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 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.

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

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.

scholarly journals Influential Usage of Big Data and Artificial Intelligence in Healthcare

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Cheng Yang ◽  
Saad Ul Islam ◽  
Asra Noor ◽  
Sadia Khan ◽  
Waseem Afsar ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Tumor Biology ◽  
Health Sector ◽  
Clinical Work ◽  
Information And Communications Technology ◽  
Added Value ◽  
Medical Health ◽  
Electronic Health

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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 The Beginning of a New Era: Artificial Intelligence in Healthcare

2020 ◽  
Vol 11 (3) ◽  
pp. 414-425
Author(s):  
Akshara Kumar ◽  
Shivaprasad Gadag ◽  
Usha Yogendra Nayak
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Drug Discovery ◽  
Science Fiction ◽  
Patient Monitoring ◽  
Basic Knowledge ◽  
Healthcare Sector ◽  
Human Beings ◽  
External Data ◽  
Applications Of Ai

The healthcare sector is considered to be one of the largest and fast-growing industries in the world. Innovations and novel approaches have always remained the prime aims in order to bring massive development. Before the emergence of technology, all the sectors, including the healthcare sector was dependant dependent on man power, which was time-consuming, and less accurate with lack of efficiency. With the recent advancements in machine learning, the condition is has been steadily revolutionizing. in the practice of the health care industry. Artificial Intelligence intelligence (AI) lies in the computer science department, which stresses on the intelligent machines’ creation, that work and react just like human beings. In simple words, AI is the capability of a computer program to think and learn, almost satisfying natural intelligence. It is the ability of a system to interpret the external data correctly, learn from it and finally use those learnings to execute some particular goals and tasks through adaptation. It utilizes multiple technologies to comprehend, act and understand from past experiences. Involving AI is not a science fiction that was once a very long time ago. It AI being an emerging technology has been adopted in various facets of healthcare ranging from drug discovery to patient monitoring. rapidly penetrated its wings developed itself into almost all the industries. Irrespective of the person’s background, whether he/she is a student, industry worker, an entrepreneur, or a scientist, having basic knowledge about the importance and applications of AI would be impactful. Currently, the applications of AI has have been expanding into those fields, which was once thought to be the only domain of human expertise such as health care sector. In this review article, we have shedthrown light on the present usage of AI in the healthcare sector, such as its working, and the way this system is being implemented in different domains, such as drug discovery, diagnosis of diseases, clinical trials, remote patient monitoring, and nanotechnology. We have also slightlybriefly touched upon its applications in touching other sectors as well. The public opinions have also been analyszed and discussed along with the future prospects.The main goals have been briefed. prospects. We have discussed the Along with the merits, we have also discussed about and the other side of AI, i.e. the disadvantages of this as wellin the last part of the manuscript.

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.

scholarly journals Clinical trial stratagems in the delivery of pharmaceutical products during epidemics

2020 ◽  
Author(s):  
Pardis Tabaee Damavandi
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Drug Discovery ◽  
Phase I ◽  
Medicinal Product ◽  
Pharmaceutical Products ◽  
Effective Drug ◽  
Phase I Clinical Trials ◽  
Efficacious Vaccine

Pandemics require facilitated, quick and effective drug discovery strategies. These can be achieved by marking Phase I clinical trials and by utilising design outlines that also support the safety of volunteers and guarantee a good quality of the medicinal product at an early juncture. More specifically, an approach to the design of vaccines for the current epidemics is presented that does not preclude a live attenuated formulation, but proposes a potentially equally efficacious vaccine necessitating shorter timescales.

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