ArticleCureus2023
Clinicians' Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence (AI) - Powered Documentation Systems in Healthcare: A Systematic Review.Journal of medical systems · 2025Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.Frontiers in public health · 2025Pooled it
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- A Competency Framework for Medical AI Education: Mixed Methods Study.JMIR medical education · 2026Article
- Examining developer perspectives on medical AI regulatory frameworks.NPJ digital medicine · 2026Article
- Targeting biofilm-driven antibiotic resistance: emerging mechanisms and next-generation therapeutic interventions.Frontiers in microbiology · 2026Review
- Artificial intelligence in endodontics: A comprehensive review of applications and advancement.Bioinformation · 2026Article
- Healthcare professionals' perspectives on artificial intelligence in clinical practice: a systematic review of facilitators and challenges.Oxford open digital health · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Artificial Intelligence-Driven Wearable and Connected Technology for Allergy: Real-Time Monitoring and Predictive Management for Personalized Care.The journal of allergy and clinical immunology. In practice · 2025Review
- Usability and adoption in a randomized trial of GutGPT a GenAI tool for gastrointestinal bleeding.NPJ digital medicine · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson's Disease Diagnosis and Monitoring.Biomedicines · 2025Review
- AI in Qualitative Health Research Appraisal: Comparative Study.JMIR formative research · 2025Article
- Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals' perspectives in the UK.BMC medical ethics · 2025Article
- ChatGPT in Dental Education: Enhancing Student Proficiency in Informed Consent.Medical science educator · 2025Article
- The Transformative Potential of AI in Ultrasound for Facial Aesthetics.Journal of cosmetic dermatology · 2025Review
- Towards Trustworthy AI in Healthcare: Epistemic Uncertainty Estimation for Clinical Decision Support.Journal of personalized medicine · 2025Article
- Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach.PeerJ. Computer science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it.
methodIn this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features.
resultsThe research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery.
conclusionAI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.
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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.