scholarly journals Artificial intelligence techniques that may be applied to primary care data to facilitate the earlier diagnosis of cancer: a systematic review (Preprint)

10.2196/23483 ◽  
2020 ◽  
Author(s):  
Owain Tudor Jones ◽  
Natalia Calanzani ◽  
Smiji Saji ◽  
Stephen W Duffy ◽  
Jon Emery ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Primary Care ◽  
Artificial Intelligence Techniques ◽  
Primary Care Data ◽  
Diagnosis Of Cancer

scholarly journals Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry—A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Naseer Ahmed ◽  
Maria Shakoor Abbasi ◽  
Filza Zuberi ◽  
Warisha Qamar ◽  
Mohamad Syahrizal Bin Halim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Full Text ◽  
Patient Management ◽  
English Language ◽  
Clinical Performance ◽  
Research Question ◽  
Time Saving ◽  
Artificial Intelligence Techniques ◽  
Clinical Scenarios

Objective. The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. Materials and Methods. Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. Results. The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. Conclusion. The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

National Cancer Diagnosis Audit: initial results on avoidable delays

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696677
Author(s):  
Ruth Swann ◽  
ÒJana Witt ◽  
Brian Shand ◽  
Georgios Lyratzopoulos ◽  
Sara Hiom ◽  
...  
Keyword(s):  
Primary Care ◽  
Quality Improvement ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Tertiary Care ◽  
Patient Characteristics ◽  
Cancer Type ◽  
Breast Cancer Patients ◽  
Primary Care Data ◽  
Diagnosis Of Cancer

BackgroundAn earlier diagnosis of cancer can increase cancer survival and quality of life. Characterising avoidable delays to a patient’s diagnosis of cancer can help to direct quality improvement initiatives.AimTo evaluate avoidable delays to cancer diagnoses and the variation by cancer type and patient characteristics using primary care data collected as part of the National Cancer Diagnosis Audit (NCDA).MethodEnglish general practices participating in the NCDA (439) entered primary care data on patients (17,042) diagnosed with cancer in 2014. Using a taxonomy developed from the National Audit of Cancer Diagnosis in Primary Care (2011), GPs reported delays to the diagnosis that in their judgement were avoidable.ResultsIn 22% of NCDA patient records (n = 3380), the GP considered there to be an avoidable delay to the patient receiving their cancer diagnosis. There was variation by cancer type; 7% of breast cancer patients experienced delays compared to 34% of stomach cancer patients. 49% of avoidable delays occurred in primary care and 38% in secondary or tertiary care. Of all delays, 28% were attributed to clinician factors and 34% to health care system factors. Results will be presented by patient characteristics.ConclusionPrimary care data from the NCDA can be used to better understand potentially avoidable delays to diagnosis and identify possible solutions for improving the diagnostic pathway. Avoidable delays during cancer diagnosis occur for many reasons. These insights can inform quality improvement initiatives, which should be directed at both clinical and organisational factors in primary care and hospital settings.

scholarly journals Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

TEM Journal ◽  
2021 ◽  
pp. 1621-1629
Author(s):  
Aayat Aljarrah ◽  
Mustafa Ababneh ◽  
Damla Karagozlu ◽  
Fezile Ozdamli
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Distance Education ◽  
Big Data ◽  
Distance Learning ◽  
Affective Computing ◽  
Learning System ◽  
Educational Systems ◽  
Artificial Intelligence Techniques ◽  
Educational Processes

In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

scholarly journals Application of artificial intelligence to electronic health records for early detection and diagnosis of cancer in primary care: a Systematic Review (Preprint)

2020 ◽  
Author(s):  
Owain Tudor Jones ◽  
Natalia Calanzani ◽  
Smiji Saji ◽  
Stephen W Duffy ◽  
Jon Emery ◽  
...  
Keyword(s):  
Primary Care ◽  
Early Detection ◽  
Electronic Health Records ◽  
Inclusion Criteria ◽  
Implementation Barriers ◽  
Health Records ◽  
Primary Care Settings ◽  
Primary Care Data ◽  
Diagnosis Of Cancer ◽  
Electronic Health

