Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging
This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1445204/full |
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| author | Priyanka Khalate Shilpa Gite Shilpa Gite Biswajeet Pradhan Chang-Wook Lee |
| author_facet | Priyanka Khalate Shilpa Gite Shilpa Gite Biswajeet Pradhan Chang-Wook Lee |
| author_sort | Priyanka Khalate |
| collection | DOAJ |
| description | This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services. |
| format | Article |
| id | doaj-art-1cb8faa6ffd84f70aeb643b698b08fb1 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-1cb8faa6ffd84f70aeb643b698b08fb12024-12-02T08:45:56ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-12-011210.3389/fphy.2024.14452041445204Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imagingPriyanka Khalate0Shilpa Gite1Shilpa Gite2Biswajeet Pradhan3Chang-Wook Lee4AI and ML Department, Symbiosis Institute of Technology (Pune Campus), Symbiosis International Deemed University, Pune, IndiaAI and ML Department, Symbiosis Institute of Technology (Pune Campus), Symbiosis International Deemed University, Pune, IndiaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology (Pune Campus), Symbiosis International Deemed University, Pune, IndiaCentre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, AustraliaDepartment of Science Education, Kangwon National University, Chuncheon, Republic of KoreaThis article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services.https://www.frontiersin.org/articles/10.3389/fphy.2024.1445204/fullnatural language processingclinical decision supporthealthcareelectronic medical recordsinterdisciplinary collaboration |
| spellingShingle | Priyanka Khalate Shilpa Gite Shilpa Gite Biswajeet Pradhan Chang-Wook Lee Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging Frontiers in Physics natural language processing clinical decision support healthcare electronic medical records interdisciplinary collaboration |
| title | Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging |
| title_full | Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging |
| title_fullStr | Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging |
| title_full_unstemmed | Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging |
| title_short | Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging |
| title_sort | advancements and gaps in natural language processing and machine learning applications in healthcare a comprehensive review of electronic medical records and medical imaging |
| topic | natural language processing clinical decision support healthcare electronic medical records interdisciplinary collaboration |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1445204/full |
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