-
Diagnostics (Basel, Switzerland) Aug 2023The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the... (Review)
Review
The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.
PubMed: 37627975
DOI: 10.3390/diagnostics13162715 -
Journal of Medical Internet Research Nov 2023The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding... (Review)
Review
BACKGROUND
The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding various topics have been developed.
OBJECTIVE
We performed this study to assess the quality of guidelines, consensus statements, and standards in the field of AI for medicine and to provide a foundation for recommendations about the future development of AI guidelines.
METHODS
We searched 7 electronic databases from database establishment to April 6, 2022, and screened articles involving AI guidelines, consensus statements, and standards for eligibility. The AGREE II (Appraisal of Guidelines for Research & Evaluation II) and RIGHT (Reporting Items for Practice Guidelines in Healthcare) tools were used to assess the methodological and reporting quality of the included articles.
RESULTS
This systematic review included 19 guideline articles, 14 consensus statement articles, and 3 standard articles published between 2019 and 2022. Their content involved disease screening, diagnosis, and treatment; AI intervention trial reporting; AI imaging development and collaboration; AI data application; and AI ethics governance and applications. Our quality assessment revealed that the average overall AGREE II score was 4.0 (range 2.2-5.5; 7-point Likert scale) and the mean overall reporting rate of the RIGHT tool was 49.4% (range 25.7%-77.1%).
CONCLUSIONS
The results indicated important differences in the quality of different AI guidelines, consensus statements, and standards. We made recommendations for improving their methodological and reporting quality.
TRIAL REGISTRATION
PROSPERO International Prospective Register of Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360.
Topics: Humans; Artificial Intelligence; Consensus; Databases, Factual; Medicine; Guidelines as Topic
PubMed: 37991819
DOI: 10.2196/46089 -
International Journal of Surgery... Dec 2023Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities.
METHODS
Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized.
RESULTS
A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%).
CONCLUSION
AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
Topics: Adult; Humans; Artificial Intelligence; Sensitivity and Specificity; Pancreas; Endosonography; Pancreatic Neoplasms
PubMed: 37800594
DOI: 10.1097/JS9.0000000000000717 -
International Journal of Medical... Dec 2023Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other... (Review)
Review
UNLABELLED
Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.
METHODS
The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.
FINDINGS AND DISCUSSION
The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.
CONCLUSION
Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.
Topics: Humans; Artificial Intelligence; Retrospective Studies; Algorithms; Emergency Medicine; Machine Learning
PubMed: 37944275
DOI: 10.1016/j.ijmedinf.2023.105274 -
Journal of Clinical Medicine Aug 2023The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is... (Review)
Review
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
PubMed: 37629474
DOI: 10.3390/jcm12165432 -
Journal of Biomedical Physics &... Oct 2023Artificial neural network helps humans in a wide range of activities, such as sports. (Review)
Review
BACKGROUND
Artificial neural network helps humans in a wide range of activities, such as sports.
OBJECTIVE
This paper aims to investigate the effect of artificial intelligence on decision-making related to human gait and sports biomechanics, using computer-based software, and to investigate the impact of artificial intelligence on individuals' biomechanics during gait and sports performance.
MATERIAL AND METHODS
This review was conducted in compliance with the PRISMA guidelines. Abstracts and citations were identified through a search based on Science Direct, Google Scholar, PubMed, Elsevier, Springer Link, Web of Science, and Scopus search engines from 1995 up to 2023 to obtain relevant literature about the impact of artificial intelligence on biomechanics. A total of 1000 articles were found related to biomechanical characteristics of gait and sport and 26 articles were directly pertinent to the subject.
RESULTS
The extent of the application of artificial intelligence in sports biomechanics in various fields. In addition, various variables in the fields of kinematics, kinetics, and the field of time can be investigated based on artificial intelligence. Conventional computational techniques are limited by the inability to process data in its raw form. Artificial Intelligence (AI) and Machine Learning (ML) techniques can handle complex and high-dimensional data.
