AI improves efficiency and outcomes in diagnosis and treatment but requires ethical governance and data safeguards.
Context
To review and classify recent studies on AI in healthcare, highlighting benefits, challenges, ethical issues and social sustainability.
Methods
- Approach: Systematic review using Webster & Watson methodology.
- Data Sources: Scopus, Web of Science, PubMed.
- Selection: 132 peer-reviewed articles analysed.
- Framework: Four dimensions—AI healthcare activities, advantages/disadvantages, ethical issues and social sustainability.
Results
- Applications: AI widely used in diagnosis, treatment, remote patient monitoring, hospital management, and fraud detection.
- detection.
- Benefits: Faster decision-making, cost reduction, improved patient outcomes, and workflow optimisation.
- patient outcomes, and workflow optimisation.
- Challenges: Data quality, integration, privacy concerns, ethical issues and lack of standardisation.
- Emerging Trends: Use of machine learning, telehealth, and DNA testing for rapid diagnostics.
Reference
IKitsios, F., Kamariotou, M., Syngelakis, A. I., & Talias, M. A. (2023). Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review. Applied Sciences, 13(7479).
