Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Artificial Intelligence in Translational Medicine

Version 1 : Received: 16 October 2021 / Approved: 18 October 2021 / Online: 18 October 2021 (11:06:18 CEST)

A peer-reviewed article of this Preprint also exists.

Brogi, S.; Calderone, V. Artificial Intelligence in Translational Medicine. Int. J. Transl. Med. 2021, 1, 223-285. Brogi, S.; Calderone, V. Artificial Intelligence in Translational Medicine. Int. J. Transl. Med. 2021, 1, 223-285.


The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.


Translational Medicine; Machine Learning; Artificial Intelligence


Medicine and Pharmacology, Pharmacology and Toxicology

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