An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude

Abstract Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption.

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Physicians’ requirements and expectations of future Artificial Intelligence applications for healthcare – a web-based survey in German university hospitals (Preprint)

10.2196/preprints.26646 ◽

2020 ◽

Author(s):

Oliver Maassen ◽

Sebastian Fritsch ◽

Julia Gantner ◽

Saskia Deffge ◽

Julian Kunze ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Practice ◽

Patient Care ◽

Biomedical Research ◽

Positive Attitude ◽

Patient Data ◽

University Hospitals ◽

Web Based ◽

Wide Range ◽

Medical Disciplines

BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of healthcare. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research has not been investigated widely in German university hospitals. OBJECTIVE Evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research e.g. for the development of machine learning (ML) algorithms in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given on Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS 121 (39.9%) female and 173 (57.1%) male physicians (N=303) from a wide range of medical disciplines and work experience levels completed the online survey. The majority of respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). 82.5% of respondents (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism, there come several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (e.g., imaging procedures in radiology and pathology) or data is collected continuously (e.g. cardiology and intensive care medicine), physicians’ expectations to substantially improve future patient care are high. However, for the practical usage of AI in healthcare regulatory and organizational challenges still have to be mastered.

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Effect of Health Education on Knowledge Levels and Attitudes of Pregnant Women About Early Breastfeeding Initiation

Kesmas Indonesia ◽

10.20884/1.ki.2020.12.2.2031 ◽

2020 ◽

Vol 12 (2) ◽

pp. 159

Author(s):

Grace Tedy Tulak ◽

Muhdar Muhdar ◽

Anik Winarni

Keyword(s):

Health Education ◽

Positive Attitude ◽

Test Design ◽

Research Use ◽

Breastfeeding Initiation ◽

Average Value ◽

Post Test ◽

Knowledge Levels ◽

Knowledge And Attitude ◽

Is Design

Early breastfeeding initiation is the process of self breastfeeding, at least for one hour when the baby has just born. The mothers can do early breastfeeding initiation properly if they have good knowledge and positive attitude. Target of this research that is analysing to determine the effect of health education on pregnant mother’s knowledge and attitude on early breastfeeding initiation in Region Work Puskesmas Wara Utara Kota, Palopo City Year 2018. This research use method of eksperimental, research desain the used is design eksperimental quasi: pre group one test post and test design. Sampel at this research amount to 34 people technicsly intake of accidental sampel of sampling. Result of research got that average value at pre test knowledge of pregnant mother’s about early breastfeeding initiation that is 1,41 while average value of post test knowledge of pregnant mother’s about early breastfeeding initiation that is 1,74 and got by probability equal to 0,001, showing 0,001 < 0,05. While average value at pre test attitude of pregnant mother’s about early breastfeeding initiation that is 1,41 while average value of post test attitude of pregnant mother’s about early breastfeeding initiation that is 1,74 and got by probability equal to 0,001, showing 0,001 < 0,05. Pursuant to difference value and analysis result, this matter can be concluded that there are to determine the effect of health education on pregnant mother’s knowledge and attitude on early breastfeeding initiation in Region Work Puskesmas Wara Utara Kota, Palopo City Year 2018.

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Artificial Intelligence in Radiology Residency Training

Seminars in Musculoskeletal Radiology ◽

10.1055/s-0039-3400270 ◽

2020 ◽

Vol 24 (01) ◽

pp. 74-80 ◽

Cited By ~ 1

Author(s):

Michael C. Forney ◽

Aaron F. McBride

Keyword(s):

Artificial Intelligence ◽

Residency Training ◽

Training Programs ◽

Emerging Technology ◽

Radiology Training ◽

Radiology Residency ◽

Radiology Residents

AbstractArtificial intelligence (AI) is an emerging technology that brings a wide array of new tools to the field of radiology. AI will certainly have an impact on the day-to-day work of radiologists in the coming decades, thus training programs must prepare radiology residents adequately for their future careers. Radiology training programs should aim to give residents an understanding of the fundamentals and types of AI in radiology, the broad areas AI can be applied in radiology, how to assess AI applications in radiology, and resources available to build their knowledge in IA applications in radiology.

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Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

Visceral Medicine ◽

10.1159/000511930 ◽

2020 ◽

Vol 36 (6) ◽

pp. 443-449

Author(s):

Julian Varghese

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Clinical Practice ◽

Clinical Research ◽

Structured Data ◽

Regulatory Aspects ◽

Clinical Benefits ◽

The Subject ◽

Common Barriers ◽

Clinical Adoption

Background: Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. Summary: Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. Key Messages: There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

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Mobile Artificial Intelligence Applications for Skin Cancer Diagnostics: Preferences and Concerns of Digital Natives (Preprint)

10.2196/preprints.22909 ◽

2020 ◽

Author(s):

Sarah Haggenmüller ◽

Eva Krieghoff-Henning ◽

Tanja Jutzi ◽

Nicole Trapp ◽

Lennard Kiehl ◽

...

