Applied Intelligence and Computing
Understanding the Potential of AI in Disease Identification and Prevention
Authors: Vazeer Ali Mohammed, Ashween Ganesh, Murtuza Ali Mohammed, Ketan Gupta and N. Yuvaraj
Publishing Date: 01-01-2025
ISBN: 978-81-955020-9-7
Abstract
AI can revolutionize the fitness care industry, from improving the early detection and analysis of disease to tracking chronic ailments and even predicting destiny health risks. AI can be used to identify deceptive interactions among dangerous elements and clinical treatments to save you from the onset of a disorder. AI can also assist in identifying new targets for preventive and healing interventions and compare treatment plans through the years to alter them as important. AI algorithms can examine massive quantities of facts with the intention of discerning patterns and uncovering new insights about how a disease develops or how an intervention can paintings to help save you from disease. Similarly, AI may be used to improve patient consequences by providing correct and dependable diagnostic and prognostic outcomes. AI can also aid the improvement of personalized treatments for diseases, which include cancer, in addition to generating personalized preventive fitness care plans. ultimately, AI can be used to expand new strategies for the prevention of disease by predicting how the spread of sickness might be induced and aided by means of environmental variations. In precis, AI can enhance the early detection and prognosis of ailment and enhance affected person outcomes via personalized remedies. It could provide new insights into sickness approaches, as well as offer greater precise predictive fashions for ailment prevention.
Keywords
Fitness, Quantities, Sickness, Algorithms, Prognosis.
Cite as
Vazeer Ali Mohammed, Ashween Ganesh, Murtuza Ali Mohammed, Ketan Gupta and N. Yuvaraj, "Understanding the Potential of AI in Disease Identification and Prevention", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2025, pp. 261-270. https://doi.org/10.56155/978-81-955020-9-7-25