From Algorithm Performance to Laboratory Deployment: A Critical Review of Artificial Intelligence in Clinical Chemistry
- 1Department of Medical Analysis, Faculty of Applied Medical Sciences, Al al-Bayt University, Mafraq 25113, Jordan
- 2Department of Intelligent Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Salt 19117, Jordan
Abstract
Artificial intelligence (AI) is increasingly being applied in clinical laboratories to support laboratory processes and interpretation of test results. Recent developments in machine learning, deep learning, and large language models have expanded the potential applications of AI across different stages of laboratory testing. However, the use of AI in clinical laboratories also raises important questions regarding validation, analytical performance, and transferability between laboratories. This narrative review examines recent applications of AI across the total testing process in clinical chemistry. Recent English-language scientific literature published from 2023 to 18 August 2026 was identified through searches of PubMed, Scopus, and Google Scholar. It describes AI-assisted test utilization; specimen handling and quality assessment; detection of laboratory errors; patient-based quality control; interpretation of complex analytical outputs, including signals from emerging AI-integrated biosensors and nanomaterial-based analytical platforms; result verification; and laboratory-report interpretation. The review also discusses major implementation challenges, including analytical variability, data quality, sensor and platform reproducibility, model generalizability, interoperability, and limitations of large language models. Particular attention is given to external validation, transportability, and real-world implementation. Among the studies considered in this review, many applications remain supported mainly by retrospective or internally validated studies, while prospective and operational evaluations are less common. Finally, practical considerations for validation, local verification, quality assurance, and performance monitoring are discussed. AI has considerable potential to support clinical chemistry laboratories, but safe use requires appropriate evaluation and continued involvement of laboratory professionals.
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BibTeX
@article{baniahmad2026from,
title = {{From Algorithm Performance to Laboratory Deployment: A Critical Review of Artificial Intelligence in Clinical Chemistry}},
author = {Abdelraheem Baniahmad and Sulieman Bani-Ahmad},
journal = {Molecules},
year = {2026},
month = oct,
doi = {10.3390/molecules31203617},
url = {https://www.mdpi.com/1420-3049/31/20/3617},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - From Algorithm Performance to Laboratory Deployment: A Critical Review of Artificial Intelligence in Clinical Chemistry
AU - Abdelraheem Baniahmad
AU - Sulieman Bani-Ahmad
PY - 2026
DA - 2026/10/01
JO - Molecules
DO - 10.3390/molecules31203617
UR - https://www.mdpi.com/1420-3049/31/20/3617
AB - Artificial intelligence (AI) is increasingly being applied in clinical laboratories to support laboratory processes and interpretation of test results. Recent developments in machine learning, deep learning, and large language models have expanded the potential applications of AI across different stages of laboratory testing. However, the use of AI in clinical laboratories also raises important questions regarding validation, analytical performance, and transferability between laboratories. This narrative review examines recent applications of AI across the total testing process in clinical chemistry. Recent English-language scientific literature published from 2023 to 18 August 2026 was identified through searches of PubMed, Scopus, and Google Scholar. It describes AI-assisted test utilization; specimen handling and quality assessment; detection of laboratory errors; patient-based quality control; interpretation of complex analytical outputs, including signals from emerging AI-integrated biosensors and nanomaterial-based analytical platforms; result verification; and laboratory-report interpretation. The review also discusses major implementation challenges, including analytical variability, data quality, sensor and platform reproducibility, model generalizability, interoperability, and limitations of large language models. Particular attention is given to external validation, transportability, and real-world implementation. Among the studies considered in this review, many applications remain supported mainly by retrospective or internally validated studies, while prospective and operational evaluations are less common. Finally, practical considerations for validation, local verification, quality assurance, and performance monitoring are discussed. AI has considerable potential to support clinical chemistry laboratories, but safe use requires appropriate evaluation and continued involvement of laboratory professionals.
SN - 1420-3049
ER -