Artificial Intelligence and Teleradiology in Pediatric Radiology: A Survey by the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR)

Abstract

Purpose

The aim of our study was to assess the attitudes towards AI and teleradiology and their current usage in pediatric radiology within German-speaking countries.

Materials and Methods

From March to May 2023, we conducted an anonymous online survey among members of the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR) via the SurveyMonkey platform. The survey consisted of 25 items with rating scales and open-ended responses.

Results

Out of 418 society members, 36 completed the questionnaire (8.6%). Teleradiology (50% fully agree, 27.8% partly agree) and AI (38.9% fully agree, 22.2% partly agree) were considered relevant for pediatric radiology by the majority of respondents. Teleconsultation for second opinions is regularly used in 58% of the departments. Currently, AI does not play a significant role in the daily work of 52.8% of respondents. Beyond segmentation, AI is used primarily for image acquisition and dose reduction. Over 80% of respondents indicated that bone age determination is well-suited for an AI solution, yet only 31% routinely use such a solution.

Conclusion

AI and teleradiology have a high level of acceptance in German-speaking pediatric radiology (Germany, Austria, Switzerland, i.e. the DACH region) and are seen as a possible strategy for improving pediatric radiology care. This contrasts with the current low level of use in clinical routine.

Key Points

• Pediatric radiologists in the DACH region consider AI and teleradiology to be important for pediatric radiology care.

• AI/teleradiology are seen as viable options to enhance pediatric radiology care.

• However, the actual use of AI/teleradiology in everyday routine is low.

Citation Format

• Sturm M, von Kalle T, Renz DM et al. Artificial Intelligence and Teleradiology in Pediatric Radiology: A Survey by the Society for German-speaking Pediatric Radiologists (GPR) and the Swiss Society for Pediatric Radiology (SGPR). Rofo 2025; DOI 10.1055/a-2516-3057

Keywords

economics - teleradiology - cost-effectiveness - acceptance testing - diagnostic radiology - pediatric radiology

Introduction

Pediatric and adolescent radiology has made a major contribution to medical successes in the fight against diseases of childhood and adolescence. Its continued development depends largely on insights from clinical specialties and on technical innovations from other imaging fields [1] [2]. There is no broad pool of doctors specializing in pediatric and adolescent radiology in German-speaking countries. The strategy paper of the Society for German-speaking Pediatric Radiologists (GPR) calls for providing adequately staffed regional referral centers (with both medical and non-medical personnel) to focus on pediatric and adolescent radiology; these centers will cooperate closely with the referring physicians from conservative and surgical pediatric and adolescent medicine, as well as outpatient or inpatient radiology [1]. According to the GPR strategy paper, technologies such as artificial intelligence (AI) and teleradiology could open new opportunities for more efficient and improved radiology care for children in Germany, Austria, and Switzerland (i.e. the DACH region) [1].

The role of AI in medical imaging is growing steadily [3]. The pediatric radiology workflow holds many points of reference at which AI applications could potentially be implemented. These can range from patient allocation and appointment scheduling to dose and artifact reduction, as well as image interpretation, e.g. using computer-assisted diagnosis (CAD) systems [4] [5] [6]. Virtual grids can be used for X-rays in intensive care units to reduce radiation exposure and improve image quality [7]. With AI, image acquisition in MRI or CT examinations can be improved through faster MRI sequences [8] or dose reduction can be optimized in CT examinations [9] [10]. In post-processing, AI can handle, in addition to image optimization, more time-consuming activities such as segmentation [6]. Of the more than 200 approved AI products for radiology, individual applications have been developed specifically for pediatrics [3] [11]. One prime example of the application of computer-assisted diagnostics is the determination of bone age based on hand X-rays. As a result, in 2019 the Radiological Society of North America (RSNA) announced a competition to develop a corresponding AI solution [12], which contributed to the development of products such as BoneXpert, VUNO Med-BoneAge, or IB Lab PANDA [11] [13] [14].

Due to the shortage of personnel in pediatric radiology, commercial teleradiology concepts were developed early on in the United States [15]. By pooling expertise in larger pediatric radiology centers, the workload in departments without pediatric and adolescent radiologists can be reduced, because medical staff with a specialty in pediatric radiology no longer has to be available continuously [16]. In order to cope with the increased workload in pediatric radiology centers, it is essential to have adequate staffing. The GPR strategy paper recommends a similar cooperation between centers and referring physicians [1]. In addition, a second diagnosis by specialized pediatric radiologists can lead to substantial and therapy-relevant changes in the findings or assessment [17].

