AI and Cancer Research ── Cancer biology ── 2026-10-17
Cancer molecular biology and AI
- Abstract
Introduction: Lung cancer remains a major cause of cancer-related death; prompt detection and monitoring of pulmonary nodules through computed tomography (CT) is critical to prevent mortality. CT interpretation is time-intensive and can be prone to fatigue-related errors. The application of artificial intelligence (AI) may aid in the enhanced detection of pulmonary nodules when used in conjunction with radiologists. Thus, possibly decreasing interpretation time and enhancing diagnostic reliability. However, studies have reported uncertainty regarding the extent to which true positives are always diagnosed, as studies have indicated an increase in false positive rates with AI-assisted pulmonary nodule detection. This systematic review and meta-analysis focuses on the impact of AI assistance on sensitivity and false positive rates in comparison to unaided reading. Methodology: Adhering to the PRISMA guidelines, an intensive literature search was carried out on Cochrane, PubMed, Medline and Embase. Research composed of studies that compared AI-assisted reading to unaided radiologists in the context of nodule detection, which also provided additional outcomes that were utilised in the secondary outcome analysis. ROBINS-1 tool was used to assess risk of bias and data quality. Risk ratios and risk differences with 95% CI were reported for dichotomous outcomes, whereas mean differences with 95% CI were utilised for continuous outcomes. Sensitivity analyses were performed for the imputation of missing standard deviations.
Results: Seven studies reported mean sensitivity in either second reader (SR) mode or concurrent reader (CR) mode. Five of the included studies implemented second reader designs; AI-assisted sensitivity ranged from 38% to 85.66%, whereas unassisted reading sensitivity ranged from 20.9% to 73.46%. Additionally, three studies reported concurrent reading, with AI-assisted reading ranging from 80% to 84%, while unassisted reading varied from 64% to 72%. According to our research, AI-assisted reading had a significantly higher sensitivity (RR of 1.39; CI 95% 1.08–1.79; p = 0.01; I2 = 95%). Five studies reported false positives per scan. Four reported a numerical increase with AI assistance, but incomplete variance reporting and study heterogeneity precluded quantitative pooling. Reading time was increased under AI assistance as a second reader (MD 77.54; CI 95% −57.50 to 212.6, p = 0.132; I2 = 64.9%) but did not yield significance. Additionally, AI-assisted reading under concurrent reading did not yield statistical significance for reading time (MD –11.39; 95% CI −498.7 to 476.0, p = 0.816; I2 = 41.3%).
Conclusions: AI-assisted reading significantly improves sensitivity for pulmonary nodule detection compared to unaided reading, but the trade-offs with false positives and workflow disruption remain uncertain. Substantial heterogeneity and the limited number of studies preclude definitive clinical recommendations. These findings should be considered exploratory, and the implementation of AI in lung cancer screening requires prospective validation.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Singh V, Gunda S, Shah A, Mathew J, Ravendran K. Impact of Artificial Intelligence Assistance on Radiologist Performance in CT-Based Pulmonary Nodule Detection: A Systematic Review and Meta-Analysis of Detection Accuracy Studies. J Imaging. 2026 Oct 14;12(10):509. doi:10.3390/jimaging12100509.Checked: Abstract only - Abstract
Background: Brain imaging is central to the structural, functional, metabolic, and neurophysiological characterization of neurological disease, yet interpretation remains constrained by heterogeneity in acquisition, annotation, expertise, and clinical context.
Objective: This systematic literature review evaluates recent artificial intelligence approaches for brain imaging and computer-aided diagnosis, with emphasis on methods, modalities, neurological diseases, diagnostic tasks, datasets, validation practices, and translational limitations.
Methods: The review followed PRISMA 2020 and covered peer-reviewed English-language studies published from January 2020 through June 2026. Eight information sources were searched using database-specific terms for artificial intelligence, neuroimaging, neurological disease, and diagnostic analysis. The search identified 1,078 records; 236 duplicates were removed, 842 unique records were screened, 258 full texts were assessed, and 123 studies were included after a record-level eligibility re-audit. Heterogeneous evidence was synthesized narratively and by descriptive mapping rather than meta-analysis.
Results: MRI was frequently represented in the included studies and auxiliary mapping data; CT was commonly used in acute stroke and intracranial hemorrhage; PET supported selected molecular and differential-diagnosis studies; fMRI and diffusion imaging were used for network and white-matter analysis; and EEG extended the scope to neurophysiological diagnosis. CNN and three-dimensional networks were commonly reported, while attention, Transformer, graph, self-supervised, generative, multimodal, and foundation-model approaches appeared in selected recent studies. Stroke, dementia, and brain tumors were frequently represented, but heterogeneous populations, outcomes, and incomplete validation extraction precluded ranking diseases, modalities, architectures, or validation strategies.
Conclusion: The evidence suggests movement from task-specific pattern recognition toward multimodal and context-rich decision support in selected settings, but it does not establish uniform methodological superiority or clinical maturity. Dependable translation requires patient-level data governance, transparent reporting, uncertainty estimation, independent multicenter testing, prospective workflow evaluation, and clinically meaningful endpoints.
Journal IF-equivalent: 3.6 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Omarov B. Advances in artificial intelligence for brain imaging and computer-aided diagnosis of neurological diseases: a systematic literature review. Front Hum Neurosci. 2026 Oct 14;20:1963855. doi:10.3389/fnhum.2026.1963855.Checked: Abstract only