# Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals

arXiv: https://arxiv.org/abs/2403.14691 · HTML: https://mlsanity.com/paper/2403.14691/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective](https://mlsanity.com/paper/2304.11215/) (2023)

## Built on by (most cited first, 13 of 13)

- [WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries](https://mlsanity.com/paper/2407.17468/) (2024)
- [Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses](https://mlsanity.com/paper/2405.06712/) (2024)
- [Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM](https://mlsanity.com/paper/2410.14879/) (2024)
- [Fine-Tuning LLMs for Reliable Medical Question-Answering Services](https://mlsanity.com/paper/2410.16088/) (2024)
- [Trust & Safety of LLMs and LLMs in Trust & Safety](https://mlsanity.com/paper/2412.02113/) (2024)
- [Streamlining evidence based clinical recommendations with large language models](https://mlsanity.com/paper/2505.10282/) (2025)
- [If LLMs Have Human-Like Attributes, Then So Does Age of Empires II](https://mlsanity.com/paper/2605.31514/) (2026)
- [User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study](https://mlsanity.com/paper/2502.17487/) (2025)
- [Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows](https://mlsanity.com/paper/2604.26851/) (2026)
- [Bridging LMS and generative AI: dynamic course content integration (DCCI) for enhancing student satisfaction and engagement via the ask ME assistant](https://mlsanity.com/paper/2504.03966/) (2025)
- [Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center](https://mlsanity.com/paper/2506.22523/) (2025)
- [When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption](https://mlsanity.com/paper/2601.21650/) (2026)
- [Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering](https://mlsanity.com/paper/2608.19029/) (2026)

## Abstract (arXiv)

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.