In the following, we present the results of a survey regarding the use of AI and teleradiology in pediatric radiology care in Germany, Austria, and Switzerland. The survey of pediatric radiologists aimed to evaluate the attitudes and use of technologies recommended in the GPR strategy paper in the field of pediatric radiology – also with regard to a potential solution to the shortage of pediatric and adolescent radiologists in Germany and Austria [1]. Additionally, in contrast to previous studies, we intend to pay special attention to the needs of pediatric radiology.

Materials & Methods

The survey was conducted in German on the SurveyMonkey platform (SurveyMonkey Inc., San Mateo, CA, USA). The survey was sent by the GPR and the SGPR to pediatric and adolescent radiologists in Germany, Austria, and Switzerland, and it was open from March to May 2023. The survey was advertised on the homepages of the GPR and the SGPR, as well as in the newsletters of both professional associations. In addition to the 5-point Likert scales (“fully agree”, “partly agree”, “neither agree nor disagree”, “partly disagree”, “fully disagree”), we included multiple choice questions with the option for multiple answers as well as free-text responses. The answers were voluntary and anonymous. Information about the place of work was not requested. Each individual question could be skipped. This manuscript was shortened editorially using ChatGPT (OpenAI, San Francisco, USA).

Results

Demographics

We received 36 responses from pediatric radiology departments in the DACH region, which corresponds to a response rate of 8.6% for 350 GPR and 69 SGPR members (excluding emeritus/retiree members in both organizations). Additional demographic information is included in [Table 1]. The distribution in the table roughly corresponds to the distribution of characteristics in the GPR.

Table 1 Demographics.

Parameters	Quantity (%)
Level of education
Doctor in training	1 (2.8%)
Specialist doctor	2 (5.6%)
Specialist doctor + specialty designation	33 (91.7%)
Age
< 30	0 (0%)
31–40	8 (22.2%)
41–50	8 (22.2%)
51–60	11 (30.6%)
>60	9 (25.0%)
Type of department
University hospital	27 (75.0%)
Non-university hospital	7 (19.4%)
Doctor’s office/practice	2 (5.6%)
Country
Germany	26 (72.2%)
Austria	5 (13.9%)
Switzerland	5 (13.9%)

Teleradiology

77.8% of respondents stated that teleradiology is an important topic for pediatric radiology (50% “fully agree”; 27.8% “partly agree”), but it only plays a major role in the daily work of half of the respondents (25% “fully agree”; 22.2% “partly agree”) ([Fig. 1] a). For further differentiation, we also asked about the use of teleradiology consultations/second opinions as well as teleradiology in line with radiation protection regulations (Section 14 of the Radiation Protection Act, StrSchG, Germany, or Section 32 of the Medical Radiation Protection Ordinance, MedStrSchV, Austria). Teleradiology second opinions are frequently carried out in half of the departments (50% “fully agree”; 8.3% “partly agree”) and teleradiology consultations are regularly used by one-third of the departments (19.4% “fully agree”; 16.7% “partly agree”). For a quarter of the respondents, teleradiology examinations are carried out in line with radiation protection regulations (16.7% “fully agree”; 8.3% “partly agree”) ([Fig. 1] b). The main barriers to further expansion are legal issues (n = 14, multiple answers), billing difficulties (n = 12), and technical limitations (n = 11). Other difficulties (n=15) include staff capacity, billing options, lack of quality assurance, and lack of guidelines. The greatest benefits of teleradiology are considered the all-around improved pediatric radiology care (47.2%) and the increased expertise (41.7%). Only one respondent sees no benefit (2.8%) ([Fig. 2]).

Fig. 1 a Relevance and utilization of teleradiology in pediatric radiology; b Utilization and reporting on teleradiology consultations and examinations in line with the StrSchG and MedStrSchV medical radiation regulations.

Fig. 2 a Limiting factors in the further implementation of teleradiology (multiple answers possible); b Greatest benefit of teleradiology.