CONCLUSION
The utilization of specialized systems and neural networks in gait analysis has shown great potential in sports performance analysis. Integrating AI into this field would be a significant advancement in sport biomechanics. Coaches and athletes can develop more precise training regimens with specialized performance prediction models.
PubMed: 37868944
DOI: 10.31661/jbpe.v0i0.2305-1621 -
JCO Clinical Cancer Informatics Sep 2023Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
Most individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs that use artificial intelligence to simulate conversation, have emerged as a promising tool in health care and, more recently, as a potential tool for genetic cancer risk assessment and counseling. Here, we evaluated the existing literature on the use of chatbots in genetic cancer risk assessment and counseling.
METHODS
A systematic review was conducted using key electronic databases to identify studies which use chatbots for genetic cancer risk assessment and counseling. Eligible studies were further subjected to meta-analysis.
RESULTS
Seven studies met inclusion criteria, evaluating five distinct chatbots. Three studies evaluated a chatbot that could perform genetic cancer risk assessment, one study evaluated a chatbot that offered patient counseling, and three studies included both functions. The pooled estimated completion rate for the genetic cancer risk assessment was 36.7% (95% CI, 14.8 to 65.9). Two studies included comprehensive patient characteristics, and none involved a comparison group. Chatbots varied as to the involvement of a health care provider in the process of risk assessment and counseling.
CONCLUSION
Chatbots have been used to streamline genetic cancer risk assessment and counseling and hold promise for reducing barriers to genetic services. Data regarding user and nonuser characteristics are lacking, as are data regarding comparative effectiveness to usual care. Future research may consider the impact of chatbots on equitable access to genetic services.
Topics: Humans; Artificial Intelligence; Software; Counseling; Neoplastic Syndromes, Hereditary; Risk Assessment
PubMed: 37934933
DOI: 10.1200/CCI.23.00123 -
Cureus Oct 2023Barrett's esophagus (BE) remains a significant precursor to esophageal adenocarcinoma, requiring accurate and efficient diagnosis and management. The increasing... (Review)
Review
Barrett's esophagus (BE) remains a significant precursor to esophageal adenocarcinoma, requiring accurate and efficient diagnosis and management. The increasing application of machine learning (ML) technologies presents a transformative opportunity for diagnosing and treating BE. This systematic review evaluates the effectiveness and accuracy of machine learning technologies in BE diagnosis and management by conducting a comprehensive search across PubMed, Scopus, and Web of Science databases up to the year 2023. The studies were organized into five categories: computer-aided systems, natural language processing and text-based systems, deep learning on histology and biopsy images, real-time and video analysis, and miscellaneous studies. Results indicate high sensitivity and specificity across machine learning applications. Specifically, computer-aided systems showed sensitivities ranging from 84% to 100% and specificities from 64% to 90.7%. Natural language processing and text-based systems achieved an accuracy as high as 98.7%. Deep learning techniques applied to histology and biopsy images displayed sensitivities up to greater than 90% and a specificity of 100%. Furthermore, real-time and video analysis technologies demonstrated high performance with assessment speeds of up to 48 frames per second (fps) and a mean average precision of 75.3%. Overall, the reviewed literature underscores the growing capability and efficiency of machine learning technologies in diagnosing and managing Barrett's esophagus, often outperforming traditional diagnostic methods. These findings highlight the promising future role of machine learning in enhancing clinical practice and improving patient care for Barrett's esophagus.
PubMed: 38021699
DOI: 10.7759/cureus.47755 -
Antibiotics (Basel, Switzerland) Aug 2023The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI)... (Review)
Review
The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. (CRD42022329049).
PubMed: 37627713
DOI: 10.3390/antibiotics12081293 -
Journal of Clinical Medicine Dec 2023Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence... (Review)
Review
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
PubMed: 38202187
DOI: 10.3390/jcm13010180