Sports Medicine and Artificial Intelligence: A Primer

2021 ◽  
pp. 036354652110086
Author(s):  
Prem N. Ramkumar ◽  
Bryan C. Luu ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Benedict U. Nwachukwu ◽  
...  

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

Author(s):  
Roman David Bülow ◽  
Daniel Dimitrov ◽  
Peter Boor ◽  
Julio Saez-Rodriguez

AbstractIgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.


2017 ◽  
Vol 29 (6) ◽  
pp. 874-879 ◽  
Author(s):  
John Øvretveit ◽  
Lisa Zubkoff ◽  
Eugene C Nelson ◽  
Susan Frampton ◽  
Janne Lehmann Knudsen ◽  
...  

2019 ◽  
Author(s):  
Stephanie Loo ◽  
Chris Grasso ◽  
Jessica Glushkina ◽  
Justin McReynolds ◽  
William Lober ◽  
...  

BACKGROUND Electronic patient-reported outcome (ePRO) systems can improve health outcomes by detecting health issues or risk behaviors that may be missed when relying on provider elicitation. OBJECTIVE This study aimed to implement an ePRO system that administers key health questionnaires in an urban community health center in Boston, Massachusetts. METHODS An ePRO system that administers key health questionnaires was implemented in an urban community health center in Boston, Massachusetts. The system was integrated with the electronic health record so that medical providers could review and adjudicate patient responses in real-time during the course of the patient visit. This implementation project was accomplished through careful examination of clinical workflows and a graduated rollout process that was mindful of patient and clinical staff time and burden. Patients responded to questionnaires using a tablet at the beginning of their visit. RESULTS Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model in terms of applying technological innovation to streamline clinical processes and improve patient care. CONCLUSIONS Our program demonstrates that implementation of an ePRO system in a primary care setting is feasible, allowing for facilitation of patient-provider communication and care. Other community health centers can learn from our model for application of technological innovation to streamline clinical processes and improve patient care.


2021 ◽  
pp. 49-52
Author(s):  
Gaurvi Vikram Kamra ◽  
Ankur Sharma

The concept of "articial intelligence" (AI) refers to machines that are capable of executing human-like tasks. AI can also be dened as a eld concerned with computational models that can reason and act intelligently. Perspicacious software for data computation has become a necessity as the amount of documented information and patient data has increased dramatically. The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview. AI has been used in a variety of ways, from processing of data and locating relevant information to using neural networks for diagnosis and the introduction of augmented reality and virtual reality in dental education. AI-based apps will improve patient care by relieving the dental workforce of tedious routine duties, improving population health at lower costs, and eventually facilitating individualized, anticipatory, prophylactic, and collaborative dentistry. The convergence of AI and digitization has ushered in a new age in dentistry, with tremendously promising future prospects.The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview.


Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Glen Purnomo ◽  
Seng-Jin Yeo ◽  
Ming Han Lincoln Liow

AbstractArtificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.


2017 ◽  
Vol 5 (2) ◽  
pp. 1-280 ◽  
Author(s):  
Joanne Greenhalgh ◽  
Sonia Dalkin ◽  
Kate Gooding ◽  
Elizabeth Gibbons ◽  
Judy Wright ◽  
...  