Artificial Intelligence

Over 60% of pediatric radiologists consider AI to be important for pediatric radiology (38.9% “fully agree”; 22.2% “partly agree”). 30% are undecided (“neither agree nor disagree”). Only 8% see AI as a topic of no importance (5.6% “partly disagree”; 2.8% “fully disagree”). However, AI plays a major role in the daily work of less than 15% of the pediatric radiologists surveyed (2.8% “fully agree”; 11.1% “partly agree”) ([Fig. 3]). The most common areas of application are segmentation or volumetry (n = 15, multiple answers), dose reduction (n = 14), image acquisition/processing (n = 12), and detection/CAD (n = 10). We asked respondents whether three areas of application in pediatric radiology were suitable for an AI solution and were already regularly in use: over 80% view bone age determination as a suitable area of application (61.1% “fully agree”; 22.2% “partly agree”), but only 30% use such systems regularly (30.6% “fully agree”). The use of AI in pediatric chest X-rays is considered appropriate by one-third of respondents. In contrast, actual usage is 14% for image acquisition (11.1% “fully agree”; 2.8% “partly agree”) and just under 3% for the detection of pathologies (2.8% “fully agree”). 75% of respondents stated that AI can help improve CT protocols (38.9% “agree”; 36.1% “partly agree”). In more than half of the departments, innovative algorithms such as iterative reconstruction techniques and AI applications such as deep learning are used to improve image quality and reduce dose in CT examinations of children (44.4% “fully agree”; 11.1% “partly agree”) ([Fig. 4] a, b, c). The main obstacles to implementation of AI applications are costs (n = 21, multiple answers) and technical limitations (n = 19). Other obstacles include skepticism (n = 11), lack of expertise (n = 9), and legal issues (n = 7). Other obstacles (n=5) include low availability of pediatric AI applications, low return on investment, and administrative hurdles. The greatest benefit is the reduction in workload (37.1%), followed by time savings (17.1%). Other benefits (17.1%) include improved computing power, image quality, and error prevention, especially as support for doctors inexperienced in pediatric radiology ([Fig. 5] a, b).

Fig. 3 Relevance and use of AI in pediatric radiology.

Fig. 4 Examples of AI applications in pediatric radiology; a Bone age determination; b Chest X-rays; c Computed tomography.

Fig. 5 a Limiting factors in the further implementation of AI (multiple answers possible); b Greatest benefit of AI.

Discussion

This article presents a first survey on attitudes towards teleradiology and AI in pediatric and adolescent radiology in German-speaking countries.

In an international comparison, Shelmerdine et al., in a survey among members of the European Society of Paediatric Radiology (ESPR), the Society of Pediatric Radiology (SPR), and other professional associations, reported that pediatric radiologists had a positive perception of AI [18]. Other surveys on the use of teleradiology or AI are limited to adult radiology [19] [20] or specific applications, for example, surveys by an individual commercial provider (bone age determination using BoneXpert) [13]. The pediatric radiologists surveyed in the present study view both teleradiology and AI and its applications as forward-looking and relevant, although the current level of use lags behind the positive attitudes towards these topics. This may indicate possible administrative hurdles in implementation or inadequacies of these techniques.

One limiting factor of our study is the number of only 36 responding pediatric radiologists. The aforementioned global survey by ESPR, SPR, etc. on AI in pediatric radiology received 240 responses. Of these, only 159 responses were from pediatric radiologists, the rest were from non-medical professions. Overall, the response rate of all medical and non-medical respondents was 9.9% [18]. With a response rate of 8.6% from 419 members of the GPR and SGPR, our study has a comparable response rate. Other surveys in pediatric radiology on various topics, such as neonatal neurosonography or home office during the COVID-19 pandemic, have shown higher response rates between 13% and 17% [21] [22]. Our survey received responses from Germany and, in equal proportions, from Austria and Switzerland. We received more responses from the three DACH countries compared to the percentages from other multinational surveys previously [13] [18]. The responses came predominantly from university departments, which is consistent with previous studies [18].

