<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Abstract

The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

“…However, this large influx of data presents an issue, as no reliable or standardized means of analysis has been developed. Such data are too large to be analyzed through common visual analysis or statistical correlation methods ( Álvarez-Machancoses et al, 2020 ; Caudai et al, 2021 ). The use of AI and ML techniques alleviates this issue by allowing for the efficient management of data and providing the ability to recognize patterns in complex datasets ( Caudai et al, 2021 ).…”

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross‐sectional analysis of the Saudi human genome program

“…Despite some concerns about AI use in health care and genomic data, such as inaccuracies, discrimination, and bias in the database, AI algorithms will revolutionize genomics and proteomics data analysis, improving precision medicine in genetic disease diagnosis and treatment 23 , 37 . AI algorithms, more specifically, deep learning based algorithms are currently being employed in clinical diagnosis and analysis of complex and large-scale genomic databases.…”

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

“…Dissemination of knowledge and research personnel and facilities is also warranted. Furthermore, genuinely personalized medicine is unlikely to be realized without the use of artificial intelligence (AI) ( 35 ) and ML. In neonatal neurology, ML is used to prevent brain injury from the continuous assessment of vital signs ( 36 ).…”