Artificial intelligence-directed acupuncture: a review

Publication type:
Journal article
Metadata:
Autoren
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

Data source: Web of Science (Lite)

Other metadata sources:
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
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

Data source: 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
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

Data source: 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
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

Data source: PubMed

Beziehungen:
Property of

Tools