by Alaa Fawaz, Alessandra Ferraresi, and Ciro Isidoro
Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, 28100 Novara, Italy
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Author to whom correspondence should be addressed.
J. Pers. Med.2023, 13(11), 1590; https://doi.org/10.3390/jpm13111590
Submission received: 17 October 2023 / Revised: 7 November 2023 / Accepted: 8 November 2023 / Published: 10 November 2023
(This article belongs to the Special Issue Transforming Precision Medicine: The Intersection of Digital Health and AI)
Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient’s response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient’s big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical–clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.
Keywords:
artificial intelligence; medical technology; smart health; digital health; omics technologies; imaging; diagnosis; personalized medicine
https://www.mdpi.com/2075-4426/13/11/1590
