Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support

Title

Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support

Creator

Nagashruthi, M.K.; Hemanth, K.S.

Description

The potential of artificial intelligence (AI) and machine learning to significantly modify clinical decision support is examined in this chapter. AI algorithms can use extensive databases of imaging outcomes, clinical trials, and medical records to identify complex patterns that lead to precise diagnoses, treatment plans, and progressively affected patient outcomes. A diagnosis includes evaluating the patients condition leveraging information gathered from multiple kinds of tests and their past medical history. AI-driven systems in the healthcare industry are constrained by the difficulty of handling tiny volumes and poor-quality medical data. A better prediction system for low-quality data and the analysis of unusual and sensitive medical cases can be analyzed by more powerful AI technologies. The chapter shows how AI-powered equipment is presently affecting healthcare. With excellent accuracy, device getting-to-know algorithms can examine medical images and identify potential abnormalities in X-rays, mammograms, or other imaging modes that a human might overlook. Furthermore, AI may review an affected persons records and show fitness facts to estimate a patients vulnerability to specific diseases, allowing for active intervention and preventative measures. The chapter concludes with critical tips for optimizing AIs complete range of applications in scientific care. To ensure the ideal and ethical application of these effective technologies, the responsibilities consist of defensive record safety and privacy, tackling algorithmic bias, and inspiring cooperation among clinical experts and AI developers. The healthcare zone can enter a modern section of using statistics to make educated selections through the implementation of AI and machine-gaining knowledge. 2025 selection and editorial matter, Rakesh Kumar and Meenu Gupta individual chapters, the contributors.

Source

Innovation in HealthTech: a Roadmap for Empowering Healthcare;pp.95-116

Date

01-01-2025

Publisher

CRC Press

Coverage

Nagashruthi M.K., School of Computer Science and Application, REVA University, Karnataka, Bangalore, India; Hemanth K.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-104033782-0; 978-103285027-6;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003516163-6

https://www.scopus.com/pages/publications/105002517479?origin=resultslist