Impacts of the advancement in artificial intelligence on laboratory medicine in low‐ and middle‐income countries: Challenges and recommendations—A literature review

Abstract

Background and AimsArtificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low‐ and middle‐income countries (LMICs).MethodsIn writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision‐making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.ResultsIn the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource‐constrained LMICs.ConclusionWhile acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data‐sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Laboratory Information Management Systems in Oral Pathology: A Comprehensive Review

“…Platforms for telepathology facilitate the digital sharing and review of specimen images and diagnostic data, fostering efficient collaboration between oral pathologists, clinicians, and researchers. This approach is valuable in providing specialized oral pathology expertise to underserved or remote areas [20].…”

“…In today's world of scientific research, medical advancements, and industrial discoveries, efficiently managing laboratory data is critical for success in various fields [25]. LIMS have been concurrently employed in various modern laboratories to provide researchers, lab directors, and corporate executives with the necessary aids for navigation of issues with data management [20]. The duties of laboratory management are to manage the steps of sample tracking, result interpretation, and report creation.…”