scholarly journals Physician Confidence in Artificial Intelligence: An Online Mobile Survey (Preprint)

2018 ◽  
Author(s):  
Songhee Oh ◽  
Jae Heon Kim ◽  
Sung-Woo Choi ◽  
Hee Jeong Lee ◽  
Jungrak Hong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Online Survey ◽  
Medical Application ◽  
Disease Diagnosis ◽  
Medical Field ◽  
High Quality ◽  
Inadequate Information ◽  
Development Direction ◽  
The Future ◽  
Mobile Survey

BACKGROUND It is expected that artificial intelligence (AI) will be used extensively in the medical field in the future. OBJECTIVE The purpose of this study is to investigate the awareness of AI among Korean doctors and to assess physicians’ attitudes toward the medical application of AI. METHODS We conducted an online survey composed of 11 closed-ended questions using Google Forms. The survey consisted of questions regarding the recognition of and attitudes toward AI, the development direction of AI in medicine, and the possible risks of using AI in the medical field. RESULTS A total of 669 participants completed the survey. Only 40 (5.9%) answered that they had good familiarity with AI. However, most participants considered AI useful in the medical field (558/669, 83.4% agreement). The advantage of using AI was seen as the ability to analyze vast amounts of high-quality, clinically relevant data in real time. Respondents agreed that the area of medicine in which AI would be most useful is disease diagnosis (558/669, 83.4% agreement). One possible problem cited by the participants was that AI would not be able to assist in unexpected situations owing to inadequate information (196/669, 29.3%). Less than half of the participants(294/669, 43.9%) agreed that AI is diagnostically superior to human doctors. Only 237 (35.4%) answered that they agreed that AI could replace them in their jobs. CONCLUSIONS This study suggests that Korean doctors and medical students have favorable attitudes toward AI in the medical field. The majority of physicians surveyed believed that AI will not replace their roles in the future.

scholarly journals A Survey on Drug Discovery in Medical Application using Artificial Intelligence

2020 ◽  
Vol 7 (4) ◽  
pp. 99-102
Author(s):  
Jayasheelan Palanisamy ◽  
S B Malarvizhi ◽  
D Shayamala
Keyword(s):  
Artificial Intelligence ◽  
Critical Thinking ◽  
Drug Discovery ◽  
Rare Diseases ◽  
Medical Application ◽  
Medical Field ◽  
Early Stages ◽  
The Future ◽  
Intelligent Behavior ◽  
The One

Artificial intelligence is the one that is developed and used in all developing sectors. It is almost used in every field. Artificial intelligence (AI) is the term used to describe the use of computers and technology to simulate intelligent behavior, and critical thinking comparable to a human being.AI is also used in the medical field for various purposes. It has been concentrating on rare diseases that are still in the process to cure. It is also used to discover the drug for medical purposes. The four major things that AI that concentrate is identifying, discovering, speeding up, and diagnosing the disease. Cancer, neurology, cardiology, radiology are the disease where AI is used. Cancer is one such disease where AI is mostly used. Experts say that there is no future medical without AI. From the early stages, until now, AI has been developing steadily its growth in the future will be unexplainable. Moreover, it can be a replacement for humans also.

Attitudes of Medical Workers Toward Artificial Intelligence in Ophthalmology: A Comparative Survey (Preprint)

2020 ◽  
Author(s):  
Weihua Yang ◽  
Bo Zheng ◽  
Maonian Wu ◽  
Shaojun Zhu ◽  
Hongxia Zhou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Ethics ◽  
Online Survey ◽  
Medical Professionals ◽  
Medical Field ◽  
Acceptance Level ◽  
Professional Workers ◽  
Basic Understanding ◽  
Comparative Survey ◽  
Definition Of

BACKGROUND Artificial intelligence (AI) is widely applied in the medical field, especially in ophthalmology. In the development of ophthalmic artificial intelligence, some problems worthy of attention have gradually emerged, among which the ophthalmic AI-related recognition issues are particularly prominent. That is to say, currently, there is a lack of research into people's familiarity with and their attitudes toward ophthalmic AI. OBJECTIVE This survey aims to assess medical workers’ and other professional technicians’ familiarity with AI, as well as their attitudes toward and concerns of ophthalmic AI. METHODS An electronic questionnaire was designed through the Questionnaire Star APP, an online survey software and questionnaire tool, and was sent to relevant professional workers through Wechat, China’s version of Facebook or WhatsApp. The participation was based on a voluntary and anonymous principle. The questionnaire mainly consisted of four parts, namely the participant’s background, the participant's basic understanding of AI, the participant's attitude toward AI, and the participant's concerns about AI. A total of 562 participants were counted, with 562 valid questionnaires returned. The results of the questionnaires are displayed in an Excel 2003 form. RESULTS A total of 562 professional workers completed the questionnaire, of whom 291 were medical workers and 271 were other professional technicians. About 37.9% of the participants understood AI, and 31.67% understood ophthalmic AI. The percentages of people who understood ophthalmic AI among medical workers and other professional technicians were about 42.61% and 15.6%, respectively. About 66.01% of the participants thought that ophthalmic AI would partly replace doctors, with about 59.07% still having a relatively high acceptance level of ophthalmic AI. Meanwhile, among those with ophthalmic AI application experiences (30.6%), respectively about 84.25% of medical professionals and 73.33% of other professional technicians held a full acceptance attitude toward ophthalmic AI. The participants expressed concerns that ophthalmic AI might bring about issues such as the unclear definition of medical responsibilities, the difficulty of ensuring service quality, and the medical ethics risks. And among the medical workers and other professional technicians who understood ophthalmic AI, 98.39%, and 95.24%, respectively, said that there was a need to increase the study of medical ethics issues in the ophthalmic AI field. CONCLUSIONS Analysis of the questionnaire results shows that the medical workers have a higher understanding level of ophthalmic AI than other professional technicians, making it necessary to popularize ophthalmic AI education among other professional technicians. Most of the participants did not have any experience in ophthalmic AI, but generally had a relatively high acceptance level of ophthalmic AI, believing that doctors would partly be replaced by it and that there was a need to strengthen research into medical ethics issues of the field.

