Background: Artificial intelligence–based clinical decision support systems (AI-CDSSs) have enhanced personalized medicine and improved the efficiency of health care workers. Despite these opportunities, trust in these tools remains a critical factor for their successful integration into practice. Existing research lacks synthesized insights and actionable recommendations to guide the development of AI-CDSSs that foster trust among health care workers. Objective: This systematic review aims to identify and synthesize key factors that influence health care workers’ trust in AI-CDSSs and to provide actionable recommendations for enhancing their trust in these systems. Methods: We conducted a systematic review of published studies from January 2020 to November 2024, retrieved from PubMed, Scopus, and Google Scholar. Inclusion criteria focused on studies that examined health care workers’ perceptions, experiences, and trust in AI-CDSSs. Studies in non–English languages and those unrelated to health care settings were excluded. Two independent reviewers followed the Cochrane Collaboration Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Analysis was conducted using a developed data charter. The Critical Appraisal Skills Programme tool was applied to assess the quality of the included studies and to evaluate the risk of bias, ensuring a rigorous and systematic review process. Results: A total of 27 studies met the inclusion criteria, involving diverse health care workers, predominantly in hospitalized settings. Qualitative methods were the most common (n=16, 59%), with sample sizes ranging from small focus groups to cohorts of over 1000 participants. Eight key themes emerged as pivotal in improving health care workers’ trust in AI-CDSSs: (1) System Transparency, emphasizing the need for clear and interpretable AI; (2) Training and Familiarity, highlighting the importance of knowledge sharing and user education; (3) System Usability, focusing on effective integration into clinical workflows; (4) Clinical Reliability, addressing the consistency and accuracy of system performance; (5) Credibility and Validation, referring to how well the system performs across diverse clinical contexts; (6) Ethical Consideration, examining medicolegal liability, fairness, and adherence to ethical standards;(7) Human Centric Design, pioritizing patient centered approaches; (8) Customization and Control, highlighting the need to tailor tools to specific clinical needs while preserving health care providers’ decision-making autonomy. Barriers to trust included algorithmic opacity, insufficient training, and ethical challenges, while enabling factors for health care workers’ trust in AI-CDSS tools were transparency, usability, and clinical reliability. Conclusions: The findings highlight the need for explainable AI models, comprehensive training, stakeholder involvement, and human-centered design to foster health care workers’ trust in AI-CDSSs. Although the heterogeneity of study designs and lack of specific data limit further analysis, this review bridges existing gaps by identifying key themes that support trust in AI-CDSSs. It also recommends that future research include diverse demographics, cross-cultural perspectives, and contextual differences in trust across various health care professions.

Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored. This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs. A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024. The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency. Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Armed conflicts and military offensives after the 2021 coup in Myanmar have resulted in the internal displacement of almost 3.5 million people as

The ageing and growing population, rising cancer rate, and increased healthcare demand drive higher medical costs. Integrating Artificial Intelligence (AI) into oncology revolutionizes cancer diagnosis, treatment, and management, promising enhanced efficiency and precision. Despite potential for healthcare cost savings from implementing AI system in healthcare, its successful integration depends on stakeholder acceptance, particularly physician adoption and integration into routine clinical practice. This scoping review aimed to systematically assess recent literature on health economic evaluations (HEEs) of artificial AI applications in oncology and explore the economic risks and benefits of integrating AI systems into oncology. A scoping review was conducted using PRISMA-ScR guidelines across databases, including PubMed, Scopus, and Google Scholar for articles published between January 1, 2019, and June 30, 2024, in English. Eligible studies focused on the economic aspects of AI applications in oncology, including cost-effectiveness analyses, budget impact studies, and evaluations of economic benefits in clinical practice. Two independent reviewers used CHEERS-AI and Philips checklists for data extraction, quality assessment and analysis. Out of 870 studies identified,12 studies were selected which focused on colorectal (5), breast (2), lung (2), cervical (1), and prostate (1) cancers. Most emphasis is on early-stage care (N = 10,83%) and cost-effectiveness analysis (N = 9,75%) was predominant along with Markov model being the most common approach. The studies used a healthcare system perspective (N = 6,50%) and examined AI's cost-effectiveness in medical imaging (N = 5,42%) and biomarkers (N = 4,33%). Time horizons varied from less than a year to a lifetime, with most applying a 3% discount rate. AI demonstrated economic benefits, improved diagnostic sensitivity,potential cost reduction, workflow efficiency, and treatment optimization while presenting risks like reimbursement challenges, data security concerns, and potential error costs. The scoping review highlights the necessity for thorough health economic evaluations of AI integration in oncology. Although AI technologies are cost-effective, there is a gap in user perspectives and considerations of health equity, regulation, and ethics. To maximize its benefits, future research should include a comprehensive economic evaluation of a more diverse population for the effective adoption of AI into the healthcare system. The online version contains supplementary material available at 10.1186/s13561-026-00751-x.