Artificial intelligence (AI) has emerged as a transformative technology in modern medical treatment, providing significant advances in disease diagnosis, clinical decision-making, medical imaging, drug discovery, and personalized healthcare. However, the rapid integration of AI into healthcare systems has generated various social and ethical challenges, including concerns related to patient privacy, algorithmic bias, transparency, accountability, data governance, and regulatory uncertainty. These challenges have created an urgent need to understand the global research landscape regarding the responsible implementation and governance of AI in medical treatment.

This study aims to investigate the development trends, knowledge structures, research hotspots, and emerging frontiers in the field of social risks and regulatory governance of artificial intelligence in medical treatment through a CiteSpace-based bibliometric analysis. Publications related to AI, healthcare risks, ethics, and regulation were retrieved from the Web of Science Core Collection database. CiteSpace software was applied to conduct scientific mapping, including publication trend analysis, country and institutional collaboration analysis, keyword co-occurrence analysis, citation analysis, and burst detection.

The findings demonstrate that research on AI governance in medical treatment has expanded rapidly, particularly after 2015, with increasing attention toward ethical AI, trustworthy artificial intelligence, explainable AI, healthcare data protection, and regulatory frameworks. Early research mainly concentrated on technological development and clinical applications, while recent studies have shifted toward social responsibility, governance mechanisms, and international regulatory cooperation. This study provides a comprehensive understanding of global research evolution and highlights future directions for developing safe, transparent, and human-centered artificial intelligence systems in healthcare.

The rapid advancement of artificial intelligence (AI) has created significant opportunities for innovation in life sciences, including precision medicine, drug discovery, disease prediction, biomedical research, and personalized healthcare. However, the successful development and implementation of AI models depend heavily on the availability of high-quality, diverse, and ethically managed medical datasets. Although healthcare data are continuously generated through hospitals, clinical studies, and digital health platforms, concerns related to privacy, security, ownership, and ethical use often limit voluntary participation in medical data sharing.

This study proposes a voluntary medical data sharing framework designed to support artificial intelligence applications in life sciences while ensuring individual autonomy, transparency, privacy protection, and responsible data governance. The proposed framework integrates patient consent mechanisms, secure data management systems, ethical oversight, incentive models, and artificial intelligence-based research infrastructure. The framework aims to establish a trust-based ecosystem where individuals can voluntarily contribute medical information for scientific advancement while maintaining control over their personal health data.

The study discusses key challenges associated with voluntary medical data sharing and presents a structured approach for developing sustainable AI-driven life science research environments. The proposed model may support future collaborations among patients, healthcare institutions, researchers, and technology developers by promoting responsible data utilization and accelerating biomedical innovation.

The increasing complexity of biomedical research requires academic medical institutions to adopt advanced computational approaches for improving research productivity, collaboration, and innovation. Data science provides opportunities to transform healthcare research by enabling efficient management of large-scale clinical datasets, predictive analytics, artificial intelligence (AI)-driven discoveries, and evidence-based decision-making. This conceptual article proposes a framework for integrating data science capabilities within research-intensive medical institutions. The proposed framework focuses on five major components: data infrastructure development, researcher training, interdisciplinary collaboration, ethical data governance, and sustainable innovation ecosystems. By strengthening institutional data science capacity, medical colleges can improve translational research outcomes, accelerate scientific discoveries, and enhance healthcare innovation.