Digital orthopedics in the new AI era: from ASIA aspect

Wang, Yan.

doi:10.1186/s42836-023-00220-4

ScienceOpen: research and publishing network

For Publishers

For Researchers

Blog
About

Search
Advanced search

views

recommends

Record: found
Abstract: found
Article: found

Is Open Access

Digital orthopedics in the new AI era: from ASIA aspect

editorial

Author(s): Yan Wang

Publication date (Electronic): 13 December 2023

Journal: Arthroplasty

Publisher: BioMed Central

Keywords: Artificial intelligence, Orthopedics, Robotics, Medical education

Read this article at

ScienceOpen Publisher PMC

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

This editorial explores the transformative impact of artificial intelligence (AI) on orthopedics, with a particular focus on advancements in Asia. It delves into the integration of AI in hospitals, advanced applications in China, and future expectations. The discussion is underpinned by an examination of AI's role in assisted diagnosis, treatment planning, surgical navigation, predictive analysis, and post-operative rehabilitation monitoring.

Related collections

Most cited references 13

Record: found
Abstract: found
Article: found

Is Open Access

Health system-scale language models are all-purpose prediction engines

Lavender Yao Jiang, Xujin Chris Liu, Nima Pour Nejatian … (2023)

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment 1 – 3 . Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing 4 , 5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7–94.9%, with an improvement of 5.36–14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care. A clinical language model trained on unstructured clinical notes from the electronic health record enhances prediction of clinical and operational events.

0 comments Cited 108 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review

Lok Lee, Ping Keung Chan, Chunyi Wen … (2022)

Background Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. Methods PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: ‘artificial intelligence’, ‘machine learning’, ‘knee’, ‘osteoarthritis’, and ‘arthroplasty’. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. Results Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. Conclusions Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.

0 comments Cited 28 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Andrew Kurmis, Jamie R. Ianunzio (2022)

Technological advances continue to evolve at a breath-taking pace. Computer-navigation, robot-assistance and three-dimensional digital planning have become commonplace in many parts of the world. With near exponential advances in computer processing capacity, and the advent, progressive understanding and refinement of software algorithms, medicine and orthopaedic surgery have begun to delve into artificial intelligence (AI) systems. While for some, such applications still seem in the realm of science fiction, these technologies are already in selective clinical use and are likely to soon see wider uptake. The purpose of this structured review was to provide an understandable summary to non-academic orthopaedic surgeons, exploring key definitions and basic development principles of AI technology as it currently stands. To ensure content validity and representativeness, a structured, systematic review was performed following the accepted PRISMA principles. The paper concludes with a forward-look into heralded and potential applications of AI technology in orthopedic surgery. While not intended to be a detailed technical description of the complex processing that underpins AI applications, this work will take a small step forward in demystifying some of the commonly-held misconceptions regarding AI and its potential benefits to patients and surgeons. With evidence-supported broader awareness, we aim to foster an open-mindedness among clinicians toward such technologies in the future.

0 comments Cited 24 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Yan Wang: wangyan@asiaarthroplasty.org

Journal

Journal ID (nlm-ta): Arthroplasty

Journal ID (iso-abbrev): Arthroplasty

Title: Arthroplasty

Publisher: BioMed Central (London )

ISSN (Electronic): 2524-7948

Publication date (Electronic): 13 December 2023

Publication date PMC-release: 13 December 2023

Publication date Collection: 2023

Volume: 5

Electronic Location Identifier: 61

Affiliations

Department of Orthopaedics, General Hospital of Chinese People’s Liberation Army, ( https://ror.org/05tf9r976) Beijing, 100853 China

Article

Publisher ID: 220

DOI: 10.1186/s42836-023-00220-4

PMC ID: 10717913

PubMed ID: 38087379

SO-VID: 3acdaa02-4f94-4c46-8607-46016752967a

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 1 November 2023

Date accepted : 7 November 2023

Custom metadata

Keywords: artificial intelligence,orthopedics,robotics,medical education

Data availability:

Keywords: artificial intelligence, orthopedics, robotics, medical education

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Most referenced authors 98

See all reference authors

Digital orthopedics in the new AI era: from ASIA aspect

Read this article at

Abstract

Related collections

Orthopedics

Most cited references 13

Health system-scale language models are all-purpose prediction engines

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Author and article information

Contributors

Journal

Affiliations

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 32

Most referenced authors 98