Artificial Intelligence-Related Risks in Interventional Pulmonology: An Exploratory Enumeration and Ranking Study Across Five General-Purpose Large Language Models

Abstract

Background/Objectives: Artificial intelligence (AI) is entering interventional pulmonology (IP) faster than its potential risks have been systematically catalogued. We explored whether general-purpose large language models (LLMs), now widely consulted informally by patients and clinicians, could provide a rapid and structured means of enumerating and ranking candidate AI-related risks in IP, potentially contributing to risk awareness and hypothesis generation. Methods: Five LLMs (ChatGPT, Claude, Gemini, Grok, DeepSeek) were each queried once, in independent, memory-free sessions, with one standardised prompt requesting ten ranked AI-related risks with impact and likelihood scores (1–5); an informal repeat administration was performed, but output stability was not formally assessed. The resulting 50 risk statements were inductively coded, by an AI coder with independent human validation by two reviewers, into 15 constructs nested in 8 higher-order domains. Results: Mean self-assigned impact was 3.92 (SD 0.78) and mean likelihood 3.54 (SD 0.68). The eight domains comprised AI technical/perceptual accuracy (diagnostic, detection and navigational error); automation bias and over-reliance; generalisability, algorithmic bias and health equity; model and system reliability over time; erosion of procedural competence; explainability, transparency and accountability; cybersecurity, privacy and data integrity; and cognitive and workflow burden. Five domains were raised by all five models and the remaining three by four of five. Two domains-AI technical/perceptual accuracy and automation bias-together accounted for every model’s two highest-ranked risks, and no risk statement outside these two domains was ranked first or second by any model. Deskilling (erosion of procedural competence), although listed by all five models, was never ranked above third by any model, whereas practising IP specialists in our prior international survey rated it the single highest research priority. Conclusions: These exploratory and hypothesis-generating findings suggest that general-purpose LLMs may have potential as a rapid, low-burden means of enumerating and ranking candidate AI-related risks and broadening awareness of issues that may warrant further investigation in IP. The lower ranking of deskilling by the LLMs than by domain experts indicates that LLM-generated rankings may under-prioritise risks that clinicians consider most important. Although their outputs are not validated measures of clinical risk and do not replace expert appraisal, we believe they may provide a starting point for subsequent human-led risk assessment and prioritization in an area of research that remains largely unexplored yet is highly relevant to patient safety.