Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous med...

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Vydané v:Journal of ambient intelligence and humanized computing Ročník 14; číslo 7; s. 8459 - 8486
Hlavní autori: Kumar, Yogesh, Koul, Apeksha, Singla, Ruchi, Ijaz, Muhammad Fazal
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2023
Springer Nature B.V
Predmet:
Artificial Intelligence
Cardiovascular disease
Computational Intelligence
Computed tomography
Deep learning
Diagnosis
Engineering
Feature extraction
Heart diseases
Hypertension
Literature reviews
Machine learning
Magnetic resonance imaging
Medical electronics
Medical imaging
Neural networks
Original Research
Parameter sensitivity
Robotics and Automation
Sensors
Support vector machines
Ultrasonic testing
User Interfaces and Human Computer Interaction
Tuberculosis
Cancer disease
Chronic disease
Artificial intelligence
Alzheimer
Heart disease
ISSN:1868-5137, 1868-5145
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Popis
Shrnutí:Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-03612-z