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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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Detalles Bibliográficos
Autores principales: Kumar, Yogesh, Koul, Apeksha, Singla, Ruchi, Ijaz, Muhammad Fazal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754556/
https://www.ncbi.nlm.nih.gov/pubmed/35039756
http://dx.doi.org/10.1007/s12652-021-03612-z
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author Kumar, Yogesh
Koul, Apeksha
Singla, Ruchi
Ijaz, Muhammad Fazal
author_facet Kumar, Yogesh
Koul, Apeksha
Singla, Ruchi
Ijaz, Muhammad Fazal
author_sort Kumar, Yogesh
collection PubMed
description 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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spelling pubmed-87545562022-01-13 Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda Kumar, Yogesh Koul, Apeksha Singla, Ruchi Ijaz, Muhammad Fazal J Ambient Intell Humaniz Comput Original Research 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. Springer Berlin Heidelberg 2022-01-13 2023 /pmc/articles/PMC8754556/ /pubmed/35039756 http://dx.doi.org/10.1007/s12652-021-03612-z Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Kumar, Yogesh
Koul, Apeksha
Singla, Ruchi
Ijaz, Muhammad Fazal
Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title_full Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title_fullStr Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title_full_unstemmed Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title_short Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
title_sort artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754556/
https://www.ncbi.nlm.nih.gov/pubmed/35039756
http://dx.doi.org/10.1007/s12652-021-03612-z
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