The paper first notes that there is no universally accepted definition of an AI agent, then compares several views centered on autonomy, perception, decision-making and tool use.

In other words, the paper is not defining Agent as “a better chatbot”, but as a system that can organize and execute multi-step work.

The application section starts with six broad areas in its opening sentence: diagnosis, decision-making, report generation, chatbots/health management, healthcare management and medical education.

The authors explicitly say this classification is inductive and content-driven, rather than a fixed standardized framework.

In assisted diagnosis, the review emphasizes that multi-agent interaction can improve diagnostic accuracy and help correct errors in historical records.

Examples include role-based multidisciplinary discussion, adaptive GP/specialist collaboration, oncology agents using text, imaging, genomics, web search and guidelines, and emergency teams with several clinical roles.

The review describes report generation as one of the earlier healthcare-agent applications, initially aimed at image interpretation and reducing radiologists’ workload.

Drug-management examples extend the same workflow logic to prescription verification, pharmacovigilance and clinical-trial efficacy/safety analysis.

For health management, the review says conversational agents are the predominant form and increasingly handle more complex information.

The paper argues that insufficient evaluation can create diagnostic error, inappropriate treatment advice, extra clinician cross-checking, bias, equity concerns and privacy risks.