Responsible artificial intelligence in healthcare: a systematic review on the use of ethical principles in the development and deployment of artificial intelligence.

OBJECTIVE: As hospitals increasingly adopt artificial intelligence (AI) to manage rising patient volumes, workforce shortages and healthcare costs, concerns about ethical implementation have become prominent. This systematic review aims to assess how hospital-focused AI literature addresses the WHO's six ethical AI principles-autonomy; well-being and safety; transparency and explainability; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability. METHODS AND ANALYSIS: A systematic review (PROSPERO registration: CRD42022347871) was conducted by searching Embase, MEDLINE ALL, Web of Science and the Cochrane Central Register of Controlled Trials from inception to December 2023, supplemented by Google Scholar. English-language studies describing AI (machine learning, deep learning, predictive analytics) relevant to inpatient settings and referencing at least one WHO principle were included. Two reviewers independently screened titles, abstracts and full texts, extracting data on publication year, country, study design, AI type, technology readiness level and ethical considerations. Discrepancies were resolved by consensus. RESULTS: Of 4770 unique records, 673 were included. Most (83%) originated from high-income countries, with publication volume rising sharply after 2021. Of these, 558 (83%) addressed at least one WHO principle in depth, most frequently inclusiveness and equity (49%), transparency and explainability (45%) and autonomy (42%). Well-being and safety (26%) and responsibility and accountability (29%) were less frequently covered, while responsiveness and sustainability (6%) was rarely explored. Among 44 studies developing AI applications with technology readiness levels 1-6, ethical principles were acknowledged but rarely operationalised. CONCLUSION: Hospital-based AI research demonstrates increasing attention to ethical principles but lacks comprehensive application, particularly regarding sustainability. High-income countries dominate this discourse, underscoring the need for broader global engagement. To achieve equitable, safe and sustainable AI in clinical practice, clearer operational guidance and more inclusive collaboration is warranted.