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Transitioning to AI-based Laryngeal Cancer Diagnosis: A Systematic Review

by Mohd Usman, Puneet Misra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 138
Year of Publication: 2026
Authors: Mohd Usman, Puneet Misra
10.5120/ijca64b0b233b17b

Mohd Usman, Puneet Misra . Transitioning to AI-based Laryngeal Cancer Diagnosis: A Systematic Review. International Journal of Computer Applications. 187, 138 ( Aug 2026), 1-8. DOI=10.5120/ijca64b0b233b17b

@article{ 10.5120/ijca64b0b233b17b,
author = { Mohd Usman, Puneet Misra },
title = { Transitioning to AI-based Laryngeal Cancer Diagnosis: A Systematic Review },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 138 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number138/transitioning-to-ai-based-laryngeal-cancer-diagnosis-a-systematic-review/ },
doi = { 10.5120/ijca64b0b233b17b },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:55:13.660915+05:30
%A Mohd Usman
%A Puneet Misra
%T Transitioning to AI-based Laryngeal Cancer Diagnosis: A Systematic Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 138
%P 1-8
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Difficulty with speech, respiration, and deglutition represents a primary clinical presentation in patients diagnosed with laryngeal cancer. Accounting for approximately 1.1 million cases globally, this head and neck malignancy directly impairs the structural and functional integrity of the larynx. Conventional diagnostic and treatment modalities rely heavily on specialized otolaryngological expertise and often carry risks of collateral tissue damage or compromised functional preservation. To mitigate these clinical constraints, artificial intelligence (AI) methodologies have emerged as prominent tools for enabling early detection and precision diagnosis. This systematic review synthesizes literature published between January 2011 and April 2026, mapping the evolutionary trajectory and recent advancements of AI applications in laryngeal oncology. Following PRISMA guidelines, relevant literature was systematically selected and evaluated to highlight methodological developments, identify current clinical limitations, and delineate existing research gaps.

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Index Terms

Computer Science
Information Sciences

Keywords

Laryngeal Cancer Larynx Artificial Intelligence Machine Learning Deep Learning CNNs Optimization Techniques

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