Critical appraisal of a machine learning paper: A guide for the neurologist
Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2021-01-01
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| Series: | Annals of Indian Academy of Neurology |
| Subjects: | |
| Online Access: | http://www.annalsofian.org/article.asp?issn=0972-2327;year=2021;volume=24;issue=4;spage=481;epage=489;aulast=Vinny |
| _version_ | 1818734884720476160 |
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| author | Pulikottil W Vinny Rahul Garg M V Padma Srivastava Vivek Lal Venugoapalan Y Vishnu |
| author_facet | Pulikottil W Vinny Rahul Garg M V Padma Srivastava Vivek Lal Venugoapalan Y Vishnu |
| author_sort | Pulikottil W Vinny |
| collection | DOAJ |
| description | Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment. |
| first_indexed | 2024-12-18T00:12:27Z |
| format | Article |
| id | doaj.art-26534857d3cd4bea93fba149357b0dc8 |
| institution | Directory Open Access Journal |
| issn | 0972-2327 1998-3549 |
| language | English |
| last_indexed | 2024-12-18T00:12:27Z |
| publishDate | 2021-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Annals of Indian Academy of Neurology |
| spelling | doaj.art-26534857d3cd4bea93fba149357b0dc82022-12-21T21:27:37ZengWolters Kluwer Medknow PublicationsAnnals of Indian Academy of Neurology0972-23271998-35492021-01-0124448148910.4103/aian.AIAN_1120_20Critical appraisal of a machine learning paper: A guide for the neurologistPulikottil W VinnyRahul GargM V Padma SrivastavaVivek LalVenugoapalan Y VishnuMachine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment.http://www.annalsofian.org/article.asp?issn=0972-2327;year=2021;volume=24;issue=4;spage=481;epage=489;aulast=Vinnycritical appraisaldeep learningmachine learningneural networks |
| spellingShingle | Pulikottil W Vinny Rahul Garg M V Padma Srivastava Vivek Lal Venugoapalan Y Vishnu Critical appraisal of a machine learning paper: A guide for the neurologist Annals of Indian Academy of Neurology critical appraisal deep learning machine learning neural networks |
| title | Critical appraisal of a machine learning paper: A guide for the neurologist |
| title_full | Critical appraisal of a machine learning paper: A guide for the neurologist |
| title_fullStr | Critical appraisal of a machine learning paper: A guide for the neurologist |
| title_full_unstemmed | Critical appraisal of a machine learning paper: A guide for the neurologist |
| title_short | Critical appraisal of a machine learning paper: A guide for the neurologist |
| title_sort | critical appraisal of a machine learning paper a guide for the neurologist |
| topic | critical appraisal deep learning machine learning neural networks |
| url | http://www.annalsofian.org/article.asp?issn=0972-2327;year=2021;volume=24;issue=4;spage=481;epage=489;aulast=Vinny |
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