-
Journal of Clinical Monitoring and... Apr 2024Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current... (Review)
Review
PURPOSE
Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.
METHODS
A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.
RESULTS
A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.
CONCLUSION
AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.
Topics: Animals; Humans; Anesthesiology; Artificial Intelligence; Anesthesia; Sample Size
PubMed: 37864754
DOI: 10.1007/s10877-023-01088-0 -
European Journal of Radiology Apr 2022Tracing muscle groups manually on CT to calculate body composition parameters and diagnose sarcopenia is costly and time consuming. Artificial Intelligence (AI) provides... (Meta-Analysis)
Meta-Analysis
PURPOSE
Tracing muscle groups manually on CT to calculate body composition parameters and diagnose sarcopenia is costly and time consuming. Artificial Intelligence (AI) provides an opportunity to automate this process. In this systematic review, we aimed to assess the performance of CT-based AI segmentation models used for body composition analysis.
METHOD
We systematically searched PubMed (MEDLINE), Embase, Web of Science and Scopus for studies published from January 1, 2011, to May 27, 2021. Studies using AI models for assessment of body composition and sarcopenia on CT scans were included. Excluded were studies that used muscle strength, physical performance data, DXA and MRI. Meta-analysis was conducted on the reported dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC) of AI models.
RESULTS
284 studies were identified, of which 24 could be included in the systematic review. Among them, 15 were included in the meta-analysis, all of which used deep learning. Deep learning models for skeletal muscle (SM) segmentation performed with a pooled DSC of 0.941 (95 %CI 0.923-0.959) and a pooled JSC of 0.967 (95 %CI 0.949-0.986). Additionally, a pooled DSC of 0.967 (95 %CI 0.958-0.978), 0.963 (95 %CI 0.957-0.969) and 0.970 (95 %CI 0.944-0.996) was observed for segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and bone, respectively. SM studies suffered from significant publication bias, and heterogeneity among the included studies was considerable.
CONCLUSIONS
CT-based deep learning models can facilitate the automated segmentation of body composition and aid in sarcopenia diagnosis. More rigorous guidelines and comparative studies are required to assess the efficacy of AI segmentation models before incorporating these into clinical practice.
Topics: Artificial Intelligence; Body Composition; Humans; Sarcopenia; Subcutaneous Fat; Tomography, X-Ray Computed
PubMed: 35183899
DOI: 10.1016/j.ejrad.2022.110218 -
Legal Medicine (Tokyo, Japan) Feb 2021Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence...
Forensic odontology (FO) mainly deals with the identification of the individual through the remains, which mainly includes teeth and jawbones. Artificial intelligence (AI) technology has proven to be a breakthrough in providing reliable information in decision making in forensic sciences. This systematic review aimed to report on the application and performance of AI technology in FO. The data was gathered through searching for the articles in the renowned search engines, which have been published between January 2000 - June 2020. QUADAS-2 was adopted for the risk of bias analysis of the included studies. AI technology has been widely applied in FO for identifying bite-marks, predicting mandibular morphology, gender determination, and age estimation. Most of these AI models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). The results of the studies are promising. Studies have reported that these models display accuracy and precision equivalent to that of the trained examiners. These models can be promising tools when identifying victims of mass disasters and as an additive aid in medico-legal situations.
Topics: Age Determination by Teeth; Artificial Intelligence; Body Remains; Deep Learning; Female; Forensic Dentistry; Humans; Machine Learning; Male; Neural Networks, Computer; Sex Determination Analysis
PubMed: 33341601
DOI: 10.1016/j.legalmed.2020.101826 -
Neurosurgery Aug 2018Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed.
BACKGROUND
Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed.
OBJECTIVE
To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence."
METHODS
A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature.
RESULTS
Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models (P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group.
CONCLUSION
We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.
Topics: Algorithms; Humans; Machine Learning; Neurosurgery; Neurosurgical Procedures; Surgery, Computer-Assisted
PubMed: 28945910
DOI: 10.1093/neuros/nyx384 -
Hospital Pediatrics Jan 2022Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for...
CONTEXT
Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.
OBJECTIVE
We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care.
DATA SOURCE
PubMed, IEEE Xplore, Cochrane, and Web of Science databases.
STUDY SELECTION
We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality).
DATA EXTRACTION
Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report.
RESULTS
Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization.
CONCLUSIONS
Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
Topics: Artificial Intelligence; Child; Critical Care; Delivery of Health Care; Humans; Infant, Newborn; Intensive Care Units, Neonatal; Outcome Assessment, Health Care
PubMed: 34890453
DOI: 10.1542/hpeds.2021-006094 -
European Journal of Oncology Nursing :... Feb 2024Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational... (Review)
Review
PURPOSE
Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing.
METHODS
CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.
RESULTS
Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models.
CONCLUSION
Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.
Topics: Humans; Female; Artificial Intelligence; Oncology Nursing; Educational Status; Medical Oncology; Ovarian Neoplasms
PubMed: 38310664
DOI: 10.1016/j.ejon.2024.102510 -
Journal of Orthodontics Jun 2024The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.
BACKGROUND
The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.
OBJECTIVES
To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT.
DESIGN
Systematic review.
DATA SOURCES
Unrestricted search of indexed databases and reference lists of included studies.
DATA SELECTION
Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included.
DATA EXTRACTION
Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively.
DATA SYNTHESIS
Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB.
LIMITATIONS
Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment.
CONCLUSION
AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT.
REGISTRATION
PROSPERO CRD42022366864.
Topics: Humans; Artificial Intelligence; Patient Care Planning; Orthognathic Surgical Procedures; Treatment Outcome
PubMed: 37772513
DOI: 10.1177/14653125231203743 -
Journal of the American Medical... Nov 2023To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict...
OBJECTIVE
To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.
METHODS
This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively.
RESULTS
Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model's performance. Reporting quality was poor, and a third of the studies were at high risk of bias.
CONCLUSIONS
AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication.
REGISTRATION
PROSPERO database (CRD42022331388).
Topics: Humans; Artificial Intelligence; Electronic Health Records
PubMed: 37659105
DOI: 10.1093/jamia/ocad168 -
The International Journal of Behavioral... Dec 2021This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were... (Review)
Review
BACKGROUND
This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes.
METHODS
In collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes.
RESULTS
The database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication.
CONCLUSION
Chatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future.
SYSTEMATIC REVIEW REGISTRATION
International Prospective Register of Systematic Reviews (PROSPERO): CRD42020216761 .
Topics: Artificial Intelligence; Diet, Healthy; Exercise; Female; Humans; Weight Loss
PubMed: 34895247
DOI: 10.1186/s12966-021-01224-6 -
Asian Pacific Journal of Cancer... Nov 2023Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas... (Meta-Analysis)
Meta-Analysis
INTRODUCTION
Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR.
METHODS
The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed.
RESULTS
A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias.
CONCLUSION
AI may improve colonoscopy result quality through improving PDR and ADR.
Topics: Humans; Adenoma; Artificial Intelligence; Colonoscopy; Colorectal Neoplasms; Databases, Factual
PubMed: 38019222
DOI: 10.31557/APJCP.2023.24.11.3655