scholarly journals Artificial Intelligence in Translational Medicine

2021 ◽  
Author(s):  
Simone Brogi ◽  
Vincenzo Calderone
Keyword(s):  
Artificial Intelligence ◽  
Translational Research ◽  
Translational Medicine ◽  
Clinical Investigation ◽  
Scientific Data ◽  
Review Article ◽  
Computational Techniques ◽  
Computer Based ◽  
Gain Access ◽  
Applications Of Ai

The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

scholarly journals Artificial Intelligence in Translational Medicine

2021 ◽  
Vol 1 (3) ◽  
pp. 223-285
Author(s):  
Simone Brogi ◽  
Vincenzo Calderone
Keyword(s):  
Artificial Intelligence ◽  
Translational Research ◽  
Translational Medicine ◽  
Clinical Investigation ◽  
Scientific Data ◽  
Review Article ◽  
Computational Techniques ◽  
Computer Based ◽  
Gain Access ◽  
Applications Of Ai

The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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 Artificial Intelligence (AI)-aided Disease Prediction

2020 ◽  
Vol 1 (3) ◽  
pp. 130-136
Author(s):  
Chenxi Liu ◽  
Dian Jiao ◽  
Zhe Liu
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Analytical Methods ◽  
Clinical Medicine ◽  
Healthcare Management ◽  
Review Article ◽  
Disease Prediction ◽  
Future Perspectives ◽  
Diagnostic Approaches ◽  
Applications Of Ai

Abstract Artificial intelligence (AI) has been widely used in clinical medicine, and it is witnessing increasing innovations in the fields of AI-aided image analysis, AI-aided lesion determination, AI-assisted healthcare management, and so on. This review article focuses on the emerging applications of AI-related medicine and AI-assisted visualized medicine, including novel diagnostic approaches, metadata analytical methods, and versatile AI-aided treatment applications in preclinical and clinical uses, and also looks at future perspectives of AI-aided disease prediction.

Sports Medicine and Artificial Intelligence: A Primer

2021 ◽  
pp. 036354652110086
Author(s):  
Prem N. Ramkumar ◽  
Bryan C. Luu ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Benedict U. Nwachukwu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Sports Medicine ◽  
Injury Risk ◽  
Industrial Revolution ◽  
Improve Patient Care ◽  
Great Promise ◽  
Patient Reported ◽  
Athlete Injury ◽  
Improve Patient ◽  
Applications Of Ai

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

The functional vascular anatomy of the swine for research

Vascular ◽  
2021 ◽  
pp. 170853812199650
Author(s):  
Joseph Edwards ◽  
Hossam Abdou ◽  
Neerav Patel ◽  
Marta J Madurska ◽  
Kelly Poe ◽  
...  
Keyword(s):  
Translational Research ◽  
Functional Anatomy ◽  
Three Dimensional ◽  
Vascular Anatomy ◽  
Review Article ◽  
Imaging Data ◽  
Preclinical Research ◽  
Iodinated Contrast ◽  
Translational Research Program ◽  
And Function

Objectives Swine ( Sus Scrofa) are utilized broadly in research settings, given similarities to human vessel size and function; however, there are some important differences for clinicians to understand in order to interpret and perform translational research. This review article uses angiograms acquired in the course of a translational research program to present a description of the functional anatomy of the swine. Methods Digital subtraction angiography and computed tomography angiography were obtained throughout the course of multiple studies utilizing power injection with iodinated contrast. Subtracted two-dimensional images and three-dimensional multiplanar reformations were utilized post image acquisition to create maximal intensity projections and three-dimensional renderings of using open-source software (OsiriX). These imaging data are presented along with vessel measurements for reference. Results An atlas highlighting swine vascular anatomy, with an emphasis on inter-species differences that may influence how studies are conducted and interpreted, was compiled. Conclusions Swine are utilized in broad-reaching fields for preclinical research. While many similarities between human and swine vasculature exist, there are important differences to consider when conducting and interpreting research. This review article highlights these differences and presents accompanying images to inform clinicians gaining experience in swine research.

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 38-49 ◽  
Author(s):  
Natalia Gorelik ◽  
Jaron Chong ◽  
Dana J. Lin
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Bone Tumors ◽  
Image Interpretation ◽  
Bone Age ◽  
Musculoskeletal Imaging ◽  
Radiology Workflow ◽  
Disease Response ◽  
Osseous Metastases ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

scholarly journals Peacekeeping Conditions for an Artificial Intelligence Society

2019 ◽  
Vol 3 (2) ◽  
pp. 34
Author(s):  
Hiroshi Yamakawa
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Human Society ◽  
Common Value ◽  
Goal Management ◽  
Computer Based ◽  
Common Goals ◽  
Near Future ◽  
Advanced System ◽  
Local Value

In a human society with emergent technology, the destructive actions of some pose a danger to the survival of all of humankind, increasing the need to maintain peace by overcoming universal conflicts. However, human society has not yet achieved complete global peacekeeping. Fortunately, a new possibility for peacekeeping among human societies using the appropriate interventions of an advanced system will be available in the near future. To achieve this goal, an artificial intelligence (AI) system must operate continuously and stably (condition 1) and have an intervention method for maintaining peace among human societies based on a common value (condition 2). However, as a premise, it is necessary to have a minimum common value upon which all of human society can agree (condition 3). In this study, an AI system to achieve condition 1 was investigated. This system was designed as a group of distributed intelligent agents (IAs) to ensure robust and rapid operation. Even if common goals are shared among all IAs, each autonomous IA acts on each local value to adapt quickly to each environment that it faces. Thus, conflicts between IAs are inevitable, and this situation sometimes interferes with the achievement of commonly shared goals. Even so, they can maintain peace within their own societies if all the dispersed IAs think that all other IAs aim for socially acceptable goals. However, communication channel problems, comprehension problems, and computational complexity problems are barriers to realization. This problem can be overcome by introducing an appropriate goal-management system in the case of computer-based IAs. Then, an IA society could achieve its goals peacefully, efficiently, and consistently. Therefore, condition 1 will be achievable. In contrast, humans are restricted by their biological nature and tend to interact with others similar to themselves, so the eradication of conflicts is more difficult.

scholarly journals Psychometrics of the Computer-Based Relationships With Health Care Provider Scale in Older Adults

2011 ◽  
Vol 19 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Elizabeth H. Anderson ◽  
Patricia J. Neafsey ◽  
Sheri Peabody
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Health Care Provider ◽  
Care Provider ◽  
Patient Adherence ◽  
Internal Consistency Reliability ◽  
Scientific Data ◽  
Patient Health ◽  
Computer Based

The type and quality of the provider–patient health care relationship impacts patient adherence. The study purpose was to convert the 5-item paper and pencil Relationships With Health Care Provider Scale (RHCPS) to a reliable and valid computer-based scale for use with older adults. Outpatient adults (N = 121) older than 59 years were recruited. The RHCPS underwent several iterations documenting internal consistency reliability, content and factorial validity, and scale usability in a computer tablet format. A total of 5 expert judges rated all 5 items as valid, which resulted in a scale content validity index of 1. Cronbach’s standardized alpha was .81. Principal components analysis extracted 1 factor (eigenvalue > 1; confirmed by scree plot) as anticipated. Computer-based RHCPS has the potential to reveal valuable clinical and scientific data on patient–provider relationships among older adults.

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