Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices
As implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standard...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.872675/full |
| _version_ | 1818127757897367552 |
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| author | Ryan L. Melvin Matthew G. Broyles Elizabeth W. Duggan Sonia John Andrew D. Smith Dan E. Berkowitz |
| author_facet | Ryan L. Melvin Matthew G. Broyles Elizabeth W. Duggan Sonia John Andrew D. Smith Dan E. Berkowitz |
| author_sort | Ryan L. Melvin |
| collection | DOAJ |
| description | As implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standardization. Additionally, the need to communicate with algorithm developers is paramount to actualize effective and practical implementation. Of particular interest in these discussions is the extent to which the output or predictions of algorithms and tools are understandable by medical practitioners. This work proposes a simple nomenclature that is intelligible to both clinicians and developers for quickly describing the interpretability of model results. There are three high-level categories: transparent, translucent, and opaque. To demonstrate the applicability and utility of this terminology, these terms were applied to the artificial intelligence and machine-learning-based products that have gained Food and Drug Administration approval. During this review and categorization process, 22 algorithms were found with perioperative utility (in a database of 70 total algorithms), and 12 of these had publicly available citations. The primary aim of this work is to establish a common nomenclature that will expedite and simplify descriptions of algorithm requirements from clinicians to developers and explanations of appropriate model use and limitations from developers to clinicians. |
| first_indexed | 2024-12-11T07:22:26Z |
| format | Article |
| id | doaj.art-a5641782c06945988198fba6a21ab731 |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-12-11T07:22:26Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-a5641782c06945988198fba6a21ab7312022-12-22T01:16:03ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-04-01410.3389/fdgth.2022.872675872675Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved DevicesRyan L. Melvin0Matthew G. Broyles1Elizabeth W. Duggan2Sonia John3Andrew D. Smith4Dan E. Berkowitz5Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Radiology, University of Alabama at Birmingham, Birmingham, AL, United StatesDepartment of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United StatesAs implementation of artificial intelligence grows more prevalent in perioperative medicine, a clinician's ability to distinguish differentiating aspects of these algorithms is critical. There are currently numerous marketing and technical terms to describe these algorithms with little standardization. Additionally, the need to communicate with algorithm developers is paramount to actualize effective and practical implementation. Of particular interest in these discussions is the extent to which the output or predictions of algorithms and tools are understandable by medical practitioners. This work proposes a simple nomenclature that is intelligible to both clinicians and developers for quickly describing the interpretability of model results. There are three high-level categories: transparent, translucent, and opaque. To demonstrate the applicability and utility of this terminology, these terms were applied to the artificial intelligence and machine-learning-based products that have gained Food and Drug Administration approval. During this review and categorization process, 22 algorithms were found with perioperative utility (in a database of 70 total algorithms), and 12 of these had publicly available citations. The primary aim of this work is to establish a common nomenclature that will expedite and simplify descriptions of algorithm requirements from clinicians to developers and explanations of appropriate model use and limitations from developers to clinicians.https://www.frontiersin.org/articles/10.3389/fdgth.2022.872675/fullartificial intelligenceAImachine learningalgorithmFDA approval |
| spellingShingle | Ryan L. Melvin Matthew G. Broyles Elizabeth W. Duggan Sonia John Andrew D. Smith Dan E. Berkowitz Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices Frontiers in Digital Health artificial intelligence AI machine learning algorithm FDA approval |
| title | Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices |
| title_full | Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices |
| title_fullStr | Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices |
| title_full_unstemmed | Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices |
| title_short | Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices |
| title_sort | artificial intelligence in perioperative medicine a proposed common language with applications to fda approved devices |
| topic | artificial intelligence AI machine learning algorithm FDA approval |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.872675/full |
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