For those following our series on AI in communications, or for anyone closely watching developments in the life sciences, it won’t come as news that technology is one of the most promising new tools for solving challenges in health care and developing new treatments. Yet, with every leap forward in a direction it was intended for, technology seems to restate just how far beyond our expectations its potential remains when it comes to bringing discovery to poorly understood fields, particularly in a time that brings urgent focus to global health challenges. Now, it appears that our understanding of a number of very different diseases that all pose daunting risks is benefiting from the insights that algorithms can pull from vast and disparate data sets, in ways that are advancing the ground-level impact of care for afflicted patients. Simultaneously, one breakthrough in the use of technology to administer a treatment for depression that would otherwise be impractical is helping validate important elements of our evolving understanding of this disease, particularly with respect to treatment resistant depression and the highly diverse range of patient experiences associated with the illness.
A recent announcement from the University of Buffalo brings news that the school’s researchers have successfully applied AI analytics to vast quantities of US patient data relating to Type 2 diabetes, using differences in lifestyle data across more and less afflicted regions of the United States to model and predict patterns in the illness’s prevalence. Based on six risk factors – poverty, access to healthy food, education, obesity, physical inactivity, and access to opportunities to exercise – lead researcher Zia Ahmed indicates that the team was able to improve on any existing models for tracking patterns in the development of diabetes by region. This research now offers greater insights on how medical interventions and policy choices can help prevent the development of Type 2. As we noted during CSOFT’s participation in this year’s Taste of Ginger event with Harvard’s Joslin Diabetes Center, identifying preventive measures is especially important toward supporting demographics disproportionately at risk of diabetes. (You can also listen to our podcast episode here with Dr. George King from the Joslin Institute that explores this very topic.)
In a recent article, VentureBeat reiterated the importance of AI in modelling protein folding patterns – a key aspect of developing molecules that can be used as a foundation in therapies. One very interesting area where AI has been assisting more directly in the diagnostic process, however, is mental health, as we have previously noted in our look at how language AI can help detect worrisome speech patterns indicating manic or depressive episodes in people suffering from bipolar disorder. More recent reports of AI that can detect depression from voice tones appear to be continuing to expand on the support this kind of technology can lend to assessing patients, but where AI may be taking a less prominent role than other technologies is in the treatment of the same condition.
According to Technology Review this week, one woman suffering from treatment resistant depression has successfully received a brain implant to delivers neurologically targeted electrical impulses that offset manifest symptoms of depressive episodes it detects in her brain’s activity. Just as fascinating as the ability of modern technology to adapt new therapies from old techniques such as electric shock therapy is the validation its effectiveness brings to the current understanding of depression iterated by the World Economic Forum: a highly varied range of syndromes and symptoms that can discourage the sense of credibility in those who do not respond well to treatment. In this particular patient’s case, debilitating symptoms occurring throughout the day were effectively untreatable without the solution she received, which brought psychological relief in the form of a definition for her suffering as a treatable illness.
As technology continues to refine our ability to diagnose illness, develop therapies, and inform public health decisions, communication remains the single most vital factor in developing innovative solutions, whether in bringing drugs to market or localizing MedTech products for key markets. From medical translations and regulatory consulting to medical device localization, learn more about CSOFT Health Sciences’ work with delivering solutions across borders at lifesciences.csoftintl.com!