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Agentic AI in rheumatology.

Vincenzo Venerito1, Giuseppe Lopalco1, Florenzo Iannone1

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Agentic artificial intelligence (AI) systems can autonomously chain clinical decisions, posing unique risks in rheumatology. A tiered governance framework is proposed for safe implementation and human oversight.

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Area of Science:

  • Clinical artificial intelligence
  • Agentic AI systems
  • Medical informatics

Background:

  • Large language models (LLMs) are evolving into agentic AI systems capable of autonomous planning and execution.
  • Unlike conversational AI, agentic systems act independently, chaining clinical decisions across diverse data sources.
  • Rheumatology presents unique opportunities and vulnerabilities for agentic AI due to complex disease trajectories and data.

Purpose of the Study:

  • To critically appraise the agentic AI landscape in clinical settings.
  • To analyze the specific applications and inherent risks of agentic AI within rheumatology.
  • To propose a governance framework for ensuring human oversight in agentic AI deployment.

Main Methods:

  • This viewpoint critically appraises the current agentic AI landscape.
  • It analyzes potential applications and risks within the field of rheumatology.
  • A tiered governance framework is proposed for managing agentic AI implementation.

Main Results:

  • Agentic AI represents a distinct technological paradigm shift from conversational AI.
  • Autonomous decision-chaining in rheumatology carries risks of compounding errors and exceeding verification capacities.
  • Automation of foundational clinical tasks may impact the development of clinical expertise in trainees.

Conclusions:

  • Agentic AI holds significant potential for clinical automation, particularly in complex fields like rheumatology.
  • The autonomous nature of these systems necessitates robust verification and governance to mitigate risks.
  • A proposed tiered governance framework aims to ensure meaningful human oversight for safe and effective integration.