BackgroundThe feedback of patient-reported outcome measures (PROMs) data is intended to support the care of individual patients and to act as a quality improvement (QI) strategy.ObjectivesTo (1) identify the ideas and assumptions underlying how individual and aggregated PROMs data are intended to improve patient care, and (2) review the evidence to examine the circumstances in which and processes through which PROMs feedback improves patient care.DesignTwo separate but related realist syntheses: (1) feedback of aggregate PROMs and performance data to improve patient care, and (2) feedback of individual PROMs data to improve patient care.InterventionsAggregate – feedback and public reporting of PROMs, patient experience data and performance data to hospital providers and primary care organisations. Individual – feedback of PROMs in oncology, palliative care and the care of people with mental health problems in primary and secondary care settings.Main outcome measuresAggregate – providers’ responses, attitudes and experiences of using PROMs and performance data to improve patient care. Individual – providers’ and patients’ experiences of using PROMs data to raise issues with clinicians, change clinicians’ communication practices, change patient management and improve patient well-being.Data sourcesSearches of electronic databases and forwards and backwards citation tracking.Review methodsRealist synthesis to identify, test and refine programme theories about when, how and why PROMs feedback leads to improvements in patient care.ResultsProviders were more likely to take steps to improve patient care in response to the feedback and public reporting of aggregate PROMs and performance data if they perceived that these data were credible, were aimed at improving patient care, and were timely and provided a clear indication of the source of the problem. However, implementing substantial and sustainable improvement to patient care required system-wide approaches. In the care of individual patients, PROMs function more as a tool to support patients in raising issues with clinicians than they do in substantially changing clinicians’ communication practices with patients. Patients valued both standardised and individualised PROMs as a tool to raise issues, but thought is required as to which patients may benefit and which may not. In settings such as palliative care and psychotherapy, clinicians viewed individualised PROMs as useful to build rapport and support the therapeutic process. PROMs feedback did not substantially shift clinicians’ communication practices or focus discussion on psychosocial issues; this required a shift in clinicians’ perceptions of their remit.Strengths and limitationsThere was a paucity of research examining the feedback of aggregate PROMs data to providers, and we drew on evidence from interventions with similar programme theories (other forms of performance data) to test our theories.ConclusionsPROMs data act as ‘tin openers’ rather than ‘dials’. Providers need more support and guidance on how to collect their own internal data, how to rule out alternative explanations for their outlier status and how to explore the possible causes of their outlier status. There is also tension between PROMs as a QI strategy versus their use in the care of individual patients; PROMs that clinicians find useful in assessing patients, such as individualised measures, are not useful as indicators of service quality.Future workFuture research should (1) explore how differently performing providers have responded to aggregate PROMs feedback, and how organisations have collected PROMs data both for individual patient care and to improve service quality; and (2) explore whether or not and how incorporating PROMs into patients’ electronic records allows multiple different clinicians to receive PROMs feedback, discuss it with patients and act on the data to improve patient care.Study registrationThis study is registered as PROSPERO CRD42013005938.FundingThe National Institute for Health Research Health Services and Delivery Research programme.


Gut ◽  
2019 ◽  
Vol 69 (4) ◽  
pp. 681-690 ◽  
Author(s):  
Cynthia Reichling ◽  
Julien Taieb ◽  
Valentin Derangere ◽  
Quentin Klopfenstein ◽  
Karine Le Malicot ◽  
...  

ObjectiveDiagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.DesignWe have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.ResultsWithin the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated ‘DGMuneS’, outperformed Immunoscore when used in estimating patients’ prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.ConclusionThese findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients’ prognosis.


2021 ◽  
Vol 10 (22) ◽  
pp. 5284
Author(s):  
Michael Feehan ◽  
Leah A. Owen ◽  
Ian M. McKinnon ◽  
Margaret M. DeAngelis

The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.


2019 ◽  
Vol 8 (3) ◽  
pp. 208-215
Author(s):  
I. V. Buzaev ◽  
V. V. Plechev ◽  
R. M. Galimova ◽  
A. R. Kireev ◽  
L. K. Yuldybaev ◽  
...  

Introduction. The widespread adoption of Artificial Intelligence (AI) technologies forms the core of the so-called Industrial Revolution 4.0.The aim of this study is to examine qualitative changes occurring over the last two years in the development of AI through an examination of trends in PubMed publications.Materials. All abstracts with keyword “artificial intelligence” were downloaded from PubMed database https://www.ncbi.nlm.nih.gov/pubmed/ in the form of .txt files. In order to produce a generalisation of topics, we classified present applications of AI in medicine. To this end, 78,420 abstracts, 5558 reviews, 304 randomised controlled trials, 247 multicentre studies and 4137 other publication types were extracted. (Figure 1). Next, the typical applications were classified.Results. Interest in the topic of AI in publications indexed in the PubMed library is increasing according to general innovation development principles. Along with English publications, the number of non-English publications continued to increase until 2018, represented especially by Chinese, German and French languages. By 2018, the number of non-English publications had started to decrease in favour of English publications. Implementations of AI are already being adopted in contemporary practice. Thus, AI tools have moved out of the theoretical realm to find mainstream application.Conclusions. Tools for machine learning have become widely available to working scientists over the last two years. Since this includes FDA-approved tools for general clinical practice, the change not only affects to researchers but also clinical practitioners. Medical imaging and analysis applications already approved for the most part demonstrate comparable accuracy with the human specialist. A classification of developed AI applications is presented in the article.


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