A study at Rutgers University of dozens of artificial intelligence (AI) software programs used in precision medicine to prevent, diagnose, and treat disease discovered that there is not a single program that can be applied to all the treatments. The Rutgers study is among the first to investigate competing AI algorithms and software in genomics, especially when gene expression and variant data are used.

According to Zeeshan Ahmed, the study’s lead author and an assistant professor of medicine at Rutgers Robert Wood Johnson Medical School, precision medicine is one of the hottest topics in basic and medical science right now. Primarily, it has the potential to provide predictive diagnostics and personalized treatment for a wide range of known and rare disorders. However, until now, very little effort has been made to organize and comprehend the numerous computing approaches in this field.

The grand challenge today is to successfully integrate genetics into precision medicine that translates across different ancestries, diseases, and other distinct populations, which will necessitate the clever application of artificial intelligence (AI) and machine learning (ML) methods. The researchers are trying to pave the way for a new era of data-driven healthcare discovery.

Precision medicine, a contemporary technology, is a treatment approach that compares information about an individual’s medical history and genetic profile to that of many others to find patterns that can help prevent, diagnose, or treat disease. Because of the vast amount of medical and genetic data scoured and analyzed for patterns, the AI-based approach relies on high computing power and machine-learning intelligence.

The authors believe their comparative and systematic review is one of the first of its kind, as it identified 32 of the most common precision medicine AI approaches used to study preventive treatments for a variety of diseases, including obesity, Alzheimer’s, inflammatory bowel disease, breast cancer, and major depressive disorder. The multiple AI approaches examined in the study – the researchers combed through five years of high-quality medical literature – indicate that the field is advancing rapidly but is disorganized, according to Ahmed.

Software programs in AI simulate human intelligence processes. Machine learning, a subcategory of AI, is characterized by programs that are designed to “learn” as they process more and more data, becoming increasingly accurate at predicting outcomes. Algorithms, or step-by-step procedures for solving a problem or performing a computation, are at the heart of the effort.

Researchers like Ahmed, who works at the Rutgers Institute for Health, Health Care Policy, and Aging Research (IFH), are battling to collect and analyze complex biological data while also developing the computational systems that will support the endeavor.

According to Ahmed because genetics is arguably the most data-rich and complex component of precision medicine, the team concentrated on reviewing and comparing scientific objectives, methodologies, data sources, ethics, and gaps in approaches used.

Those interested in precision medicine can use the paper to determine which AI programs are best suited for their research, he added.

The study concluded that the scientific community must embrace several “grand challenges” to aid the advent of precision medicine, ranging from addressing general issues such as improved data standardization and enhanced protection of personally identifiable information to more technical issues such as correcting for errors in genomic and clinical data. protection

According to Ahmed, AI has the potential to play a critical role in significantly improving individualized and population healthcare at lower costs. It is important to put efforts to overcome any obstacles that may impede the advancement of this ground-breaking treatment approach.

Story Sources: Paper | Reference Article

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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.


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