The healthcare sector is faced with challenges due to a shrinking healthcare workforce and a rise in chronic diseases that are worsening with demographic and epidemiological shifts. Digital health interventions that include artificial intelligence (AI) are being identified as some of the potential solutions to these challenges. The ultimate aim of these AI systems is to improve the patient’s health outcomes and satisfaction, the overall population’s health, and the well-being of healthcare professionals. The applications of AI in healthcare services are vast and are expected to assist, automate, and augment several healthcare services. Like any other emerging innovation, AI in healthcare also comes with its own risks and requires regulatory controls. A review of the literature was undertaken to study the existing regulatory landscape for AI in the healthcare services sector in developed nations. In the global regulatory landscape, most of the regulations for AI revolve around Software as a Medical Device (SaMD) and are regulated under digital health products. However, it is necessary to note that the current regulations may not suffice as AI-based technologies are capable of working autonomously, adapting their algorithms, and improving their performance over time based on the new real-world data that they have encountered. Hence, a global regulatory convergence for AI in healthcare, similar to the voluntary AI code of conduct that is being developed by the US-EU Trade and Technology Council, would be beneficial to all nations, be it developing or developed.

Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection—measures to cover the “digital health footprints” left unknowingly by patients when they access AI in health services; (ii) data quality—availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms—mapping of the explainability and causability of the AI system; (iv) accountability—whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access—whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.

Purpose: The aim of this study was to determine the effectiveness of promoting peer support to reduce depression, anxiety and stress among migrant construction workers in Singapore.Design/methodology/approach: This longitudinal study drew participants from migrant workers of various nationalities in the construction sector in Singapore. Baseline data pertaining to depression, anxiety and stress was established using the DASS-21 questionnaire, and salient covariates such as demographic factors and work environment factors recorded using suitable questionnaires. Intervention was training of participants on peer support techniques, supplemented by episodic support by trained counsellors. At the end of 6 months, DASS-21 was again deployed to obtain the post-results. Comparison of baseline with post-results data was performed to evaluate effectiveness of the peer support intervention.Findings: Statistically significant reduction was observed in measures of all the three parameters studied, namely, depression, anxiety and stress. A decrease of 3.3 (95% CI:2.3 to 4.3) points in mean depression score, a decrease of 2.6 (95% CI: 1.6 to 3.7) points in mean anxiety score and a decrease of 2.7 (with 95% CI: 1.6 to 4.0) points in mean stress scores on the DASS-21 scale were recorded. Conclusions: Peer support is effective in improving mental health of migrant workers in the construction sector in Singapore. This intervention should be considered among other measures to improve their welfare.Originality/value: This is the first paper that talks about the mental health of migrant workers pre-COVID and hence would be a strong paper for the future comparative studies for pre-and post-COVID periods. This is the first paper that addresses the benefits of peer-support among migrant workers to improve their mental wellbeing.

The aim of this study was to establish the prevalence of depression, anxiety and stress among foreign workers in the construction industry in Singapore.This cross-sectional study used convenience s...