Olawade, David B., David-Olawade, Aanuoluwapo C., Wada, Ojima Z., Asaolu, Akinsola J., Adereni, Temitope and Ling, Jonathan (2024) Artificial Intelligence in Healthcare Delivery: Prospects and Pitfalls. Journal of Medicine, Surgery, and Public Health. p. 100108.

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Abstract

This review provides a comprehensive examination of the integration of Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising a systematic search strategy across electronic databases such as PubMed, Scopus, Embase, and Sciencedirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact on healthcare delivery, including its role in enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating tasks, and driving robotics. AI algorithms demonstrate high accuracy in analysing medical images for disease diagnosis and enable the creation of tailored treatment plans based on patient data analysis. Predictive analytics identify high-risk patients for proactive interventions, while AI-powered tools streamline workflows, improving efficiency and patient experience. Additionally, AI-driven robotics automate tasks and enhance care delivery, particularly in rehabilitation and surgery. However, challenges such as data quality, interpretability, bias, and regulatory frameworks must be addressed for responsible AI implementation. Recommendations emphasise the need for robust ethical and legal frameworks, human-AI collaboration, safety validation, education, and comprehensive regulation to ensure the ethical and effective integration of AI in healthcare. This review provides valuable insights into AI's transformative potential in healthcare while advocating for responsible implementation to ensure patient safety and efficacy.

Item Type:	Article
Status:	Published
DOI:	https://doi.org/10.1016/j.glmedi.2024.100108
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/9893

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