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Orientador(es)
Resumo(s)
Polymerase chain reaction (PCR) and its advanced derivatives—quantitative PCR (qPCR), digital PCR (dPCR), high-resolution melting (HRM) analysis, and isothermal amplification—remain central to molecular diagnostics. Their growing data complexity demands computational solutions beyond traditional analysis. Meanwhile, the advancement of artificial intelligence (AI) algorithms has driven progress from conventional shallow machine learning (ML) to more complex deep learning approaches. Currently, AI is able to provide powerful frameworks for interpreting amplification dynamics, optimizing assay design, and visualizing molecular reactions in real time. With the assistance of AI, PCR can be transformed from a common laboratory technique into an intelligent diagnostic system. Herein, we review recent progress at the intersection of AI and PCR across biomedical and clinical domains. These studies demonstrate that AI-enhanced PCR platforms have significantly improved diagnostic accuracy, reproducibility, and analytical throughput, while simultaneously reducing operator dependency and cost. AI-enabled PCR is poised to become a cornerstone for next-generation, intelligent molecular diagnostics in medicine.
Descrição
This work is supported by the Macau Science and Technology Development Fund (FDCT) [FDCT0168/2023/RIA3, FDCT0001/2025/ RID, FDCT0213/2024/AGJ, FDCT0001/2025/NRP]; University of Macau [UMDF-TISF/2025/014/IME, SRG2024–00057-IME].
Palavras-chave
Artificial Intelligence AI Machine learning Polymerase chain reaction PCR DNA detection Literature review
Contexto Educativo
Citação
Andaluz S, Lv A, Yu P, Hui W, Shen R, Brito M, et al. Artificial intelligence (AI) for polymerase chain reaction (PCR): a state-of-the-art review. Biomed Instrum. 2026;2(2):100052.
Editora
Elsevier BV