Clinical Applications of Machine Learning
Objective:. This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background:. As machine learning, artificial intelligence, and generative artificial intelligenc...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Health
2024-06-01
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| Series: | Annals of Surgery Open |
| Online Access: | http://journals.lww.com/10.1097/AS9.0000000000000423 |
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| _version_ | 1832589728571981824 |
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| author | Nadayca Mateussi, PhD Michael P. Rogers, MD Emily A. Grimsley, MD Meagan Read, MD Rajavi Parikh, DO Ricardo Pietrobon, MD, PhD Paul C. Kuo, MD |
| author_facet | Nadayca Mateussi, PhD Michael P. Rogers, MD Emily A. Grimsley, MD Meagan Read, MD Rajavi Parikh, DO Ricardo Pietrobon, MD, PhD Paul C. Kuo, MD |
| author_sort | Nadayca Mateussi, PhD |
| collection | DOAJ |
| description | Objective:. This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users.
Background:. As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies.
Methods:. This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework.
Results:. This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies.
Conclusions:. Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future. |
| format | Article |
| id | doaj-art-d0196cd6d8964eabb52ceb14342962bc |
| institution | Kabale University |
| issn | 2691-3593 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Wolters Kluwer Health |
| record_format | Article |
| series | Annals of Surgery Open |
| spelling | doaj-art-d0196cd6d8964eabb52ceb14342962bc2025-01-24T09:18:39ZengWolters Kluwer HealthAnnals of Surgery Open2691-35932024-06-0152e42310.1097/AS9.0000000000000423202406000-00019Clinical Applications of Machine LearningNadayca Mateussi, PhD0Michael P. Rogers, MD1Emily A. Grimsley, MD2Meagan Read, MD3Rajavi Parikh, DO4Ricardo Pietrobon, MD, PhD5Paul C. Kuo, MD6* From the Sporedata, Durham, NC† Onetomap Analytics, Department of Surgery, University of South Florida, Tampa, FL.† Onetomap Analytics, Department of Surgery, University of South Florida, Tampa, FL.† Onetomap Analytics, Department of Surgery, University of South Florida, Tampa, FL.† Onetomap Analytics, Department of Surgery, University of South Florida, Tampa, FL.* From the Sporedata, Durham, NC† Onetomap Analytics, Department of Surgery, University of South Florida, Tampa, FL.Objective:. This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background:. As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. Methods:. This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework. Results:. This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies. Conclusions:. Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future.http://journals.lww.com/10.1097/AS9.0000000000000423 |
| spellingShingle | Nadayca Mateussi, PhD Michael P. Rogers, MD Emily A. Grimsley, MD Meagan Read, MD Rajavi Parikh, DO Ricardo Pietrobon, MD, PhD Paul C. Kuo, MD Clinical Applications of Machine Learning Annals of Surgery Open |
| title | Clinical Applications of Machine Learning |
| title_full | Clinical Applications of Machine Learning |
| title_fullStr | Clinical Applications of Machine Learning |
| title_full_unstemmed | Clinical Applications of Machine Learning |
| title_short | Clinical Applications of Machine Learning |
| title_sort | clinical applications of machine learning |
| url | http://journals.lww.com/10.1097/AS9.0000000000000423 |
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