ATTITUDES AND ACCEPTANCE TOWARD THE TECHNOLOGY OF CRYONICS IN GERMANY

2014 ◽  
Vol 30 (1) ◽  
pp. 98-104 ◽  
Author(s):  
Stephanie Kaiser ◽  
Dominik Gross ◽  
Jens Lohmeier ◽  
Michael Rosentreter ◽  
Jürgen Raschke
Keyword(s):  
Federal Republic ◽  
Medical Technology ◽  
Online Survey ◽  
Federal Republic Of Germany ◽  
Medical Field ◽  
The Future ◽  
Technological Developments ◽  
Degree Of Acceptance

Objectives: This study explores the awareness and the degree of acceptance of the idea of the medical technology cryonics—the freezing of a corpse to revive it in the future—among German citizens.Methods: Data were collected on the basis of a representatively weighted online survey of 1,000 people aged between 16 and 69 years and resident in the Federal Republic of Germany.Results: Forty-seven percent stated that they had already heard of cryonics; 22 percent could imagine having their bodies cryonized after their deaths. For 53 percent, participation in the latest technological developments which correlated with the approval of the conceivability of cryopreservation was important. The majority of the respondents were not skeptical or cautious about innovations in the medical field.Conclusions: The study shows that cryonics is known and accepted to a certain extent. However, a large proportion of respondents did not believe that it was desirable to use medical technology to overcome death, and fundamentally rejected a post-mortal continuation of life.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals Technology and artificial intelligence, the future of the medical field

Universitas ◽  
2014 ◽  
pp. 185-190
Author(s):  
Raúl Beltrán Ramírez ◽  
Rocío Maciel Arellano ◽  
José Jiménez Arévalo
Keyword(s):  
Artificial Intelligence ◽  
Medical Field ◽  
The Future

scholarly journals Design of the expert system to analyze disease in Plant Teak using Forward Chaining

2017 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Poningsih Poningsih
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Economic Value ◽  
Disease Diagnosis ◽  
High Quality ◽  
Web Based ◽  
Community Forests ◽  
Forward Chaining ◽  
Teak Wood

Teak is one kind of plant that is already widely known and developed by the wider community in the form of plantations and community forests. This is because until now Teak wood is a commodity of luxury, high quality, the price is expensive, and high economic value. Expert systems are a part of the method sciences artificial intelligence to make an application program disease diagnosis teak computerized seek to replace and mimic the reasoning process of an expert or experts in solving the problem specification that can be said to be a duplicate from an expert because science knowledge is stored inside a database  Expert System for the diagnosis of disease teak using forward chaining method aims to explore the characteristics shown in the form of questions in order to diagnose the disease teak with web-based software. Device keel expert system can recognize the disease after consulting identity by answering some of the questions presented by the application of expert systems and can infer some kind of disease in plants teak. Data disease known customize rules (rules) are made to match the characteristics of teak disease and provide treatment solutions.

Current Postvention Research and Priorities for the Future

Crisis ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 202-206 ◽  
Author(s):  
Karl Andriessen ◽  
Dolores Angela Castelli Dransart ◽  
Julie Cerel ◽  
Myfanwy Maple
Keyword(s):  
Social Life ◽  
Online Survey ◽  
Research Priorities ◽  
Future Research ◽  
Physical And Mental Health ◽  
Intercultural Collaboration ◽  
The Social ◽  
The Future ◽  
Suicide Bereavement ◽  
Ethical Approval

Abstract. Background: Suicide can have a lasting impact on the social life as well as the physical and mental health of the bereaved. Targeted research is needed to better understand the nature of suicide bereavement and the effectiveness of support. Aims: To take stock of ongoing studies, and to inquire about future research priorities regarding suicide bereavement and postvention. Method: In March 2015, an online survey was widely disseminated in the suicidology community. Results: The questionnaire was accessed 77 times, and 22 records were included in the analysis. The respondents provided valuable information regarding current research projects and recommendations for the future. Limitations: Bearing in mind the modest number of replies, all from respondents in Westernized countries, it is not known how representative the findings are. Conclusion: The survey generated three strategies for future postvention research: increase intercultural collaboration, increase theory-driven research, and build bonds between research and practice. Future surveys should include experiences with obtaining research grants and ethical approval for postvention studies.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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