JMIR Publications

ABSTRACT

Background:

Artificial intelligence is expanding in various medical fields, with machine learning increasingly used to enhance patient management in diagnosis, prevention, and therapeutic care.

Objective:

The aim of this study is to provide an overview of AI applications in HIV care, focusing on real clinical data to improve healthcare for people living with HIV and on antiretroviral therapy, while highlighting unexplored areas.

Methods:

Following PRISMA 2020 guidelines, we analyzed four databases: PubMed, Embase, IEEE, and Web of Science. We excluded from this review studies (i) that were not directly focused on HIV or those that did not apply machine learning to real clinical data, (ii) that focused on pre-exposure prophylaxis (iii) study involving in silico antiretroviral drug development and (iv) studies on the biological mechanisms underlying HIV diagnosis.

Results:

A total of 476 studies were identified, and after eligibility assessment, 98 were analyzed in detail. Random forests emerged as the most used algorithm, proving effective in identifying biomarkers of metabolic syndrome, genetic features of the HIV envelope, and predicting neurocognitive impairment. Support Vector Machines demonstrated strong abilities in analyzing the associations between HIV-1 genotypes and resistance phenotypes, predicting virological response to therapy based on HIV genotype, detecting mutations associated with HIV drug resistance without the need for expert knowledge, and enhancing computational predictions of resistance from genotype data. Logistic regression appears to be most powerful in predicting various treatment outcomes, including virological failure, adverse events, immune changes in patients receiving antiretrovirals, and biomarkers of mitochondrial toxicity.

Conclusions:

Depending on the field of application, some ML methods are more suitable and adapt better to certain HIV concerns. However, some areas such as treatment recommendations, treatment adherence, and treatment optimization, still lack AI algorithms and need further exploration such as therapeutical optimization. The development of new clinical decision-support systems for people living with HIV is the new challenge for the years ahead, and AI represent one of the most promising tools to address it.