scholarly journals Applications of Artificial Intelligence (AI) in healthcare: A review

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Target Identification ◽  
Medical Data ◽  
Discovery Process ◽  
Monitoring Methods ◽  
Pharmaceutical Firms ◽  
Genetic Codes ◽  
Applications Of Ai

Artificial intelligence is revolutionizing — and strengthening — modern healthcarethrough technologies that can predict, grasp, learn, and act, whether it's employed toidentify new relationships between genetic codes or to control surgery-assisting robots.It can detect minor patterns that humans would completely overlook. This studyexplores and discusses the various modern applications of AI in the health sector.Particularly, the study focuses on three most emerging areas of AI-poweredhealthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggestthat pharmaceutical firms have benefited from AI in healthcare by speeding up theirdrug discovery process and automating target identification. Artificial Intelligence (AI)can help also to eliminate time-consuming data monitoring methods. The findings alsoindicate that AI-assisted clinical trials are capable of handling massive volumes of dataand producing highly accurate results. Medical AI companies develop systems thatassist patients at every level. Patients' medical data is also analyzed by clinicalintelligence, which provides insights to assist them improve their quality of life.

scholarly journals Applications of Artificial Intelligence (AI) in healthcare: A review

2021 ◽  
Author(s):  
Mohammed Yousef Shaheen
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Drug Discovery ◽  
Target Identification ◽  
Health Sector ◽  
Monitoring Methods ◽  
Pharmaceutical Firms ◽  
Genetic Codes ◽  
Applications Of Ai

Artificial intelligence is revolutionizing — and strengthening — modern healthcare through technologies that can predict, grasp, learn, and act, whether it's employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients' medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ ◽  
2020 ◽  
pp. m3164 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Design ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

Abstract The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

2020 ◽  
pp. 247263032095693
Author(s):  
Masturah Bte Mohd Abdul Rashid
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Drug Development ◽  
Paradigm Shift ◽  
Drug Repositioning ◽  
Target Identification ◽  
Success Rates ◽  
Drug Target Identification ◽  
The Cost ◽  
Novel Drug

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

scholarly journals Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hussein Ibrahim ◽  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
An-Wen Chan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
International Standards ◽  
Health Interventions ◽  
Reporting Guidelines ◽  
Traditional Health ◽  
Trial Protocols ◽  
International Experts

Abstract Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

2020 ◽  
Vol 26 (9) ◽  
pp. 1364-1374 ◽  
Author(s):  
Xiaoxuan Liu ◽  
◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J. Calvert ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Design ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

AbstractThe CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

scholarly journals Applications of artificial intelligence and machine learning in orthodontics

2020 ◽  
Vol 10 ◽  
pp. 17-24
Author(s):  
Saeed N. Asiri ◽  
Larry P. Tadlock ◽  
Emet Schneiderman ◽  
Peter H. Buschang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quality Of Care ◽  
Treatment Outcomes ◽  
State Of The Art ◽  
Review Article ◽  
The Past ◽  
Clinical Problems ◽  
Applications Of Ai

Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

Prerequisites to support high-quality clinical trials in children and young people

2020 ◽  
pp. archdischild-2019-318677
Author(s):  
Steven Hirschfeld ◽  
Florian B Lagler ◽  
Jenny M Kindblom
Keyword(s):  
Clinical Trials ◽  
Healthcare Delivery ◽  
Paediatric Population ◽  
Ecosystem Functions ◽  
Healthcare Delivery System ◽  
High Quality ◽  
Children And Young People ◽  
The Right ◽  
Support Decision Making

Children have the right to treatment based on the same quality of information that guides treatment in adults. Without the proper evaluation of medicinal products and devices in paediatric clinical trials that are designed to meet the rigorous standards of the competent authorities, children are discriminated from advances in medicine. There are regulatory, scientific and ethical incentives to address the knowledge gap regarding efficacy and safety of medicines in the paediatric population. High-quality clinical trials involving children of all ages can generate data that will ultimately close the knowledge gaps and support decision making.For clinical trials that enrol children, the needs are specialised and often resource intensive. Prerequisites for successful paediatric clinical trials are personnel with training in both paediatrics and neonatology and expertise in clinical trials in these populations. Moreover, national and international networks for efficient collaboration, dissemination of information, and sharing of resources and expertise are also needed, together with competent, efficient and high-quality local infrastructure with effective processes. Monitoring and oversight bodies with the relevant competence, including expertise in paediatrics, is also an important prerequisite for paediatric clinical trials. Compromise in any of these components will compromise the downstream results.This paper discusses the structures and competences needed in order to perform effective, high-quality paediatric clinical trials with the ultimate goal of better medicines and treatments for children. We propose a model of examining the process as a series of components that each has to be optimised, then all the components are actively optimised to function together as an ecosystem, and the resulting ecosystem functions well with the general research system and the healthcare delivery system.

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