BACKGROUND More than 17 million people worldwide, including 360,000 people in the UK, were diagnosed with cancer in 2018. Cancer prognosis and disease burden is highly dependent on disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection, and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of healthcare. OBJECTIVE We aimed to systematically review AI technologies based on electronic health record (EHR) data that may facilitate the earlier diagnosis of cancer in primary care settings. We evaluated the quality of the evidence, the phase of development the AI technologies have reached, the gaps that exist in the evidence, and the potential for use in primary care. METHODS We searched Medline, Embase, SCOPUS, and Web of Science databases from 1st January 2000 to 11th June 2019 (PROSPERO ID CRD42020176674), and included all studies providing evidence for accuracy or effectiveness of applying AI technologies to early detection of cancer using electronic health records. We included all study designs, in all settings and all languages. We extended these searches through a scoping review of commercial AI technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS We identified 10,456 studies: 16 met the inclusion criteria, representing the data of 3,862,910 patients. 13 studies described the initial development and testing of AI algorithms and three studies described the validation of an AI technology in independent datasets. One study was based on prospectively collected data; only three studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk-of-bias assessment highlighted a wide range in study quality. The additional scoping review of commercial AI tools identified 21 technologies, only one meeting our inclusion criteria. Meta-analysis was not undertaken due to heterogeneity of AI modalities, dataset characteristics and outcome measures. CONCLUSIONS Applying AI technologies to electronic health records for early detection of cancer in primary care is at an early stage of maturity. Further evidence is needed on performance using primary care data, implementation barriers and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended. This study was supported by funding from the NIHR Cancer Policy Research Programme and Cancer Research UK.

scholarly journals Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

2017 ◽  
Vol 67 (659) ◽  
pp. e396-e404 ◽  
Author(s):  
Mia Schmidt-Hansen ◽  
Sabine Berendse ◽  
Willie Hamilton ◽  
David R Baldwin
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Primary Care ◽  
Risk Prediction ◽  
External Validation ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Specialist Care ◽  
Prediction Tools ◽  
Primary Care Data

BackgroundLung cancer is the leading cause of cancer deaths. Around 70% of patients first presenting to specialist care have advanced disease, at which point current treatments have little effect on survival. The issue for primary care is how to recognise patients earlier and investigate appropriately. This requires an assessment of the risk of lung cancer.AimThe aim of this study was to systematically review the existing risk prediction tools for patients presenting in primary care with symptoms that may indicate lung cancerDesign and settingSystematic review of primary care data.MethodMedline, PreMedline, Embase, the Cochrane Library, Web of Science, and ISI Proceedings (1980 to March 2016) were searched. The final list of included studies was agreed between two of the authors, who also appraised and summarised them.ResultsSeven studies with between 1482 and 2 406 127 patients were included. The tools were all based on UK primary care data, but differed in complexity of development, number/type of variables examined/included, and outcome time frame. There were four multivariable tools with internal validation area under the curves between 0.88 and 0.92. The tools all had a number of limitations, and none have been externally validated, or had their clinical and cost impact examined.ConclusionThere is insufficient evidence for the recommendation of any one of the available risk prediction tools. However, some multivariable tools showed promising discrimination. What is needed to guide clinical practice is both external validation of the existing tools and a comparative study, so that the best tools can be incorporated into clinical decision tools used in primary care.

scholarly journals Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics

2016 ◽  
Vol 6 (4) ◽  
pp. 12 ◽  
Author(s):  
Marios Pappas ◽  
Athanasios Drigas
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Mathematics Education ◽  
Expert Systems ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Personalized Feedback ◽  
Tutoring Systems ◽  
Artificial Intelligence Techniques ◽  
Human Tutor

Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.

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