Gaps in Patient-Facing Information on Artificial Intelligence in Cancer Care: A Cross-Sectional Analysis.

Authors

Pearl Subramanian, Suditi Shyamsunder, Khashayar Eshaghi, Ana I Velazquez, Ronac Mamtani, Henry K Litt

Published in

JCO clinical cancer informatics. Volume 10. Issue 2. Pages e2600114. Epub Jun 18, 2026.

Abstract

Artificial intelligence (AI) is increasingly integrated into cancer care and accessible to patients, yet the quality and accessibility of patient-facing information on this topic are poorly characterized. We evaluated the quality, readability, and AI safety concept coverage of publicly available Webpages and YouTube videos that patients are likely to encounter when searching to learn about AI in cancer care.
We conducted a cross-sectional analysis of digital content identified using Google Trends-derived search terms on August 6, 2025. Two independent reviewers screened the first 170 Google and 150 YouTube results for patient-facing content relevant to AI in cancer care, with discrepancies resolved by a third reviewer. Information quality was assessed using DISCERN (scale 1-5; ≥4 = high quality). Webpage readability was evaluated using Flesch-Kincaid (FK), Gunning Fog (GF), and Simplified Measure of Gobbledygook (SMOG) indices. Coverage of AI safety concepts (hallucination risk, clinician oversight, bias/equity, transparency) was assessed.
Of the 320 resources screened, 52 Webpages (31%) and 30 videos (20%) met inclusion criteria. Median DISCERN scores were 2.5 (IQR, 2.5-4.0) for Webpages and 2.25 (IQR, 1.5-3.0) for videos, indicating overall low-quality information. Only 17 Webpages (33%) and seven videos (23%) were of high quality. Median Webpage readability corresponded to college level across all indices (FK 12.5, GF 15.0, SMOG 14.3), exceeding the sixth- to eighth-grade reading level recommended by the American Medical Association and National Institutes of Health. Most Webpages addressed clinician oversight (79%) and transparency (79%), but few discussed hallucination risk (15%).
Patient-facing online information about AI in cancer care is limited, low in quality, difficult to read, and frequently omits safety concepts, highlighting an urgent need for accessible, high-quality resources.

PMID:
42314075
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.