As AI spreads across the healthcare sector, providers and researchers are increasingly turning their attention to how the new technology can be enlisted to help develop a deeper understanding of, and more effective treatments for, a range of specific diseases.
Take multiple sclerosis (MS), for example. In a recent commentary, three life science experts from the global consulting firm CRA, discuss how AI could “revolutionize” how MS is diagnosed and treated.
For starters, they note that the “underlying etiology” of MS is still unknown, and as a result “(p)ersistent challenges in the current treatment approach to MS include a lack of reliable biomarkers, making patient diagnosis, disease monitoring, and drug discovery challenging.”
If tapped properly and effectively, however, AI could go a long way toward changing that.
For example, they say, “AI platforms may allow for diagnosis of MS through identification of indolent clinical characteristics that a physician may not otherwise notice. . . . Developing these platforms requires the compilation of large clinical data sets to power the detection of MS patients. The ability to reference large reservoirs of clinical data can be used to power precision-based medicine, tailored to each patient, as MS subpopulations become evident.”
AI’s ability to crunch huge datasets should also help with the monitoring of MS patients, the three say, noting that “(i)nnovations in digital disease tracking technology may allow for the accumulation of real-world (RW) patient data in real time that can be integrated into burgeoning machine learning databases such as those being developed for MS. Advances in data collection technology, such as developments in biosensors and disease tracking applications for smartphones, unlock access to an unprecedented wealth of disease data. Using AI technology to sift through and derive meaningful patient care implications will be essential to capitalizing on these novel databases.”
Finally, there’s the need for new drugs. “Integration of AI approaches into pharmaceutical and biotech drug discovery platforms has the potential to expedite the identification of novel biologic therapies for challenging diseases like MS,” the three point out, adding, “AI is set to disrupt the drug discovery process across multiple disease spaces, including MS, because of its ability to enable a rapid and less labor-intensive drug discovery platform.”
For each of their recommendations for the expanded use of AI, the writers offer case studies demonstrating that change is already taking place. But they also note that the benefits of AI are not limited to the battle against MS.
“Drug developers operating in other diseases, particularly those with high unmet needs, poorly characterized underlying pathways, and expensive and burdensome drug discovery approaches, must (also) be able to understand and harness the utility of AI to be successful in the coming years,” they conclude.
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