Research article
A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over

https://doi.org/10.1016/j.ejrad.2019.108742Get rights and content

Highlights

  • AI as a support system for radiology is welcomed by students and radiologists.

  • Medical students see AI as a potential threat to diagnostic radiology.

  • Better education about this supposed fear of AI seems to be necessary.

  • Radiologists fear other disciplines in terms of “turf losses”.

Abstract

Purpose

To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing.

Method

An online questionnaire was created using the SurveyMonkey platform targeting radiologists, students and surgeons throughout the German speaking part of Switzerland. A total of 170 people participated in the survey (59 radiologists, 56 surgeons and 55 students). Statistical analysis was carried out using the Kruskal-Wallis test with Dunn’s multiple comparison post-hoc tests.

Results

While the majority of participants agreed that AI should be included as a support system in radiology (Likert scale 0-10: Median value 8), surgeons were less supportive than radiologists (p = 0.001). Students saw a potential threat of AI as more likely than radiologists did (p = 0.041). When asked whether they were concerned about “turf losses” from radiology to other disciplines, radiologists were much more likely to agree than students (p < 0.001). Of the students that do not intend to specialize in radiology, 26 % stated that AI was one of the reasons. Surgeons advocate the use of teleradiology.

Conclusions

With regard to AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, whereas medical students and surgeons tend to be more skeptical towards this technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are rather afraid of turf losses.

Introduction

Harvard Professor Clayton Christensen first introduced the theory of “disruptive innovation” in his book “The Innovator’s dilemma” [1]. A disruptive innovation is one that significantly alters the way businesses or entire industries operate and leads to the displacement of established technologies. In radiology, AI (artificial intelligence) could be such a disruptive technology that might tremendously change the way diagnostic imaging is performed. Further challenges towards the current way radiology operates could be turf losses (takeovers of radiological examinations by other disciplines), growing competitiveness of the job market and teleradiology [[2], [3], [4], [5], [6]]. An opportunity could arise for radiology by establishing itself as a specialist in 3D printing. Seeing a great future in the use of AI in radiology, a vast amount of research is being performed in this field, by academic centers but also by the industry, e.g. by digital giants like Google and Apple [[7], [8], [9], [10]]. With optimal use, AI could help radiologists and ultimately hospitals work more efficiently and thus save money by implementing AI based software to perform easy and repetitive but time-consuming tasks such as nodule detection [11]. With radiologists and AI currently being on the same level of accuracy in detecting nodules [12,13], they could improve their diagnostic accuracy by using artificial intelligence [14]. Not only could radiologists be supported in detecting abnormalities, they could also take advantage of AI-assisted interpretation and potentially AI-based integration of other clinical information [[15], [16], [17]]. Further application of these developments includes the use in an emergency setting, where cases of pneumothorax, bleedings, renal stones and foreign bodies could be automatically identified, assisting radiologists in the diagnostic process, optimizing it to be faster and more accurate [18]. The use of AI is a growing field in radiology, which can be a concern or even a threat to practicing diagnostic radiologists, as the renowned Geoffrey Hinton, an expert on artificial neural networks, believes [11].

Since the topic of AI implementation into radiology, the takeover of radiological examinations by other disciplines, teleradiology, and 3D printing are subjects of heated discussions, we wanted to know how radiologists, medical students and surgeons perceive these developments. We were interested in the opinions of medical students, as they are the next generation of doctors and possible radiologists, and the opinions of surgeons, as a group of clinicians whose work is strongly influenced by radiology. Thereby we conducted a survey to evaluate the opinions and assessments of radiologists, surgeons and medical students on a variety of potential chances and threats to radiology.

Section snippets

Materials and methods

The SurveyMonkey platform was used to create an electronic survey with implemented logic that allowed us to ask participants profession-adapted questions following a general questionnaire. The survey was divided into different sections. The first section consisted of a group of demographic questions, followed by questions on the future of radiology such as questions on AI, teleradiology, turf losses, 3D printing and the future of the profession of radiologists. Subsequent sections were

Results

Over a four-week period, a total of 203 individuals filled out the questionnaire, of which 170 completed it. Of these 170 participants, 59 were radiologists (40 diagnostic; 2 interventional; 17 both), 56 were surgeons and 55 were students. Of the participants who completed the questionnaire, 40 % were female, 59 % were male, and 1 % answered other.

Discussion

The aim of this study was to investigate the perceptions and attitudes of radiologists, surgeons and medical students towards AI and other developments that might represent opportunities or threats to radiology as a field of work.

Our results show in part substantial differences in the perception of AI among participants. While all three groups agree that AI should be integrated into the diagnostic process of radiological imaging, radiologists tend to support a future use of AI more than

Declaration of Competing Interest

None.

Acknowledgements

None.

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