Patient Privacy Protection in the AI Era: Policy Priorities for Trustworthy Healthcare Innovation

2026-06-23T14:36:52+00:00

BACKGROUND: The rapid integration of Artificial Intelligence (AI) into healthcare systems holds immense promise for improving diagnostic accuracy, personalizing treatments, and enhancing service delivery. However, this transformation also brings serious concerns regarding patient privacy and the security of sensitive health data.
AIM: This policy review explores the growing tension between AI-driven innovation and the protection of patient privacy. It focuses especially on the different and quickly changing regulatory environments across healthcare systems in Asia.
METHODOLOGY: This policy review synthesizes evidence from peer-reviewed literature, national policy documents, and major digital health initiatives, including India’s Ayushman Bharat Digital Mission (ABDM) and emerging regulatory frameworks in countries such as Iran. It identifies key gaps in current systems for data protection, informed consent processes, and cross-border data governance. These insights are then used to create practical policy recommendations for health and aged care management.
RESULTS: The review shows important governance problems, including limitations of existing regulatory frameworks in addressing AI-specific risks such as data re-identification and secondary data misuse. It also points out fragmented accountability in public–private partnerships and the lack of harmonized regional cybersecurity standards. It also points out divided responsibility in public-private partnerships and a lack of unified regional cybersecurity standards. In response, the paper supports a “governance-first” approach. This approach stresses privacy-by-design technologies, updating legal systems to include independent oversight, flexible informed consent mechanisms, regional data harmonization, and continued investment in adaptive governance and stakeholder education.
CONCLUSION: Protecting patient privacy is not a barrier to progress but a fundamental prerequisite for the trustworthy and sustainable adoption of AI in healthcare. For policymakers and health managers in the Asia-Pacific region, a coordinated, evidence-based strategy is essential, one that nurtures innovation while safeguarding patient autonomy and preserving public confidence in digital health systems.

Artificial Intelligence Approaches for Autism Spectrum Disorder Detection: A Comprehensive Survey of Multimodal Diagnostic Methods

2026-06-23T14:14:40+00:00

Autism spectrum disorder is a neurodevelopmental condition that is characterized by difficulty in social interaction, communication, and repetitive behaviors. It is a complicated disease that affects the brain. It is believed that timely diagnosis and intervention are vital because autism spectrum disorder may be identified early and precisely, which ensures that assistance is offered at the appropriate time and allows the kid to grow healthily. Nevertheless, the diagnostic methods that have been employed historically are mostly subjective, time-consuming, and observational in nature, and they need the expertise of trained doctors in order to arrive at a diagnosis. For the purpose of automating the detection and diagnosis of autism spectrum disorder, several artificial intelligence approaches, such as machine learning and deep learning, have been put through their paces in recent years. Electroencephalography, magnetic resonance imaging, eye-tracking data, face photographs, voice signals, and behavioral questionnaires are some of the types of data that they have used in their research. The objective of this study is to provide a comprehensive review and analysis of the most recent machine learning and deep learning methods that are used in the detection and diagnosis of autism spectrum disorder. At this point, the classification of the ongoing investigations has been accomplished by the utilization of data modalities, machine learning approaches, and diagnostic performance. The rising relevance of multimodal data fusion methods, which may boost diagnostic accuracy and resilience, is highlighted, and the strengths and limits of single-modality approaches are examined. The implications of these techniques are highlighted. Additionally, important research obstacles are explored, including a lack of clinical validation, inadequate data, and the generalization of models. In conclusion, there are some future research avenues that will be taken with the intention of using this as a valid and interpretable diagnostic method for autism spectrum disorder.

Asia Pacific Journal of Health Management

Patient Privacy Protection in the AI Era: Policy Priorities for Trustworthy Healthcare Innovation

Artificial Intelligence Approaches for Autism Spectrum Disorder Detection: A Comprehensive Survey of Multimodal Diagnostic Methods