This paper explores the evolving relationship between clinician trust in
LLMs, the transformation of data sources from predominantly human-generated to
AI-generated content, and the subsequent impact on the precision of LLMs and
clinician competence. One of the primary concerns identified is the potential
feedback loop that arises as LLMs become more reliant on their outputs for
learning, which may lead to a degradation in output quality and a reduction in
clinician skills due to decreased engagement with fundamental diagnostic
processes. While theoretical at this stage, this feedback loop poses a
significant challenge as the integration of LLMs in healthcare deepens,
emphasizing the need for proactive dialogue and strategic measures to ensure
the safe and effective use of LLM technology. Moreover, we delve into the
potential risks associated with LLMs' self-referential learning loops and the
deskilling of healthcare professionals. The risk of LLMs operating within an
echo chamber, where AI-generated content feeds into the learning algorithms,
threatens the diversity and quality of the data pool, potentially entrenching
biases and reducing the efficacy of LLMs. Concurrently, reliance on LLMs for
routine or critical tasks could result in a decline in healthcare providers'
diagnostic and thinking skills, particularly affecting the training and
development of future professionals.

论文探讨了临床医师对 LLMs 的信任与数据来源从人生成为 AI 生成内容的演变关系，并随之对 LLMs 的准确性和临床医师能力的影响。关注的其中一个主要问题是随着 LLMs 对其输出越来越依赖于学习，可能导致输出质量的降低和临床医师技能的减弱，因为与基本诊断过程的接触减少。虽然目前处于理论阶段，但这种反馈循环对于深入整合 LLMs 于医疗保健领域提出了重大挑战，强调了积极对话和战略措施以确保 LLM 技术的安全有效使用的必要性。此外，我们深入探讨了 LLMs 自我参考学习循环和医疗保健专业人员能力下降的潜在风险。LLMs 在回音室内运行的风险，其中 AI 生成的内容反馈到学习算法中，威胁到数据池的多样性和质量，可能固化偏见并降低 LLMs 的效力。同时，对 LLMs 在常规或关键任务方面的依赖可能导致医疗保健提供者的诊断和思考能力下降，特别影响未来专业人员的培训和发展。