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A Comprehensive Study on Algorithms and Applications of Artificial Intelligence in Diagnosis and Prognosis: AI for Healthcare
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Author(s): Sahana P. Shankar (M. S. Ramiaiah University of Applied Sciences, India), Supriya M. S. (M. S. Ramiaiah University of Applied Sciences, India), Deepak Varadam (M. S. Ramiaiah University of Applied Sciences, India), Mushkan Kumar (M. S. Ramiaiah University of Applied Sciences, India), Harshita Gupta (M. S. Ramiaiah University of Applied Sciences, India)and Rishika Saha (M. S. Ramiaiah University of Applied Sciences, India)
Copyright: 2023
Pages: 20
Source title:
Digital Twins and Healthcare: Trends, Techniques, and Challenges
Source Author(s)/Editor(s): Loveleen Gaur (Amity University, India & Taylor's University, Malaysia & University of the South Pacific, Fiji)and Noor Zaman Jhanjhi (Taylor's University, Malaysia)
DOI: 10.4018/978-1-6684-5925-6.ch003
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Abstract
Machine learning and deep learning are branches of artificial intelligence consisting of statistical, probabilistic, and optimisation techniques that allow machines to learn from previous observations recorded by humans. These machine learning algorithms, when combined with other technologies, can be used to perform very intuitive yet awkward human-like tasks. Using these algorithms, humans can enable computers to learn about certain things like recognising an object in an image. Prognosis is an important clinical skill, particularly for cancer patients' clinicians, neurooncologists. One of the biggest challenge for AI in prognosis is to verify and validate its models. Unlike diagnosis, the prognosis models are centered on predictive data that usually addresses the patient and not the disease. Prognosis models were developed to aid in the decision-making of patients' treatment.AI can have bigger impact in the health care domain with more healthcare providers using AI to make the first diagnosis and prognosis more accurate and interpretable with the available patient data for better therapy.
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