arXiv:2607.07761v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This survey examines recent progress in medical LLMs, focusing on reasoning applications and requirements. We present a dual-view approach that connects clinical practice with computational methods. On the clinical side, we establish a five-level competency scheme following Miller's Pyramid, progressing from knowledge recall to dynamic case management. On the computational side, we link deductive, inductiv
The rapid advancement and adoption of large language models (LLMs) are prompting detailed examinations into their practical and ethical integration across critical sectors like healthcare.
This survey provides a framework for integrating LLMs into clinical practice, highlighting both their potential to transform medical reasoning and the competencies required for effective deployment.
The explicit connection between clinical competency levels (Miller's Pyramid) and computational methods provides a structured roadmap for developing and evaluating medical AI, shifting focus from raw capability to practical application and safety.
- · AI developers in healthcare
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted care
- · Medical research institutions
- · Healthcare providers resistant to new tech
- · Traditional medical diagnostic tools
- · Healthcare systems lacking AI infrastructure
Increased investment and research will focus on developing LLMs specifically tailored to the identified clinical competency levels.
New regulatory and accreditation frameworks will emerge to ensure the safe and effective integration of LLMs into clinical practice.
The role of human clinicians may evolve, shifting towards oversight, ethical decision-making, and managing complex cases beyond current AI capabilities.
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Read at arXiv cs.AI