scholarly journals Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

10.2196/25759 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e25759
Author(s):  
Jiamin Yin ◽  
Kee Yuan Ngiam ◽  
Hock Hai Teo
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Practice ◽  
Risk Analysis ◽  
Early Stage ◽  
Controlled Trial ◽  
Real Life ◽  
Disease Diagnosis ◽  
Clinical Settings ◽  
Clinical Implementation

Background Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. Objective The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice. Methods We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings. Results We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation. Conclusions This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

scholarly journals Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Jiamin Yin ◽  
Kee Yuan Ngiam ◽  
Hock Hai Teo
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Practice ◽  
Risk Analysis ◽  
Early Stage ◽  
Controlled Trial ◽  
Real Life ◽  
Disease Diagnosis ◽  
Clinical Settings ◽  
Clinical Implementation

BACKGROUND Artificial intelligence (AI) applications are growing at an unprecedented pace in health care, including disease diagnosis, triage or screening, risk analysis, surgical operations, and so forth. Despite a great deal of research in the development and validation of health care AI, only few applications have been actually implemented at the frontlines of clinical practice. OBJECTIVE The objective of this study was to systematically review AI applications that have been implemented in real-life clinical practice. METHODS We conducted a literature search in PubMed, Embase, Cochrane Central, and CINAHL to identify relevant articles published between January 2010 and May 2020. We also hand searched premier computer science journals and conferences as well as registered clinical trials. Studies were included if they reported AI applications that had been implemented in real-world clinical settings. RESULTS We identified 51 relevant studies that reported the implementation and evaluation of AI applications in clinical practice, of which 13 adopted a randomized controlled trial design and eight adopted an experimental design. The AI applications targeted various clinical tasks, such as screening or triage (n=16), disease diagnosis (n=16), risk analysis (n=14), and treatment (n=7). The most commonly addressed diseases and conditions were sepsis (n=6), breast cancer (n=5), diabetic retinopathy (n=4), and polyp and adenoma (n=4). Regarding the evaluation outcomes, we found that 26 studies examined the performance of AI applications in clinical settings, 33 studies examined the effect of AI applications on clinician outcomes, 14 studies examined the effect on patient outcomes, and one study examined the economic impact associated with AI implementation. CONCLUSIONS This review indicates that research on the clinical implementation of AI applications is still at an early stage despite the great potential. More research needs to assess the benefits and challenges associated with clinical AI applications through a more rigorous methodology.

Challenges and Opportunities in Implementing Pharmacogenetic Testing in Clinical Settings

2021 ◽  
Vol 61 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Wan-Chun Chang ◽  
Reo Tanoshima ◽  
Colin J.D. Ross ◽  
Bruce C. Carleton
Keyword(s):  
Health Care ◽  
Clinical Practice ◽  
Drug Safety ◽  
Practice Guidelines ◽  
Genetic Variants ◽  
Clinical Settings ◽  
Drug Selection ◽  
Clinical Implementation ◽  
Pharmacogenetic Testing ◽  
Challenges And Opportunities

The clinical implementation of pharmacogenetic biomarkers continues to grow as new genetic variants associated with drug outcomes are discovered and validated. The number of drug labels that contain pharmacogenetic information also continues to expand. Published, peer-reviewed clinical practice guidelines have also been developed to support the implementation of pharmacogenetic tests. Incorporating pharmacogenetic information into health care benefits patients as well as clinicians by improving drug safety and reducing empiricism in drug selection. Barriers to the implementation of pharmacogenetic testing remain. This review explores current pharmacogenetic implementation initiatives with a focus on the challenges of pharmacogenetic implementation and potential opportunities to overcome these challenges.

scholarly journals Economic theory meets clinical practice: moral hazard

2019 ◽  
Vol 4 ◽  
Author(s):  
Olga Vasylyeva
Keyword(s):  
Health Care ◽  
Economic Theory ◽  
Clinical Practice ◽  
Moral Hazard ◽  
Real Life ◽  
Medical Services ◽  
Health Care Economics ◽  
Services Utilization ◽  
Field Of Vision ◽  
Concept Of Health

Economic theory must be tested by reality to prove that the goal is achievable and reproducible. However, health care economics do not always theorize based on modern-day medical practice, which results in detachment of some economic recommendations from real-life medicine. The theory of “moral hazard” assumes that patients will utilize more medical services if they transfer the risk of cost to insurances. In this article, we will revisit the understanding of appropriate avoidance of medical services and incorporate no-show rate, avoidance of care, and nonadherence into the concept of health services utilization. The primary goal of this interdisciplinary commentary is to bridge economic theory with clinical practice. It is written from the perspective of a clinical practitioner, who applies realities of everyday medicine to economic reasoning. The author hopes that this abstract will extend the field of vision of health care economics.   

