Artificial intelligence-directed acupuncture: a review
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Yulin Wang
- Xiuming Shi
- Thomas Efferth
- Dong Shang
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000819009400001&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1186/s13020-022-00636-1
- Externe Identifier
- Clarivate Analytics Document Solution ID: 2O4CX
- PubMed Identifier: 35765020
- ISSN
- 1749-8546
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- CHINESE MEDICINE
- Schlüsselwörter
- Artificial intelligence
- Acupuncture
- Machine learning
- Traditional Chinese medicine
- Artikelnummer
- ARTN 80
- Datum der Veröffentlichung
- 2022
- Status
- Published
- Titel
- Artificial intelligence-directed acupuncture: a review
- Sub types
- Review
- Ausgabe der Zeitschrift
- 17
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Abstract
- <jats:title>Abstract</jats:title><jats:p>Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.</jats:p>
- Autoren
- Yulin Wang
- Xiuming Shi
- Thomas Efferth
- Dong Shang
- DOI
- 10.1186/s13020-022-00636-1
- eISSN
- 1749-8546
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Chinese Medicine
- Sprache
- en
- Artikelnummer
- 80
- Online publication date
- 2022
- Datum der Veröffentlichung
- 2022
- Status
- Published
- Herausgeber
- Springer Science and Business Media LLC
- Herausgeber URL
- http://dx.doi.org/10.1186/s13020-022-00636-1
- Datum der Datenerfassung
- 2022
- Titel
- Artificial intelligence-directed acupuncture: a review
- Ausgabe der Zeitschrift
- 17
Datenquelle: Crossref
- Abstract
- Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.
- Addresses
- College of Pharmacy, Dalian Medical University, 9 South Lvshun Road Western Section, Dalian, 116044, People's Republic of China. wangyulin1971@126.com.
- Autoren
- Yulin Wang
- Xiuming Shi
- Thomas Efferth
- Dong Shang
- DOI
- 10.1186/s13020-022-00636-1
- eISSN
- 1749-8546
- Externe Identifier
- PubMed Identifier: 35765020
- PubMed Central ID: PMC9237974
- Open access
- true
- ISSN
- 1749-8546
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Chinese medicine
- Sprache
- eng
- Medium
- Electronic
- Online publication date
- 2022
- Open access status
- Open Access
- Paginierung
- 80
- Datum der Veröffentlichung
- 2022
- Status
- Published
- Publisher licence
- CC BY
- Datum der Datenerfassung
- 2022
- Titel
- Artificial intelligence-directed acupuncture: a review.
- Sub types
- review-article
- Review
- Journal Article
- Ausgabe der Zeitschrift
- 17
Files
https://cmjournal.biomedcentral.com/counter/pdf/10.1186/s13020-022-00636-1 https://europepmc.org/articles/PMC9237974?pdf=render
Datenquelle: Europe PubMed Central
- Abstract
- Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.
- Date of acceptance
- 2022
- Autoren
- Yulin Wang
- Xiuming Shi
- Thomas Efferth
- Dong Shang
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/35765020
- DOI
- 10.1186/s13020-022-00636-1
- Externe Identifier
- PubMed Central ID: PMC9237974
- ISSN
- 1749-8546
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Chin Med
- Schlüsselwörter
- Acupuncture
- Artificial intelligence
- Machine learning
- Traditional Chinese medicine
- Sprache
- eng
- Country
- England
- Paginierung
- 80
- PII
- 10.1186/s13020-022-00636-1
- Datum der Veröffentlichung
- 2022
- Status
- Published online
- Titel
- Artificial intelligence-directed acupuncture: a review.
- Sub types
- Journal Article
- Review
- Ausgabe der Zeitschrift
- 17
Datenquelle: PubMed
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