ReviewJournal of multidisciplinary healthcare2024
Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.
Review in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Measuring AI literacy in medical students: scale development and validation within a self-determination theory framework.Medical education online · 2026Article
- Deep-learning segmentation to guide bladder neck recognition in robot-assisted radical prostatectomy.BJU international · 2026Article
- Initial Implementation of E-Prescribing: Evidence from Eastern Taiwan.Healthcare (Basel, Switzerland) · 2026Article
- Exploring the role of generative artificial intelligence in enhancing clinical skills training: A bibliometric analysis.Medicine · 2026Article
- Artificial Intelligence and its Current Role in Clinical Outcome Prediction, Musculoskeletal Imaging, and Economic and Ethical Considerations within Orthopedics and Sports Medicine.Current reviews in musculoskeletal medicine · 2026Review
- Artificial intelligence analysis of minimally invasive surgery data.Journal of robotic surgery · 2026Review
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Machine Learning-Based Algorithm for Tacrolimus Dose Optimization in Hospitalized Kidney Transplant Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Recent advances in diagnostic technologies for postoperative central nervous system infections: a review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- Comparative Analysis of Generative Artificial Intelligence Systems in Solving Clinical Pharmacy Problems: Mixed Methods Study.JMIR medical informatics · 2025Article
- Review
- AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes.Dentistry journal · 2025Review
- AI-driven predictive modelling of orthodontic relapse using retainer compliance and patient factors.Bioinformation · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. Main Applications: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.
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.