scholarly journals Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice

2020 ◽  
Vol 11 (3) ◽  
pp. 367-376 ◽  
Author(s):  
Colin Birkenbihl ◽  
◽  
Mohammad Asif Emon ◽  
Henri Vrooman ◽  
Sarah Westwood ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cohort Study ◽  
Clinical Practice ◽  
External Validation ◽  
Disease Diagnosis ◽  
Study Data ◽  
Prediction Performance ◽  
Predictive Diagnostics ◽  
Great Opportunity ◽  
Clinical Cohort

Abstract Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

scholarly journals Diagnosis of Early Alzheimer’s Disease: Clinical Practice in 2021

2021 ◽  
pp. 1-16
Author(s):  
A.P. Porsteinsson ◽  
R.S. Isaacson ◽  
S. Knox ◽  
M.N. Sabbagh ◽  
I. Rubino
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Practice ◽  
Early Stage ◽  
Current Model ◽  
Healthcare Providers ◽  
Disease Diagnosis ◽  
Lifestyle Changes ◽  
Behavioral Impairment ◽  
Alzheimer’S Disease Dementia

Alzheimer’s disease is a progressive, irreversible neurodegenerative disease impacting cognition, function, and behavior. Alzheimer’s disease progresses along a continuum from preclinical disease, to mild cognitive and/or behavioral impairment and then Alzheimer’s disease dementia. Recently, clinicians have been encouraged to diagnose Alzheimer’s earlier, before patients have progressed to Alzheimer’s disease dementia. The early and accurate detection of Alzheimer’s disease-associated symptoms and underlying disease pathology by clinicians is fundamental for the screening, diagnosis, and subsequent management of Alzheimer’s disease patients. It also enables patients and their caregivers to plan for the future and make appropriate lifestyle changes that could help maintain their quality of life for longer. Unfortunately, detecting early-stage Alzheimer’s disease in clinical practice can be challenging and is hindered by several barriers including constraints on clinicians’ time, difficulty accurately diagnosing Alzheimer’s pathology, and that patients and healthcare providers often dismiss symptoms as part of the normal aging process. As the prevalence of this disease continues to grow, the current model for Alzheimer’s disease diagnosis and patient management will need to evolve to integrate care across clinical disciplines and the disease continuum, beginning with primary care. This review summarizes the importance of establishing an early diagnosis of Alzheimer’s disease, related practical ‘how-to’ guidance and considerations, and tools that can be used by healthcare providers throughout the diagnostic journey.

scholarly journals Current Trends of Nanobiosensors for Point-of-Care Diagnostics

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Naumih M. Noah ◽  
Peter M. Ndangili
Keyword(s):  
Health Care ◽  
Care Management ◽  
Early Stage ◽  
Point Of Care ◽  
Health Care Management ◽  
Disease Onset ◽  
Disease Diagnosis ◽  
Point Of Care Testing ◽  
Quality Health Care ◽  
Current Trends

In order to provide better-quality health care, it is very important that high standards of health care management are achieved by making timely decisions based on rapid diagnostics, smart data analysis, and informatics analysis. Point-of-care testing ensures fast detection of analytes near to the patients facilitating a better disease diagnosis, monitoring, and management. It also enables quick medical decisions since the diseases can be diagnosed at an early stage which leads to improved health outcomes for the patients enabling them to start early treatment. In the recent past, various potential point-of-care devices have been developed and they are paving the way to next-generation point-of-care testing. Biosensors are very critical components of point-of-care devices since they are directly responsible for the bioanalytical performance of an essay. As such, they have been explored for their prospective point-of-care applications necessary for personalized health care management since they usually estimate the levels of biological markers or any chemical reaction by producing signals mainly associated with the concentration of an analyte and hence can detect disease causing markers such as body fluids. Their high selectivity and sensitivity have allowed for early diagnosis and management of targeted diseases; hence, facilitating timely therapy decisions and combination with nanotechnology can improve assessment of the disease onset and its progression and help to plan for treatment of many diseases. In this review, we explore how nanotechnology has been utilized in the development of nanosensors and the current trends of these nanosensors for point-of-care diagnosis of various diseases.

