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AI-Driven Diagnostics Transform Healthcare
By Gaurav Chandra  |  Apr 04, 2022
AI-Driven Diagnostics Transform Healthcare
Image courtesy of and under license from Shutterstock.com
In the pandemic’s wake, the world still faces massive challenges in detecting and diagnosing many life-threatening illnesses. Dr Gaurav Chandra explores the evolution of healthcare from reactivity to prevention, prediction, participation, and personalization, along with the part that artificial intelligence-driven diagnostics has played in this transformation.

DENVER, COLORADO - Healthcare is evolving to become more predictive, preventative, personalized and participatory (P4). P4 medicine embodies the healthcare of the future, and artificial intelligence (AI) is the roadmap by which to plot its course. The incorporation of AI into healthcare improves access and quality, and significantly reduces costs and improves clinical outcomes, while complementing physicians’ efforts.

The pandemic has highlighted the elements of P4 medicine and accelerated innovation, quickly bringing products to the market and exploring innovative ways to ensure adequate patient diagnostics. P4 medicine and AI share the following characteristics: 

- P4 medicine aims to be predictive; AI is programmed to be prognostic. 
- P4 medicine seeks preventative measures; AI is designed to be anticipatory. 
- P4 medicine embraces personalized care; AI learns by being adaptive. 
- P4 medicine encourages physician/patient participation; AI thrives on inclusive and participatory acts.1

After the pandemic, the world still faces massive challenges in detecting and diagnosing many life-threatening illnesses, infectious diseases, chronic medical maladies, and cancer. Patients are best helped by prevention, early detection, and diagnosis, and AI is rapidly becoming indispensable in healthcare. Several successful applications exist, ranging from applying pattern recognition software to pre-process and analyze digital images, to deep learning (DL) algorithms for the subtyping of disease classification. We need to incorporate AI, which is application-rich and accepts small amounts of data, to solve some of the most critical problems facing us in h

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