Keyword(s):

Artificial Intelligence ◽

Clinical Practice ◽

Skin Cancer ◽

Cancer Detection ◽

Mobile Applications ◽

Positive Attitude ◽

Age Groups ◽

Digital Natives ◽

Cancer Diagnostics ◽

Skin Cancer Detection

BACKGROUND Artificial Intelligence (AI) has shown potential to improve diagnostics of various diseases and especially early skin cancer detection. What is missing is the bridge from AI technology to clear application scenarios in clinical practice as well as added value for patients. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation into clinical practice, while at the same time representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile applications for skin cancer diagnostics. In this way, we evaluated the preferences as well as the relative influence of concerns with a special focus on younger age groups. METHODS We recruited respondents below 35 years of age through the social media channels Facebook, LinkedIn and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes towards mobile applications for skin examination. An adaptive choice-based conjoint (ACBC) was integrated to assess respondents’ preferences. Potential concerns were evaluated using maximum difference scaling (MaxDiff). RESULTS 728 respondents were included in the analysis. About 66.5% expressed a positive attitude towards the use of AI-based applications. In particular participants residing in big cities or small towns and individuals that were familiar with the use of health or tracking apps were significantly more open towards mobile diagnostic systems. Hierarchical Bayes estimation (HB) of the preferences of participants with positive attitude (n=484) revealed that the use of mobile applications as an assistance system was preferred. Respondents ruled out app versions with an accuracy of 65 percent or less, applications using data storage without encryption as well as systems that did not deliver background information about the decision-making. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information into the decision-making process. MaxDiff analysis for the negative-minded participant group (n=244) outlined that data security, insufficient trust in the app, as well as the lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age was ready to accept AI-based diagnostic solutions for early skin cancer detection. However, for translation into clinical practice, participants’ demand for increased transparency and explainability of AI-based tools seems to be critical. Altogether, digital natives expressed similar preferences and concerns when compared to results obtained by previous studies that included other age groups.

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Artificial Intelligence for a Better Employee Engagement

International Research Journal of Business Studies ◽

10.21632/irjbs.13.2.173-188 ◽

2020 ◽

Vol 13 (2) ◽

pp. 173-188

Author(s):

Ria Emilia Sari ◽

◽

Siu Min ◽

Hiskia Purwoko ◽

Asnan Furinto ◽

...

Keyword(s):

Artificial Intelligence ◽

Employee Engagement ◽

Positive Attitude ◽

Qualitative Approach ◽

Specific Knowledge ◽

Level Of Involvement ◽

Attitudes And Behaviors ◽

Before And After ◽

The Status ◽

And Behaviors

Employee engagement is the positive attitude of each employee towards the business and the value of the organization. This research aims to see whether the use of AI-based technology, tools, and software can help management detect intangible things such as employee engagement level and provide clues as to what factors influence it and how management can improve it. This research is a qualitative approach. We interviewed the management and selected employees to determine employee engagement at SML before and after implementing the AI-based application. The interview results compared with the results obtained from the application for six months (Feb - July 2020). The study was conducted on all SML employees, amounting to 39 people. This research has shown that the use of AI based software can significantly help management, not only to find out the status of each employee’s level of involvement but also to anticipate their attitudes and behaviors through predictive indicators. Thus, the company can proactively retain key employees. This research provides new and practical insights and opportunities for company owners and leaders to utilize technology to detect something that is naturally quite difficult because it requires specific knowledge and experience.

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Interventional radiology and artificial intelligence in radiology: Is it time to enhance the vision of our medical students?

Insights into Imaging ◽

10.1186/s13244-020-00942-y ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Pierre Auloge ◽

Julien Garnon ◽

Joey Marie Robinson ◽

Sarah Dbouk ◽

Jean Sibilia ◽

...

Keyword(s):

Artificial Intelligence ◽

Interventional Radiology ◽

Clinical Practice ◽

Medical Students ◽

Medical Curriculum ◽

New Technology ◽

Large Population ◽

The Other ◽

Clinical Phase ◽

Preclinical Phase

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

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How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

npj Breast Cancer ◽

10.1038/s41523-021-00268-y ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Xinran Wang ◽

Liang Wang ◽

Hong Bu ◽

Ningning Zhang ◽

Meng Yue ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Clinical Practice ◽

Subjective Assessment ◽

Current Guideline ◽

Scoring Method ◽

Programmed Death ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

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Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)

Sustainability ◽

10.3390/su10093066 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3066 ◽

Cited By ~ 4

Author(s):

Vasile Gherheș ◽

Ciprian Obrad

Keyword(s):

Artificial Intelligence ◽

Social Life ◽

Research Method ◽

Quantitative Research ◽

Positive Attitude ◽

Negative Impact ◽

Representation Error ◽

The Social ◽

The Future ◽

Humanities Students

This study investigates how the development of artificial intelligence (AI) is perceived by the students enrolled in technical and humanistic specializations at two universities in Timisoara. It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations associated with it, and on the possible impact of artificial intelligence on certain areas of the social life. Moreover, the present study reveals the students’ perceptions on the sustainability of these changes and developments, and therefore aims to reduce the possible negative impact on consumers, and at anticipate the changes that AI will produce in the future. In order to collect the data, the authors have used a quantitative research method. A questionnaire-based sociological survey was completed by 928 students, with a representation error of only ±3%. The analysis has shown that a great number of respondents have a positive attitude towards the emergence of AI, who believe it will influence society for the better. The results have also underscored underlying differences based on the respondents’ type of specialization (humanistic or technical), and their gender.

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