A team of researchers from the University of Utah has developed an innovative software tool called RiskPath. The open-source platform uses explainable AI (XAI) to forecast the likelihood of people developing long-term and progressive health conditions years before any symptoms start to show. This development could mark a major shift in how preventive care is approached in the medical field.
Explainable AI refers to systems that not only make decisions but also communicate how those decisions are made, in a way that humans understand.
RiskPath stands out by applying this principle to complex health data collected over time. It examines long-term patterns to flag individuals who may be at higher risk of certain illnesses. With an accuracy rate ranging from 85% to 99%, RiskPath significantly outperforms many current prediction methods, which often identify high-risk patients with much lower reliability.
Traditional medical prediction models frequently struggle with time-based health data, often missing crucial patterns or offering limited insight. RiskPath addresses this gap by using sophisticated AI models designed to handle time-series data and then translating their results into clear, human-readable insights. This allows healthcare providers to better understand how various risk factors interact and evolve as a disease progresses.
According to the study’s lead author Dr. Nina de Lacy, chronic diseases are responsible for the majority of healthcare costs and deaths. She adds that the ability to detect early warning signs—sometimes even before a person feels unwell—opens the door to more personalized and proactive care.
The research team tested RiskPath on three large, long-term patient datasets, encompassing thousands of individuals. The tool was able to predict eight health conditions, such as anxiety, depression, ADHD, metabolic syndrome, and high blood pressure.
One of its key strengths is its ability to reveal how risk factors shift over time. For example, in the case of ADHD, the study found that executive function and screen time become more influential as children near adolescence.
Another practical benefit of the system is its efficiency. Although it can evaluate hundreds of data points, RiskPath often needs only about ten key variables to make accurate predictions, making it easier to apply. Additionally, its user-friendly visual tools highlight the most impactful life stages for each disease, helping doctors time interventions more effectively.
The researchers plan to expand RiskPath’s capabilities by studying more diseases and working with broader populations. They also aim to integrate the technology into tools that support clinical decisions and enhance mental health prevention strategies.
With AI increasingly making its way into the healthcare industry and other industries like social media advertising as exemplified by the solutions offered by firms like Thumzup Media Corp. (NASDAQ: TZUP), it won’t be long before this technology is commonplace in all walks of life.
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