AI in Pharma for Personalized Sequential Decision-Making: Methods,
  Applications and Opportunities
Yuhan Li Hongtao Zhang Keaven Anderson Songzi Li Ruoqing Zhu
In the pharmaceutical industry, the use of artificial intelligence (AI) has
seen consistent growth over the past decade. This rise is attributed to major
advancements in statistical machine learning methodologies, computational
capabilities and the increased availability of large datasets. AI techniques
are applied throughout different stages of drug development, ranging from drug
discovery to post-marketing benefit-risk assessment. Kolluri et al. provided a
review of several case studies that span these stages, featuring key
applications such as protein structure prediction, success probability
estimation, subgroup identification, and AI-assisted clinical trial monitoring.
From a regulatory standpoint, there was a notable uptick in submissions
incorporating AI components in 2021. The most prevalent therapeutic areas
leveraging AI were oncology (27%), psychiatry (15%), gastroenterology (12%),
and neurology (11%). The paradigm of personalized or precision medicine has
gained significant traction in recent research, partly due to advancements in
AI techniques \cite{hamburg2010path}. This shift has had a transformative
impact on the pharmaceutical industry. Departing from the traditional
"one-size-fits-all" model, personalized medicine incorporates various
individual factors, such as environmental conditions, lifestyle choices, and
health histories, to formulate customized treatment plans. By utilizing
sophisticated machine learning algorithms, clinicians and researchers are
better equipped to make informed decisions in areas such as disease prevention,
diagnosis, and treatment selection, thereby optimizing health outcomes for each
individual.