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Journal of Clinical Nursing Jul 2023Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an... (Review)
Review
BACKGROUND
Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited.
OBJECTIVES
To synthesise literature on AI in nursing and midwifery.
METHODS
CINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting.
RESULTS
One hundred and forty articles were included. Nurses' and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions.
CONCLUSION
Digital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare.
RELEVANCE FOR CLINICAL PRACTICE
Adult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.
Topics: Pregnancy; Adult; Humans; Child; Female; Midwifery; Artificial Intelligence; Delivery of Health Care; Curriculum
PubMed: 35908207
DOI: 10.1111/jocn.16478 -
Annals of Oncology : Official Journal... Jan 2024The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI... (Review)
Review
BACKGROUND
The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.
MATERIALS AND METHODS
We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.
RESULTS
A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.
CONCLUSION
AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
Topics: Humans; Artificial Intelligence; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Medical Oncology
PubMed: 37879443
DOI: 10.1016/j.annonc.2023.10.125 -
The Journal of Prosthetic Dentistry Dec 2023Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and... (Review)
Review
STATEMENT OF PROBLEM
Artificial intelligence (AI) models have been developed for periodontal applications, including diagnosing gingivitis and periodontal disease, but their accuracy and maturity of the technology remain unclear.
PURPOSE
The purpose of this systematic review was to evaluate the performance of the AI models for detecting dental plaque and diagnosing gingivitis and periodontal disease.
MATERIAL AND METHODS
A review was performed in 4 databases: MEDLINE/PubMed, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies were classified into 4 groups: detecting dental plaque, diagnosis of gingivitis, diagnosis of periodontal disease from intraoral images, and diagnosis of alveolar bone loss from periapical, bitewing, and panoramic radiographs. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus.
RESULTS
Twenty-four articles were included: 2 studies developed AI models for detecting plaque, resulting in accuracy ranging from 73.6% to 99%; 7 studies assessed the ability to diagnose gingivitis from intraoral photographs reporting an accuracy between 74% and 78.20%; 1 study used fluorescent intraoral images to diagnose gingivitis reporting 67.7% to 73.72% accuracy; 3 studies assessed the ability to diagnose periodontal disease from intraoral photographs with an accuracy between 47% and 81%, and 11 studies evaluated the performance of AI models for detecting alveolar bone loss from radiographic images reporting an accuracy between 73.4% and 99%.
CONCLUSIONS
AI models for periodontology applications are still in development but might provide a powerful diagnostic tool.
Topics: Humans; Dental Plaque; Alveolar Bone Loss; Artificial Intelligence; Periodontal Diseases; Gingivitis
PubMed: 35300850
DOI: 10.1016/j.prosdent.2022.01.026 -
Annals of Internal Medicine Sep 2023Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Artificial intelligence computer-aided detection (CADe) of colorectal neoplasia during colonoscopy may increase adenoma detection rates (ADRs) and reduce adenoma miss rates, but it may increase overdiagnosis and overtreatment of nonneoplastic polyps.
PURPOSE
To quantify the benefits and harms of CADe in randomized trials.
DESIGN
Systematic review and meta-analysis. (PROSPERO: CRD42022293181).
DATA SOURCES
Medline, Embase, and Scopus databases through February 2023.
STUDY SELECTION
Randomized trials comparing CADe-assisted with standard colonoscopy for polyp and cancer detection.
DATA EXTRACTION
Adenoma detection rate (proportion of patients with ≥1 adenoma), number of adenomas detected per colonoscopy, advanced adenoma (≥10 mm with high-grade dysplasia and villous histology), number of serrated lesions per colonoscopy, and adenoma miss rate were extracted as benefit outcomes. Number of polypectomies for nonneoplastic lesions and withdrawal time were extracted as harm outcomes. For each outcome, studies were pooled using a random-effects model. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework.
DATA SYNTHESIS
Twenty-one randomized trials on 18 232 patients were included. The ADR was higher in the CADe group than in the standard colonoscopy group (44.0% vs. 35.9%; relative risk, 1.24 [95% CI, 1.16 to 1.33]; low-certainty evidence), corresponding to a 55% (risk ratio, 0.45 [CI, 0.35 to 0.58]) relative reduction in miss rate (moderate-certainty evidence). More nonneoplastic polyps were removed in the CADe than the standard group (0.52 vs. 0.34 per colonoscopy; mean difference [MD], 0.18 polypectomy [CI, 0.11 to 0.26 polypectomy]; low-certainty evidence). Mean inspection time increased only marginally with CADe (MD, 0.47 minute [CI, 0.23 to 0.72 minute]; moderate-certainty evidence).
