Artificial intelligence for non-invasive imaging diagnosis of basal cell carcinoma: A systematic review
Full Text (PDF)
Ibrahim Al Souleiman 1 * , Žanna Al Suleimane 2 , Murat Abdiev 1 , Habib Al Souleiman 1
More Detail
1 Research Department, Swiss International University, Zurich, SWITZERLAND2 Faculty of Medicine, University of Latvia, Riga, LATVIA* Corresponding Author

Abstract

Background/objectives: Basal cell carcinoma is the most common human cancer; it rarely spreads but destroys tissue locally, and its growth pattern determines treatment. We reviewed the accuracy of artificial intelligence (AI) applied to non-invasive skin imaging, against expert interpretation.
Materials and methods: Five databases were searched until 31 May 2026. Eligible studies applied AI to dermoscopy, confocal microscopy, optical coherence tomography, ultrasound or hyperspectral imaging with a separable result for this cancer. Studies were grouped by reference standard, appraised for risk of bias and rated for certainty; findings were synthesized narratively.
Results: Twenty-eight studies were included: fifteen histopathologically verified studies formed the primary evidence, and thirteen studies with archive-, consensus or undocumented reference standards were analyzed as supplementary evidence of technical performance. Accuracy varied widely within every technique and fell whenever a model was tested outside its development data. Only four studies compared stand-alone AI directly with human readers on the same lesions; the algorithm was level with or below the readers in three and better on specificity alone in one. Off-the-shelf general-purpose chatbots, in the versions and configurations evaluated, had the lowest sensitivities. Certainty was low or very low throughout.
Conclusions: Current evidence does not demonstrate superiority of AI over expert interpretation; direct comparisons are few, and most comparisons rely on expert benchmarks from other populations. Supervised decision support, not autonomous diagnosis, is the defensible position.

Keywords

Supplementary Files

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

ELECTRON J GEN MED, Volume 23, Issue 6, December 2026, Article No: em761

https://doi.org/10.29333/ejgm/19771

Publication date: 04 Nov 2026

Article Views: 89

Article Downloads: 20

Open Access References How to cite this article