scholarly journals Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists (Preprint)

2019 ◽  
Author(s):  
Nicoletta Musacchio ◽  
Annalisa Giancaterini ◽  
Giacomo Guaita ◽  
Alessandro Ozzello ◽  
Maria A Pellegrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Diabetes Care ◽  
Digital Health ◽  
Descriptive Analysis ◽  
Careful Analysis ◽  
Great Opportunity ◽  
Italian Association ◽  
Number Of Patients

UNSTRUCTURED Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

scholarly journals Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

10.2196/16922 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e16922
Author(s):  
Nicoletta Musacchio ◽  
Annalisa Giancaterini ◽  
Giacomo Guaita ◽  
Alessandro Ozzello ◽  
Maria A Pellegrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Diabetes Care ◽  
Digital Health ◽  
Descriptive Analysis ◽  
Careful Analysis ◽  
Great Opportunity ◽  
Italian Association ◽  
Number Of Patients

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

scholarly journals Artificial Intelligence in Health Care

2021 ◽  
pp. 207-221
Author(s):  
Eric D. Perakslis ◽  
Martin Stanley
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Racial Bias ◽  
Digital Health ◽  
Digital Devices ◽  
Black Boxes ◽  
Electronic Medical Record Systems ◽  
The Right ◽  
Electrical Impulses

Although not always considered a domain of digital health, artificial intelligence (AI) and machine learning (ML) are inseparable, given that virtually all digital health tools utilize advance algorithm technologies. For example, a wearable device is not directly measuring sleep. It is measuring movement, electrical impulses, and other variables that are processed via algorithm and presented as a representation of sleep. Beyond digital devices, AI is being seen as a revolution in health care driven by the hopes that algorithms running on electronic medical record systems will predict when a patient is turning septic or that AI will free clinicians from tedious tasks. Much of this is coming true, but there is also a growing list of potential hazards such as trusting “black boxes,” racial bias, and poor technical execution. This chapter discusses ways to find the right balance between the progress and problems of AI in health care.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey

2020 ◽  
Vol 6 ◽  
pp. 205520762096835
Author(s):  
C Blease ◽  
C Locher ◽  
M Leon-Carlyle ◽  
M Doraiswamy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Medical Profession ◽  
Descriptive Analysis ◽  
Psychiatric Practice ◽  
Limited Attention ◽  
Written Responses ◽  
Future Technology ◽  
Qualitative Descriptive ◽  
The Impact

Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. Objective This study aimed to explore psychiatrists’ opinions about the potential impact innovations in artificial intelligence and machine learning on psychiatric practice Methods In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written responses (“comments”) to three open-ended questions in the survey. Results Comments were classified into four major categories in relation to the impact of future technology on: (1) patient-psychiatrist interactions; (2) the quality of patient medical care; (3) the profession of psychiatry; and (4) health systems. Overwhelmingly, psychiatrists were skeptical that technology could replace human empathy. Many predicted that ‘man and machine’ would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. Conclusions This study presents timely information on psychiatrists’ views about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

Clinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Shivank Garg ◽  
Noelle L. Williams ◽  
Andrew Ip ◽  
Adam P. Dicker
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Health Care ◽  
Data Collection ◽  
Patient Outcomes ◽  
Cancer Care ◽  
Digital Health ◽  
Health Technologies ◽  
Health Care Value

Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants. In these sections, we give an overview of the potential clinical impact of such technologies as they pertain to key domains, including patient education, patient outcomes, quality of life, and health care value. We performed a search of PubMed ( www.ncbi.nlm.nih.gov/pubmed ) and www.ClinicalTrials.gov for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. From these results, studies were included that exemplified use of the various domains of digital health technologies in oncologic care. Digital health encompasses the integration of a vast array of technologies with health care, each associated with varied methods of data collection and information flow. Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, on-treatment patient management, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and quality of life.

scholarly journals What is happening to laboratory medicine in Denmark?

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Per E. Jørgensen
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Digital Health ◽  
Laboratory Medicine ◽  
Positive Development ◽  
Scandinavian Country ◽  
Health Records ◽  
Current Trends ◽  
Technological Developments ◽  
Almost All

Abstract A number of current trends will affect and probably change laboratory medicine, as we know it. Scientific and technological developments, digital health with big data and artificial intelligence, and centralization will change the interfaces among the specialties of laboratory medicine. They might even challenge the identity of some specialties. Other trends such as demographic changes, increased complexity of health care, digital health with electronic health records, and more demanding and well-informed patients will change the way laboratory medicine specialties deliver their services. This paper discusses the possible changes of laboratory medicine in Denmark – a Scandinavian country where almost all hospitals are public. If Danish laboratories grasp the new possibilities instead of trying to avoid them, laboratory medicine is likely to prosper. Such a positive development will call upon good leadership and a genuine willingness among laboratory specialist to adapt to a future where their own specialty might be very different from today.

scholarly journals Artificial intelligence/machine learning in diabetes care

2019 ◽  
Vol 23 (4) ◽  
pp. 495 ◽  
Author(s):  
Rajiv Singla ◽  
Ankush Singla ◽  
Yashdeep Gupta ◽  
Sanjay Kalra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Diabetes Care
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