To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries

Abstract

Purpose: As online health information-seeking surges, concerns mount over the quality and safety of accessible content, potentially leading to patient harm through misinformation. On one hand, the emergence of Artificial Intelligence (AI) in healthcare could prevent it; on the other hand, questions raise regarding the quality and safety of the medical information provided. As laryngeal cancer is a prevalent head and neck malignancy, this study aims to evaluate the utility and safety of three large language models (LLMs) as sources of patient information about laryngeal cancer.

Methods: A cross-sectional study was conducted using three LLMs (ChatGPT 3.5, ChatGPT 4.0, and Bard). A questionnaire comprising 36 inquiries about laryngeal cancer was categorised into diagnosis (11 questions), treatment (9 questions), novelties and upcoming treatments (4 questions), controversies (8 questions), and sources of information (4 questions). The population of reviewers consisted of 3 groups, including ENT specialists, junior physicians, and non-medicals, who graded the responses. Each physician evaluated each question twice for each model, while non-medicals only once. Everyone was blinded to the model type, and the question order was shuffled. Outcome evaluations were based on a safety score (1-3) and a Global Quality Score (GQS, 1-5). Results were compared between LLMs. The study included iterative assessments and statistical validations.

Results: Analysis revealed that ChatGPT 3.5 scored highest in both safety (mean: 2.70) and GQS (mean: 3.95). ChatGPT 4.0 and Bard had lower safety scores of 2.56 and 2.42, respectively, with corresponding quality scores of 3.65 and 3.38. Inter-rater reliability was consistent, with less than 3% discrepancy. About 4.2% of responses fell into the lowest safety category (1), particularly in the novelty category. Non-medical reviewers' quality assessments correlated moderately (r = 0.67) with response length.

Conclusions: LLMs can be valuable resources for patients seeking information on laryngeal cancer. ChatGPT 3.5 provided the most reliable and safe responses among the models evaluated.

Keywords: Artificial intelligence; Bard; ChatGPT; Laryngeal cancer; Oncology; Patient education.

Fig. 1

Quality score assessment—legend

Fig. 2

A flowchart with methodological steps

Fig. 3

Answer word count length—the distribution among the models

Fig. 4

Likert plot visualizes responses to quality and safety according to category. Dark blue maximum score, light blue 4 out of 5 points in quality score, green medium value, light red 2 out of 5 points in quality score, and dark red minimum score. Q stands for quality, and S for safety