scholarly journals Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons

2021 ◽  
Vol 14 ◽  
pp. 175628482110177
Author(s):  
Gian Eugenio Tontini ◽  
Alessandro Rimondi ◽  
Marta Vernero ◽  
Helmut Neumann ◽  
Maurizio Vecchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Gastrointestinal Endoscopy ◽  
Early Stage ◽  
Real Life ◽  
Added Value ◽  
Clinical Scenarios ◽  
Inflammatory Bowel

Introduction: Since the advent of artificial intelligence (AI) in clinical studies, luminal gastrointestinal endoscopy has made great progress, especially in the detection and characterization of neoplastic and preneoplastic lesions. Several studies have recently shown the potential of AI-driven endoscopy for the investigation of inflammatory bowel disease (IBD). This systematic review provides an overview of the current position and future potential of AI in IBD endoscopy. Methods: A systematic search was carried out in PubMed and Scopus up to 2 December 2020 using the following search terms: artificial intelligence, machine learning, computer-aided, inflammatory bowel disease, ulcerative colitis (UC), Crohn’s disease (CD). All studies on human digestive endoscopy were included. A qualitative analysis and a narrative description were performed for each selected record according to the Joanna Briggs Institute methodologies and the PRISMA statement. Results: Of 398 identified records, 18 were ultimately included. Two-thirds of these (12/18) were published in 2020 and most were cross-sectional studies (15/18). No relevant bias at the study level was reported, although the risk of publication bias across studies cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most of the AI systems involved convolutional neural network, random forest and deep neural network architecture. Most studies focused on capsule endoscopy readings in CD ( n = 5) and on the AI-assisted assessment of mucosal activity in UC ( n = 10) for automated endoscopic scoring or real-time prediction of histological disease. Discussion: AI-assisted endoscopy in IBD is a rapidly evolving research field with promising technical results and additional benefits when tested in an experimental clinical scenario. External validation studies being conducted in large and prospective cohorts in real-life clinical scenarios will help confirm the added value of AI in assessing UC mucosal activity and in CD capsule reading. Plain language summary Artificial intelligence for inflammatory bowel disease endoscopy Artificial intelligence (AI) is a promising technology in many areas of medicine. In recent years, AI-assisted endoscopy has been introduced into several research fields, including inflammatory bowel disease (IBD) endoscopy, with promising applications that have the potential to revolutionize clinical practice and gastrointestinal endoscopy. We have performed the first systematic review of AI and its application in the field of IBD and endoscopy. A formal process of paper selection and analysis resulted in the assessment of 18 records. Most of these (12/18) were published in 2020 and were cross-sectional studies (15/18). No relevant biases were reported. All studies showed positive results concerning the novel technology evaluated, so the risk of publication bias cannot be ruled out at this early stage. Eleven records dealt with UC, five with CD and two with both. Most studies focused on capsule endoscopy reading in CD patients ( n = 5) and on AI-assisted assessment of mucosal activity in UC patients ( n = 10) for automated endoscopic scoring and real-time prediction of histological disease. We found that AI-assisted endoscopy in IBD is a rapidly growing research field. All studies indicated promising technical results. When tested in an experimental clinical scenario, AI-assisted endoscopy showed it could potentially improve the management of patients with IBD. Confirmatory evidence from real-life clinical scenarios should be obtained to verify the added value of AI-assisted IBD endoscopy in assessing UC mucosal activity and in CD capsule reading.

scholarly journals Artificial Intelligent in Healthcare

2019 ◽  
Vol 11 (2) ◽  
pp. 125-35
Author(s):  
Anna Meiliana ◽  
Nurrani Mustika Dewi ◽  
Andi Wijaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Complex Disease ◽  
Disease Diagnosis ◽  
Large Set ◽  
Health Records ◽  
Treatment Data ◽  
Precision Health

BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data

scholarly journals Artificial Intelligence Support for Skin Lesion Triage in Primary Care and Dermatology (Preprint)

2021 ◽  
Author(s):  
Harmony Thompson ◽  
Amanda Oakley ◽  
Michael B Jameson ◽  
Adrian Bowling
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Care ◽  
Skin Cancer ◽  
Sensitivity And Specificity ◽  
Health Care Access ◽  
Real Life ◽  
Primary Care Providers ◽  
Skin Lesions ◽  
Care Providers

BACKGROUND Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. OBJECTIVE We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). METHODS This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. RESULTS Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). CONCLUSIONS The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care.

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