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Artificial Intelligence in Healthcare: Diagnosis, Treatment, and Prediction

Authors :
Devi Kharibam Jilenkumari
Alghamdi Wajdi
N Divya
Alkhayyat Ahmed
Sayyora Artikbaeva
Sathish T.
Source :
E3S Web of Conferences, Vol 399, p 04043 (2023)
Publication Year :
2023
Publisher :
EDP Sciences, 2023.

Abstract

One of the most potential uses of artificial intelligence (AI), which has changed a number of industries, is in healthcare. The application of AI in healthcare is discussed in general in this study, with an emphasis on diagnosis, treatment, and prediction. In the area of diagnostics, AI has proven to be remarkably adept at deciphering X-rays, CT scans, and MRI pictures to spot illnesses and anomalies. A branch of AI known as deep learning algorithms has shown to be particularly good at accurately identifying and categorizing medical disorders. Large volumes of imaging data may be swiftly analyzed by AI systems, enabling medical personnel to diagnose patients more accurately and with fewer mistakes. Additionally, AI may combine patient information, genetic data, and other pertinent data to produce tailored diagnostic suggestions. Consequently, AI has become a game-changing force in healthcare, especially in the disciplines of diagnosis, treatment, and prediction. AI systems can help medical personnel make more precise diagnoses, create individualized treatment plans, and forecast patient outcomes by utilizing machine learning algorithms and advanced data analytics. While there are still difficulties, there are enormous potential advantages for AI in healthcare, and coordinated efforts are required to realize these advantages and assure its ethical and fair incorporation into healthcare systems.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
399
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.ba1d7a5a62b64b8bbbeedf5aa72705f0
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/202339904043