Table 2.

Comparison of AI diagnostic performance across different medical fields.

Diagnostic field	Application	Diagnostic accuracy	Speed	Strengths	Challenges
Dermatology	Skin cancer detection	90–95%	Significantly faster than biopsy	High accuracy for melanoma; valuable for early detection	Struggles with atypical cases and non-Caucasian skin due to data bias (41, 33)
Radiology	Lung cancer detection	85–95%	<1 min per image	Sensitive to small nodules; reduces radiologist workload	Needs high-quality images; susceptible to motion artifacts (14, 42)
Ophthalmology	Diabetic retinopathy screening	90–98%	Immediate (seconds)	Enables mass screening; accurate in staging progression	May miss atypical cases; limited by dataset diversity (43, 44)
Cardiology	ECG interpretation for arrhythmias	85–92%	Real-time analysis	Supports continuous monitoring; aids early detection	Prone to errors in complex or mixed arrhythmias (45)
Pathology	Histopathology for cancer diagnosis	90–97%	Faster than human review	High sensitivity; helps prioritize critical cases	Limited interpretability; risk of over-reliance (46–48)
Pulmonology	Pneumonia Diagnosis via Chest X-Ray	85–93%	Immediate (seconds)	Effective for rapid triage in emergencies	Challenged by overlapping symptoms; sensitive to image quality (49, 50)
Neurology	Stroke Detection on MRI/CT	88–94%	Rapid pre-processing	High accuracy for ischemic/hemorrhagic stroke; time-sensitive	Limited diverse datasets; interpretability issues (51–53)