LIMITATIONS
This review focused on surrogates of patient-important outcomes. Most patients, however, may consider cancer incidence and cancer-related mortality important outcomes. The effect of CADe on such patient-important outcomes remains unclear.
CONCLUSION
The use of CADe for polyp detection during colonoscopy results in increased detection of adenomas but not advanced adenomas and in higher rates of unnecessary removal of nonneoplastic polyps.
PRIMARY FUNDING SOURCE
European Commission Horizon 2020 Marie Skłodowska-Curie Individual Fellowship.
Topics: Humans; Artificial Intelligence; Colorectal Neoplasms; Computers; Colonoscopy; Databases, Factual
PubMed: 37639719
DOI: 10.7326/M22-3678 -
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.Alzheimer's & Dementia : the Journal of... Dec 2023Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. (Review)
Review
INTRODUCTION
Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.
METHODS
We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.
RESULTS
A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.
DISCUSSION
The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.
HIGHLIGHTS
There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Topics: Humans; Alzheimer Disease; Prognosis; Artificial Intelligence; Neurodegenerative Diseases; Brain; Neuroimaging
PubMed: 37563912
DOI: 10.1002/alz.13412 -
Advances in Therapy Aug 2023Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to... (Meta-Analysis)
Meta-Analysis
INTRODUCTION
Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.
METHODS
Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.
RESULTS
Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.
CONCLUSION
At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.
Topics: Humans; Artificial Intelligence; Head and Neck Neoplasms; Machine Learning; Prospective Studies; Research Design
PubMed: 37291378
DOI: 10.1007/s12325-023-02527-9 -
World Journal of Emergency Surgery :... Dec 2023To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional... (Review)
Review
BACKGROUND
To assess the efficacy of artificial intelligence (AI) models in diagnosing and prognosticating acute appendicitis (AA) in adult patients compared to traditional methods. AA is a common cause of emergency department visits and abdominal surgeries. It is typically diagnosed through clinical assessments, laboratory tests, and imaging studies. However, traditional diagnostic methods can be time-consuming and inaccurate. Machine learning models have shown promise in improving diagnostic accuracy and predicting outcomes.
MAIN BODY
A systematic review following the PRISMA guidelines was conducted, searching PubMed, Embase, Scopus, and Web of Science databases. Studies were evaluated for risk of bias using the Prediction Model Risk of Bias Assessment Tool. Data points extracted included model type, input features, validation strategies, and key performance metrics.
RESULTS
In total, 29 studies were analyzed, out of which 21 focused on diagnosis, seven on prognosis, and one on both. Artificial neural networks (ANNs) were the most commonly employed algorithm for diagnosis. Both ANN and logistic regression were also widely used for categorizing types of AA. ANNs showed high performance in most cases, with accuracy rates often exceeding 80% and AUC values peaking at 0.985. The models also demonstrated promising results in predicting postoperative outcomes such as sepsis risk and ICU admission. Risk of bias was identified in a majority of studies, with selection bias and lack of internal validation being the most common issues.
CONCLUSION
AI algorithms demonstrate significant promise in diagnosing and prognosticating AA, often surpassing traditional methods and clinical scores such as the Alvarado scoring system in terms of speed and accuracy.
Topics: Adult; Humans; Artificial Intelligence; Appendicitis; Prognosis; Algorithms; Machine Learning; Acute Disease
PubMed: 38114983
DOI: 10.1186/s13017-023-00527-2 -
British Journal of Sports Medicine Oct 2023Intention is the proximal antecedent of physical activity in many popular psychological models. Despite the utility of these models, the discrepancy between intention... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Intention is the proximal antecedent of physical activity in many popular psychological models. Despite the utility of these models, the discrepancy between intention and actual behaviour, known as the intention-behaviour gap, is a central topic of current basic and applied research. The purpose of this meta-analysis was to quantify intention-behaviour profiles and the intention-behaviour gap.
DESIGN
Systematic review and meta-analysis.
