment they provide in managing health. However, some general practitioners perceive these tools as challenges to their expertise. This article proposes that artificial-intelligence-based symptom checkers can optimize medical-history taking for the benefit of both general practitioners and patients, with potential enhancements in complex diagnostic tasks rather than routine diagnoses. It underscores the importance of carefully integrating digital innovations while preserving the essential human touch in healthcare. Symptom checkers offer promising solutions; ensuring their accuracy, reliability, and effective integration into primary care requires rigorous research, clinical guidance, and an understanding of varied user perceptions. Collaboration among technologists, clinicians, and patients is paramount for the successful evolution of digital tools in healthcare.
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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.