Medical and health insurance provides financial protection against the consequences of the occurrences of health risks. Different perceptions have led to resistance to change, as well as the acceptance level from Malaysians. It is fundamental to study the reception and acceptance of all types of insurance schemes by Malaysians and to identify the knowledge and information pertaining to reception by Malaysians. Methods A systematic search was performed from six major search engines from 2013– 2018 in searches of published articles on factors that influence the demand or willingness to pay for health insurance among Malaysians. There were nine articles included, in which personal factor was found to influence the most when demanding for health insurance. Results Higher education level, younger age group, and good knowledge were associated with higher demand for health insurance. Higher household monthly income and the cost to pay were among the positively significant economic determinant factors to demand for medical insurance. Conclusions Education and promotion are important to understand why such policies were introduced and how they intend to serve the public before implementing major policies. These elements capture the essence of ‘health promotion’, which is about enabling people to take control over their health and thereby, improving their health. .

The application of artificial intelligence (AI) is on the rise in the healthcare industry. However, the study on the physicians’ perspectives is still lacking. The study aimed to examine physicians’ attitudes, expectations, and concerns regarding the application of AI in medicine. A cross-sectional study was conducted in October 2019 among physicians in a tertiary teaching hospital in Malaysia. The survey used a validated questionnaire from the literature, which covered: (1) socio-demographic profile; (2) attitude towards the application of AI; (3) expected application in medicine; and (4) possible risks of using AI. Comparison of the mean score between the groups using a t-test or one-way analysis of variance (ANOVA). A total of 112 physicians participated in the study: 64.3% from the clinical departments; 35.7% from the non-clinical specialties. The physicians from non-clinical departments had significantly higher mean attitude score (mean = 14.94 ± 3.12) compared to the clinical (person-oriented) departments (mean = 14.13 ± 3.10) and clinical (technique-oriented) departments (mean = 13.06 ± 2.88) (p = 0.033). The tech-savvy participants had a significantly higher mean attitude score (mean = 14.72 ± 3.55) than the non–tech-savvy participants (mean = 13.21 ± 2.46) (p = 0.01). There are differences in the expectations among the respondents and some concerns exist especially on the legal aspect of AI application in medicine. Proper training and orientation should precede its implementation and must be appropriate to the physicians’ needs for its utilization and sustainability.