DATA SOURCES
Literature search was conducted in June 2022 and updated in February 2023 in five databases.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Eligible studies included a measure of physical activity, an assessment of physical activity intention and the employment of the intention-behaviour relationship into profile quadrants. Only papers published in the English language and in peer-reviewed journals were considered. Screening was assisted by the artificial intelligence tool ASReview.
RESULTS
Twenty-five independent samples were selected from 22 articles including a total of N=29 600. Random-effects meta-analysis revealed that 26.0% of all participants were non-intenders not exceeding their intentions, 4.2% were non-intenders who exceeded their intentions, 33.0% were unsuccessful intenders and 38.7% were successful intenders. Based on the proportion of unsuccessful intenders to all intenders, the overall intention-behaviour gap was 47.6%.
CONCLUSION
The findings underscore that intention is a necessary, yet insufficient antecedent of physical activity for many. Successful translation of a positive intention into behaviour is nearly at chance. Incorporating mechanisms to overcome the intention-behaviour gap are recommended for clinical practice.
Topics: Humans; Intention; Artificial Intelligence; Exercise; Health Behavior
PubMed: 37460164
DOI: 10.1136/bjsports-2022-106640 -
Fertility and Sterility Jul 2023Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and... (Review)
Review
Despite the increasing number of assisted reproductive technologies based treatments being performed worldwide, there has been little improvement in fertilization and pregnancy outcomes. Male infertility is a major contributing factor, and sperm evaluation is a crucial step in diagnosis and treatment. However, embryologists face the daunting task of selecting a single sperm from millions in a sample based on various parameters, which can be time-consuming, subjective, and may even cause damage to the sperm, deeming them unusable for fertility treatments. Artificial intelligence algorithms have revolutionized the field of medicine, particularly in image processing, because of their discerning abilities, efficacy, and reproducibility. Artificial intelligence algorithms have the potential to address the challenges of sperm selection with their large-data processing capabilities and high objectivity. These algorithms could provide valuable assistance to embryologists in sperm analysis and selection. Furthermore, these algorithms could continue to improve over time as larger and more robust datasets become available for their training.
Topics: Pregnancy; Female; Male; Humans; Artificial Intelligence; Reproducibility of Results; Semen; Spermatozoa; Infertility, Male
PubMed: 37236418
DOI: 10.1016/j.fertnstert.2023.05.157 -
Journal of Medical Internet Research Nov 2023Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety. Wearable artificial intelligence (AI), the combination of AI technology and wearable devices, has been widely used to detect and predict anxiety disorders automatically, objectively, and more efficiently.
OBJECTIVE
This systematic review and meta-analysis aims to assess the performance of wearable AI in detecting and predicting anxiety.
METHODS
Relevant studies were retrieved by searching 8 electronic databases and backward and forward reference list checking. In total, 2 reviewers independently carried out study selection, data extraction, and risk-of-bias assessment. The included studies were assessed for risk of bias using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-Revised. Evidence was synthesized using a narrative (ie, text and tables) and statistical (ie, meta-analysis) approach as appropriate.
RESULTS
Of the 918 records identified, 21 (2.3%) were included in this review. A meta-analysis of results from 81% (17/21) of the studies revealed a pooled mean accuracy of 0.82 (95% CI 0.71-0.89). Meta-analyses of results from 48% (10/21) of the studies showed a pooled mean sensitivity of 0.79 (95% CI 0.57-0.91) and a pooled mean specificity of 0.92 (95% CI 0.68-0.98). Subgroup analyses demonstrated that the performance of wearable AI was not moderated by algorithms, aims of AI, wearable devices used, status of wearable devices, data types, data sources, reference standards, and validation methods.
CONCLUSIONS
Although wearable AI has the potential to detect anxiety, it is not yet advanced enough for clinical use. Until further evidence shows an ideal performance of wearable AI, it should be used along with other clinical assessments. Wearable device companies need to develop devices that can promptly detect anxiety and identify specific time points during the day when anxiety levels are high. Further research is needed to differentiate types of anxiety, compare the performance of different wearable devices, and investigate the impact of the combination of wearable device data and neuroimaging data on the performance of wearable AI.
TRIAL REGISTRATION
PROSPERO CRD42023387560; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387560.
Topics: Humans; Artificial Intelligence; Anxiety; Anxiety Disorders; Algorithms; Databases, Factual
PubMed: 37938883
DOI: 10.2196/48754