Year: 2025
Type: article
Abstract: Hematology forms the backbone of medical diagnostics, yet access to reliable and timely blood analysis remains a challenge in resource-limited settings. Conventional hematology analyzers demand trained personnel, costly reagents, and advanced infrastructure, creating significant barriers in underserved regions. This paper examines the integration of artificial intelligence (AI) into hematology analyzers and its role in bridging diagnostic disparities. Using a simulated dataset of 500 patient samples, we compare conventional analyzers, semi-automated systems, and AI-powered platforms across accuracy, turnaround time, cost, and technician dependency. Results indicate that AI-driven analyzers achieve diagnostic accuracies exceeding 96%, reduce turnaround time by 72%, and cut per-test costs by more than 50%. Beyond technical advantages, the study underscores the human impact: faster diagnoses enabling life-saving interventions in rural hospitals. The findings highlight the transformative potential of AI in hematology, paving the way for equitable access to diagnostics in low-resource environments
Author: Omkar Chavan
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415093900
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