Diagnostic Accuracy of Artificial Intelligence Expert Systems for Ear, Nose, and Throat Diseases: A Prospective Cross-Sectional Observational Study.

Jitendra Pratap Singh Chauhan; Kanchan Chaudhary; Jyoti Kumar Verma

doi:10.51168/sjhrafrica.v7i2.2798

Authors

Jitendra Pratap Singh Chauhan HOD & Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India
Kanchan Chaudhary Assistant Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India
Jyoti Kumar Verma Assistant Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India

DOI:

https://doi.org/10.51168/sjhrafrica.v7i2.2798

Keywords:

Artificial Intelligence, Ear, Nose, Throat diseases, diagnostic accuracy, expert systems, decision support systems, sensitivity, specificity

Abstract

Background:

Artificial Intelligence (AI)-based expert systems are increasingly being explored as decision-support tools in clinical practice. Their application in Ear, Nose, and Throat diseases may improve early diagnosis and facilitate timely referral, particularly in resource-constrained settings.

Objective:

To evaluate the diagnostic accuracy of an Artificial Intelligence expert system in patients presenting with Ear, Nose, and Throat diseases by comparing AI-generated diagnoses with final diagnoses established by consultant ENT specialists.

Methods:

A prospective cross-sectional observational study was conducted in the Department of Ear, Nose, and Throat and Head-Neck Surgery of a tertiary care teaching hospital from March 2025 to February 2026. A total of 150 patients presenting with various ENT complaints were included using consecutive sampling. Clinical information was entered into the AI expert system, and the generated diagnoses were compared with final diagnoses made by consultant ENT specialists. Sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy were calculated.

Results:

The AI expert system demonstrated good agreement with specialist diagnoses and showed satisfactory diagnostic performance across common ENT conditions. The system achieved high sensitivity and specificity for frequently encountered disorders, supporting its utility as an adjunctive diagnostic tool in clinical practice.

Conclusion:

Artificial Intelligence expert systems demonstrated promising diagnostic accuracy for Ear, Nose, and Throat diseases and may serve as valuable decision-support tools alongside specialist clinical assessment.

Recommendation:

AI expert systems may be utilized for preliminary screening, triage, and referral support in ENT practice, especially in settings with limited specialist availability. Further multicenter studies involving larger populations are recommended to validate these findings.

Author Biographies

Jitendra Pratap Singh Chauhan, HOD & Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India

HOD & Professor, Department of ENT & Head-Neck Surgery

Kanchan Chaudhary, Assistant Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India

Assistant Professor, Department of ENT & Head-Neck Surgery

Jyoti Kumar Verma, Assistant Professor, Department of ENT & Head-Neck Surgery, UPUMS, Saifai, India

Assistant Professor, Department of ENT & Head-Neck Surgery

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