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

Avishek Choudhury, Zaria Chaudhry

arXiv:2403.14691v2cs.CYcs.AI

TL;DR

Healthcare use of LLMs is growing, while their changing performance and the implications of shifting training data for clinician competence remain concerns. The paper conceptually examines these dynamics and identifies a potential feedback loop linking AI-generated training data, model quality, and clinician skills. It concludes that oversight and informed integration are needed to keep LLMs assistive.

Abstract

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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.

INTRODUCTION

Healthcare LLM deployment is growing, but safe integration remains challenging amid incomplete protocols and evaluation metrics. The paper conceptually examines how clinician trust and shifting training data may affect LLM precision and clinician competence over time.

INTRODUCTION

  • Healthcare AI integration remains incomplete, with deployment protocols unfinished and comprehensive, standardized evaluation metrics lacking.The paper also notes ongoing work on evaluation frameworks and policies addressing ethics, privacy, transparency, and patient safety.Source: INTRODUCTION · p. 3

Advent of generative AI – large language models in healthcare

  • LLMs are already used for healthcare-related tasks, while their stochastic, unpredictable outputs and lack of truth discernment make reliability a concern in this high-stakes setting.The paper notes that plausible but incorrect content can create risks where accuracy and predictability matter.Source: Advent of generative AI – large… · p. 3

Objective

  • The paper conceptually examines how growing clinician trust and shifting training data may affect clinician competence and LLM precision over time.It describes a potential feedback loop in which LLMs trained increasingly on their own outputs may lose quality as user skills decline; these challenges are anticipated, not fully realized.Source: Objective · pp. 3–4

USER EXPERTISE AND TRUST IN LLMS

Clinician trust in LLMs depends partly on expertise and willingness to scrutinize outputs. Expertise can make routine-task delegation productive, but trusting without review and the time required for verification create distinct risks and trade-offs.

USER EXPERTISE AND TRUST IN LLMS

User expertise: ability to detect LLM error

  • Subject-matter experts can delegate routine tasks to LLMs and use their expertise to verify and correct outputs, supporting productivity without introducing risk.The paper gives complex diagnoses and patient triage as examples of work clinicians may have more time to address.Source: User expertise: ability to detect LLM… · p. 5
  • Users lacking relevant expertise may be more vulnerable to accepting persuasive but erroneous LLM suggestions, raising patient-safety concerns.The paper illustrates this with a general practitioner assessing an atypical dermatological case without specialist knowledge to judge the output.Source: User expertise: ability to detect LLM… · p. 5
  • Verification can offset time saved through automation, so productivity and accuracy must be balanced against potential resource strain and longer waits.The paper suggests targeted use in high-impact areas and clearer, more actionable recommendations as possible ways to manage the trade-off.Source: User expertise: ability to detect LLM… · p. 5

User trust: willingness to review LLM output

  • Trust varies with the stakes, autonomy, oversight, and user willingness to review; blind trust may reinforce confirmation or precautionary bias.Agreement with an LLM can narrow consideration of alternatives, while disagreement may lead clinicians to doubt their own expertise.Source: User trust: willingness to review LLM… · p. 6

FUTURE RISK CONSIDERATIONS

The paper frames self-referential learning and deskilling as anticipated risks of deeper healthcare LLM adoption, alongside questions about liability and accountability. It discusses data-quality and oversight measures while emphasizing that clinicians, providers, and institutions each shape safe use.

FUTURE RISK CONSIDERATIONS

  • The paper links broader LLM adoption to concerns about clinician reliance, deskilling, and self-referential learning, while identifying accountability as another issue.These are framed as future risks and challenges rather than established outcomes.Source: FUTURE RISK CONSIDERATIONS · p. 6

LLM paradox of self-referential loop (learning from itself)

  • The self-referential-loop concern arises if widespread AI-generated healthcare content dilutes or obscures original human-generated insights.The paper says this problem has not yet materialized, but could emerge as generative AI advances and proliferates.Source: LLM paradox of self-referential loop… · pp. 6–7
  • Self-referential training could entrench bias, homogenize generated data, and hinder performance; healthcare fine-tuning and quality assurance may mitigate some risks.Even curated datasets can reinforce prevailing clinical approaches, motivating deliberate inclusion of varied, novel human-generated content.Source: LLM paradox of self-referential loop… · p. 7

Risk of deskilling

  • Repeated delegation may reduce skills through less practice, with particular concern that medical students could underdevelop analytical and diagnostic abilities.The paper does not claim current practitioners will definitively deskill; it emphasizes risks for future clinicians who rely on AI in learning and diagnostic review.Source: Risk of deskilling · p. 7

Legal Framework and Liability Allocation

  • Clinicians are generally treated as final decision-makers and bear primary moral and legal responsibility, even as LLM use complicates traditional liability frameworks.Their responsibility is tied to integrating recommendations with patient-specific factors and professional standards.Source: Legal Framework and Liability Allocation · p. 8

Shared Liability and AI Providers

  • Provider liability may depend on training-data accuracy, transparency about system capabilities and limits, and the adequacy of user training and support.LLM providers’ development, deployment, and maintenance roles raise questions about shared liability when system deficiencies contribute to adverse outcomes.Source: Shared Liability and AI Providers · p. 8

Institutional Responsibility

  • Healthcare institutions are responsible for integrating LLMs into workflows in ways that protect patient safety and comply with regulatory standards.Tool selection, clinician training, and oversight mechanisms are identified as institutional practices for mitigating risk.Source: Institutional Responsibility · p. 8

Algorithmic Accountability Act of 2023

  • The cited accountability acts call for standardized assessment procedures and frameworks, with transparency presented as a way to support clinician review of LLM outputs.The passage connects impact assessments with fairness and preventing discriminatory practices.Source: Algorithmic Accountability Act of 2023 · p. 9

CONCLUSION

LLM performance can change as new data are introduced, so healthcare integration requires oversight, ongoing evaluation, and clear limits on the technology’s role. The paper concludes that human oversight can help LLMs complement rather than compromise care.

CONCLUSION

  • LLMs’ current performance does not guarantee future performance, so healthcare use calls for ongoing evaluation, oversight, and an assistive—not standalone—role.The paper describes performance as temporal and subject to change as new data enter the algorithm.Source: CONCLUSION · p. 9

FUNDING

The study reports receiving no internal or external funding.

FUNDING

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