Comment on Megat Ramli et al. - A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays - Author Letters

Sep 30, 2026

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Letter to the authors of a systematic review comparing artificial intelligence (AI) with radiologists for lung-nodule detection on chest X-rays, focusing on factors affecting performance and patterns of missed nodules. The letter argues that higher AI sensitivity may increase false positives, creating resource burdens through follow-up CT, extra visits, and greater patient anxiety. It calls for evaluations beyond sensitivity and specificity by analyzing downstream workflow and patient outcomes via study designs such as stepped-wedge cluster randomized trials.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Background and key observations

Sensitivity vs false positives

Measuring true clinical value

Recommendations for clinical alignment

Author contributions and conflicts of interest

Abbreviations and references

Comment Comment on Megat Ramli et al. A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays. Diagnostics 2025, 15, 246 Aybaniz Ismayilli 1, *, Jan Hansel 2 and Emmanuel Erhieyovwe 1 Academic Editor: Andreas Kjaer Received: 15 September 2025 Accepted: 11 December 2025 Published: 29 December 2025 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. 1 Research and Innovation, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK; [emmanuel.erhieyovwe@mft.nhs.uk](emmanuel.erhieyovwe@mft.nhs.uk) 2 Division of Immunology, Immunity to Infection and Respiratory Medicine, The University of Manchester, Manchester M13 9WL, UK; [jan.hansel@manchester.ac.uk](jan.hansel@manchester.ac.uk) * Correspondence: [aybaniz.ismayilli@mft.nhs.uk](aybaniz.ismayilli@mft.nhs.uk) We read with interest the review by Megat Ramli et al. [1], which systematically compares artificial intelligence (AI) with radiologists for lung-nodule detection on chest x-rays (CXR). The review explores factors that influence performance (size, morphology, location) and summarizes patterns of missed nodules. The review also provides a summary of AI performance, noting that AI frequently achieves a higher performance than radiologists [1] . The review correctly notes that high sensitivity of AI can be accompanied by an increased number of false-positive findings. In settings where prevalence is low, even a small increase in the false-positive rate can translate into a significant burden on healthcare resources, leading to unnecessary follow-up computed tomography (CT) scans, additional clinic visits, and heightened patient anxiety [2] . While the review addresses the technical performance of AI, it does not fully explore how the high false-positive rate can offset the benefits of improved sensitivity. The true value of AI in a clinical setting must be measured not just by its algorithmic performance but by its overall impact on the healthcare system and patient outcomes. We believe that future reviews and studies should move beyond a sole focus on sensitivity and specificity and provide a more thorough analysis of the downstream effects of AI-generated false positives. This would provide a complete and more accurate picture of AI’s true role in lung cancer screening. This analysis can be achieved by using specific study designs, such as stepped-wedge cluster randomized trials, which assess the real-world impact of AI on clinical workflow and downstream patient outcomes—as demonstrated in a recent study protocol [3] . To align technical performance with clinical value, we suggest readers designing or evaluating services may wish to assure the following principles. AI should be used in an assistive rather than autonomous manner, deployed as a second reader, with particular attention to commonly missed zones [4] . Operating thresholds should be balanced and clearly documented, ensuring that sensitivity gains are not achieved at the cost of disproportionate false positives [5] . Routine monitoring should be implemented with simple recording of the actions following AI alerts (such as further imaging or clinic review) to make workload and patient impact visible [6] . Reporting should be stratified by nodulesize, morphology, and anatomical location, reflecting where performance differs [1] . The above are general reporting and governance habits. They help readers interpret sensitivity and specificity alongside practical consequences without introducing new data. AI can add real value as an assistive tool, especially for readers with less experience, but variability and activity arising from false-positive findings must be pro-actively managed. Presenting technical accuracy together with clear information on downstream acti

FAQs

What primary comparison does the referenced review make?

It systematically compares artificial intelligence (AI) with radiologists for lung-nodule detection on chest X-rays (CXR).

Why can increased AI sensitivity be problematic in low-prevalence settings?

Higher sensitivity can coincide with more false positives; when prevalence is low, even small false-positive increases can meaningfully burden healthcare resources and increase unnecessary follow-up CT, visits, and patient anxiety.

How should future research assess AI’s true clinical value?

By moving beyond sole focus on sensitivity and specificity and analyzing downstream effects on healthcare workflow and patient outcomes, using study designs such as stepped-wedge cluster randomized trials.

Sep 30, 2026

Uploaded by Aurelia