ReviewTurkish journal of medical sciences2024
How to mitigate the risks of deployment of artificial intelligence in medicine?
Review in Turkish journal of medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sustainability of AI-Assisted Mental Health Intervention: A Review of the Literature from 2020-2025.International journal of environmental research and public health · 2025Pooled it
- Recommended guidelines concerning artificial intelligence and health equity: a scoping review.Health affairs scholar · 2026Review
- Review
- An exploratory study of large language models in preoperative anesthesia assessment and planning for cardiac patients undergoing noncardiac surgery.BMC anesthesiology · 2026Article
- Automated chain-of-thought evaluation framework for large language model-generated emergency department documentation: a simulation-based study.Clinical and experimental emergency medicine · 2026Article
- Untold stories of digital transformation in medical education: AI overdependence and nomophobia among medical students.BMC medical education · 2026Article
- AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden.Frontiers in digital health · 2026Article
- Optimistic and ambivalent attitudes toward clinical artificial intelligence.Turkish journal of medical sciences · 2026Article
- Development and Examination of the Psychometric Properties of the Social Perception of Artificial Intelligence in Healthcare Scale in the Turkish Context: Evidence From Hatay Province.International journal of public health · 2026Article
- Bridging generational gaps in medication safety: insights from nurses, students, and generative AI models.BMC nursing · 2025Article
- Review
- Is Artificial Intelligence hallucinating?Turk psikiyatri dergisi = Turkish journal of psychiatry · 2024Article
- Identifying Measurement Dimensions of Users' Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review.Inquiry : a journal of medical care organization, provision and financingArticle
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
The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.
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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.