Abbas Syed Raza, Seol Huiseung, Abbas Zeeshan, Lee Seung Won
Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Healthcare (Basel). 2025 Jul 8;13(14):1642. doi: 10.3390/healthcare13141642.
Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations.
人工智能（AI）正在通过提高诊断精度、自动化临床工作流程以及制定个性化治疗策略来改变智能医疗保健。本综述从两个关键角度探讨了人工智能在医疗保健领域的现状：能力类型（如狭义人工智能和通用人工智能）和功能架构（如有限记忆和心理理论）。基于能力，如今大多数人工智能系统被归类为狭义人工智能，能够高精度地执行特定任务，如医学图像分析和风险预测。更先进的形式，如通用人工智能（AGI）和超级智能人工智能，仍停留在理论阶段，但具有变革潜力。从功能角度来看，有限记忆人工智能通过从历史患者数据中学习以辅助决策，在临床应用中占据主导地位。反应式系统用于基于规则的警报，而心理理论（ToM）和自我意识人工智能仍处于未来发展的概念阶段。这种双重视角提供了一个全面的框架，以评估人工智能在医疗保健领域的成熟度、影响和未来方向。它还强调了随着人工智能系统变得更加复杂和自主，通过纳入跨领域人工智能见解，进行道德设计、提高透明度和加强监管的必要性。此外，我们以韩国为例，评估在区域特定法律和监管框架下开发通用人工智能的可行性，以强调基础设施准备和医疗数据治理法规所带来的限制。
