Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis
<h3>Introduction</h3><p dir="ltr">Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , , |
| منشور في: |
2024
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| _version_ | 1864513551798894592 |
|---|---|
| author | Jithin Kalathikudiyil Sreedharan (18268894) |
| author2 | Fred Saleh (20735696) Abdullah Alqahtani (7128143) Ibrahim Ahmed Albalawi (20735699) Gokul Krishna Gopalakrishnan (20735702) Hadi Abdullah Alahmed (20735705) Basem Ahmed Alsultan (20735708) Dhafer Mana Alalharith (20735711) Musallam Alnasser (20735714) Ayedh Dafer Alahmari (20735717) Manjush Karthika (18384907) |
| author2_role | author author author author author author author author author author |
| author_facet | Jithin Kalathikudiyil Sreedharan (18268894) Fred Saleh (20735696) Abdullah Alqahtani (7128143) Ibrahim Ahmed Albalawi (20735699) Gokul Krishna Gopalakrishnan (20735702) Hadi Abdullah Alahmed (20735705) Basem Ahmed Alsultan (20735708) Dhafer Mana Alalharith (20735711) Musallam Alnasser (20735714) Ayedh Dafer Alahmari (20735717) Manjush Karthika (18384907) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jithin Kalathikudiyil Sreedharan (18268894) Fred Saleh (20735696) Abdullah Alqahtani (7128143) Ibrahim Ahmed Albalawi (20735699) Gokul Krishna Gopalakrishnan (20735702) Hadi Abdullah Alahmed (20735705) Basem Ahmed Alsultan (20735708) Dhafer Mana Alalharith (20735711) Musallam Alnasser (20735714) Ayedh Dafer Alahmari (20735717) Manjush Karthika (18384907) |
| dc.date.none.fl_str_mv | 2024-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/frai.2024.1422551 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Applications_of_artificial_intelligence_in_emergency_and_critical_care_diagnostics_a_systematic_review_and_meta-analysis/28428620 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Epidemiology Health services and systems Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Statistics artificial intelligence machine learning critical care medicine healthcare diagnosis |
| dc.title.none.fl_str_mv | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Introduction</h3><p dir="ltr">Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial. </p><h3>Methodology</h3><p dir="ltr">The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews. </p><h3>Results</h3><p dir="ltr">In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette’s neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%. </p><h3>Conclusion</h3><p dir="ltr">The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2024.1422551" target="_blank">https://dx.doi.org/10.3389/frai.2024.1422551</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_89fc45ca87372f913d9959e81dcc2d59 |
| identifier_str_mv | 10.3389/frai.2024.1422551 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28428620 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysisJithin Kalathikudiyil Sreedharan (18268894)Fred Saleh (20735696)Abdullah Alqahtani (7128143)Ibrahim Ahmed Albalawi (20735699)Gokul Krishna Gopalakrishnan (20735702)Hadi Abdullah Alahmed (20735705)Basem Ahmed Alsultan (20735708)Dhafer Mana Alalharith (20735711)Musallam Alnasser (20735714)Ayedh Dafer Alahmari (20735717)Manjush Karthika (18384907)Health sciencesEpidemiologyHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningMathematical sciencesStatisticsartificial intelligencemachine learningcritical care medicinehealthcarediagnosis<h3>Introduction</h3><p dir="ltr">Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial. </p><h3>Methodology</h3><p dir="ltr">The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews. </p><h3>Results</h3><p dir="ltr">In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette’s neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%. </p><h3>Conclusion</h3><p dir="ltr">The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Artificial Intelligence<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/frai.2024.1422551" target="_blank">https://dx.doi.org/10.3389/frai.2024.1422551</a></p>2024-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/frai.2024.1422551https://figshare.com/articles/journal_contribution/Applications_of_artificial_intelligence_in_emergency_and_critical_care_diagnostics_a_systematic_review_and_meta-analysis/28428620CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284286202024-10-01T00:00:00Z |
| spellingShingle | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis Jithin Kalathikudiyil Sreedharan (18268894) Health sciences Epidemiology Health services and systems Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Statistics artificial intelligence machine learning critical care medicine healthcare diagnosis |
| status_str | publishedVersion |
| title | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| title_full | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| title_fullStr | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| title_full_unstemmed | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| title_short | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| title_sort | Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis |
| topic | Health sciences Epidemiology Health services and systems Information and computing sciences Artificial intelligence Machine learning Mathematical sciences Statistics artificial intelligence machine learning critical care medicine healthcare diagnosis |