Several healthcare companies and startups in the digital health space use predictive modeling and AI-based techniques to develop interactive and integrated solutions. These solutions aim to provide personalized coaching and education for lifestyle changes and ways for early disease risk prediction, detection, and diagnosis, symptom monitoring, and treatment. 

The following are five key aspects of your AI-driven digital health solution that could provide the maximum value and ROI.

• Performs longitudinal data analysis: Longitudinal data analyzed using AI could provide more details on physiological changes than data obtained using a single test at a doctor’s office. For example, the continuous data collected by period tracking apps like Clue and Flo from millions of women worldwide could be used to build data models and predictive analytic algorithms to study, understand and develop solutions around reproductive health.
• Regularly provides updated insights: As life progresses, subtle changes can happen in the data collected, and these subtle changes could have a significant impact on the health condition that is being tracked, for example, using a wearable device. Without adequate updating and ongoing maintenance of analysis algorithms, the usability of data decays over time. Traditional generic app updates or device updates are generally cumbersome and may miss capturing personalized changes occurring in an individual. On the other hand, embedded AI-powered algorithms can iteratively improve over time using newly accrued data. 
• Integrates data from several relevant sources: With the technological advancements, AI-based solutions can now be quickly developed using data from multiple structured and unstructured data sources – such as apps, wearables, connected medical devices, Electronic Health Records, Clinical notes, etc. Such an integrated approach allows for the development of personalized solutions customized to each user’s individual physical, biological, and socioeconomic conditions.
• Addresses gender and demographic bias: AI-powered algorithms can quickly learn the characteristics of diverse datasets collected worldwide. This capability is critical for developing accurate solutions that work for the global population and can also be customizable for local groups.  For example, AI algorithms could also be trained using only data from women to develop targeted solutions for women’s health. Did you know? Because of hormonal changes, symptoms of heart diseases present themselves differently in women. An AI algorithm trained using a dominant male dataset will likely misdiagnose heart attacks in women than men. This misdiagnosis is because the algorithm would have failed to learn from the unique indicators of a heart attack in women. Thus, targeted data collection is critical to cater to the issues of specific gender and demographics.
• Helps reduce cost and time of diagnosis: Does your solution help reduce cost and time of diagnosis? One common digital health solution that addresses these aspects is a wearable/handheld device that may help provide timely and affordable healthcare services to remote and rural areas in developed and developing countries worldwide. These may be of particular benefit in developing countries. For example, in countries that do not have national cancer screening programs, wearable screening devices using AI could help detect cancer early and provide a means for non-invasive detection that would encourage more users to participate.