Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.
Artificial intelligence (AI) continues to grow in sophistication, largely due to advances in machine learning (ML). However, there are still critical questions that need to be answered.
Machine learning has close ties to predictive analytics. Both can be powerful tools for uncovering insights and identifying patterns in large amounts of data. These capabilities could serve the healthcare sector quite well, particularly when you consider that 30% of all data generated worldwide comes from healthcare alone.
However, AI in the healthcare industry is still in its relative infancy in numerous areas, often relegated to managing medical records or automating repetitive, mundane tasks. Of course, neither of those things lacks value, but moving toward greater industry-wide adoption has the potential to solve the “triple As” of healthcare: accessibility, affordability and accuracy. Explainable AI has even more potential: It can help institutions better find correlations through data and improve diagnostics.
Consider mental disorders. For the past 20 to 30 years, there’s been surprisingly little progress in the field of mental disorders. Healthcare providers often don’t always know what triggers certain mental disorders in different people. Mental disorders are, by their nature, highly personalized. Fortunately, the use of explainable AI presents an opportunity to find a correlation between data points, allowing physicians to offer more personalized diagnostic results.
Intelligent Security Summit
Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.