Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals¶
Arxiv Link - 2024-04-01 05:03:45
Abstract¶
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. A key takeaway from our investigation is the critical role of user expertise and the necessity for a discerning approach to trusting and validating LLM outputs. The paper highlights how expert users, particularly clinicians, can leverage LLMs to enhance productivity by offloading routine tasks while maintaining a critical oversight to identify and correct potential inaccuracies in AI-generated content. This balance of trust and skepticism is vital for ensuring that LLMs augment rather than undermine the quality of patient care. 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.
Socials¶
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| 🚀 Just published! Dive into the complex dynamics between clinician trust in LLMs, evolving data sources, and the impact on precision and competence in healthcare. Explore the potential risks of feedback loops and deskilling, emphasizing the crucial role of proactive dialogue and user expertise. Stay informed with the full paper here: http://arxiv.org/abs/2403.14691v2 #AI #LLMs #Healthcare #TechInnovation 📈🔍 | 🔍 New research alert! Explore the impact of clinician trust in LLMs on data sources and clinician competence in healthcare. Uncover the risks of feedback loops and deskilling in AI integration. Proactive dialogue is key. Access the full study here: http://arxiv.org/abs/2403.14691v2 #AI #LLMs #healthtech #research |