Our survey shows that 75% of respondents consider teleradiology to be important for pediatric radiology, but actual use is moderately low. Legal uncertainties and personal concerns are cited as the main limiting factors resulting in lower usage. Teleradiology is subject to various laws and standards, for example, the Radiation Protection Act (StrSchG) in Germany and the Medical Radiation Protection Ordinance (MedStrSchV) in Austria. In addition to radiation protection regulations, other legal areas such as data protection or employment law have to be followed. The Swiss Society of Radiology (SSR), for example, summarizes the multitude of legal areas and standards to be observed in Switzerland in its white paper on teleradiology [23]. When preparing the survey, it was important for us to deliberately differentiate between a teleradiology examination in line with the StrSchG regulation and a teleradiology consultation in line with German case law. In the former case, the specialist doctor remotely supervises the examination performed by a medical technologist for radiology (MTR), which requires close and extensive cooperation in advance with the relevant referring physician [24]. In Switzerland, this corresponds to performing the examination in the teleradiology satellite [23]. The latter are less extensive and more readily available. Our survey confirms the perception of many pediatric radiologists that teleradiology consultations for second opinions are regularly carried out in more than half of the departments. In cases of substantial discrepancies between the assessments by general or pediatric radiologists, a pediatric radiology second opinion can bring about therapy-relevant changes to the findings and thus help to ensure that children are receiving the best possible medical care [17]. However, this leads to a significant increase in the workload for pediatric radiologists.

Compared to consultations, fewer teleradiology examinations are carried out in line with the StrSchG, MedStrSchV, or the Swiss satellite model. The comprehensive network of referring physicians and pediatric radiology centers recommended in the GPR strategy paper could further promote teleradiology cooperation, especially for second opinions and consultations [1]. The pediatric radiologists surveyed see the benefits of teleradiology in the improved availability of pediatric radiology expertise. In addition to legal uncertainties, billing issues and technical limitations are cited as limiting factors for developing the offering of teleradiology services. The GPR strategy paper therefore recommends technical and personnel needs assessments in order to plan the necessary expansion of services [1]. The lack of guidelines on the use of teleradiology for children and adolescents is considered a limiting factor. The heterogeneity of teleradiology services and related regulations was further identified as a limiting factor. Based on the GPR strategy paper, we focused the survey on the cooperation of pediatric radiology centers with peripheral referring physicians and not on possible diagnosis from home. Even though home office and reduced workload could basically make the discipline more attractive [16] [22], the respective centers are responsible for the relevant regulations. An Italian survey from general radiology also supports our observations that teleradiology is seen as an approach oriented on the future [19]. The concern described in that study – that teleradiology could impair cooperation with referring clinicians – was not borne out by our study. On the one hand, this could be due to the focus on cooperation with other radiology departments; on the other hand, pediatric radiology generally works closely already with pediatrics and pediatric surgery [1]. Overall, despite existing concerns, teleradiology is seen as an opportunity to improve radiology care for children and adolescents.

The pediatric radiologists surveyed also consider AI to be an important technology for pediatric and adolescent radiology. The international survey by Shelmerdine et al. mentioned above underscores this positive perception [18]. Due to our focus on key technologies to improve pediatric radiology care on an all-around basis, we have deliberately omitted questions related to the fear of being replaced. Especially since previous studies provided no evidence of these concerns [18]. However, the use of AI in everyday clinical practice, similar to the use of teleradiology, lags behind the positive attitudes towards these topics.

Although AI is applicable in almost every step of the radiology workflow [4] [5] [6], the pediatric radiologists surveyed use AI applications mainly for segmentation/volumetry, dose reduction, image acquisition and processing, and less for detection. Due to the special radiation risk of children [25], it is not surprising that a major focus of AI use is on dose reduction and optimal image acquisition [9] [10]. We further differentiated the use of AI using the following three examples: bone age determination, chest X-ray, and CT.

The biggest discrepancy was found regarding determination of bone age, which over 80% of respondents considered a suitable activity for AI, but only a third of respondents’ departments actually use a relevant AI solution. In general, bone age determination is considered one of the prime examples of AI solutions for pediatric radiology, which is why the “RSNA Pediatric Bone Age Machine Learning Challenge” was launched in 2019 [12]. Three approved commercial solutions are now available on the market [11], and they provide solid age determinations according to Greulich and Pyle [14]. A Europe-wide survey by Thodberg et al. among users of the commercial application BoneXpert may offer possible explanations for this discrepancy: although AI-based analysis saves time, it is usually only used as a supplement to radiology reporting. Human expertise is still required when assessing the X-ray of the left hand, as AI applications do not yet detect syndromal or metabolic abnormalities [13]. This applies primarily to the initial presentation for bone age determination. The assessment of bone age during the follow-up examinations, some of which are annual, can easily be carried out by the AI application. Implementation and operating costs for AI solutions are expensive [5]. In addition, the quality of continuing professional development of doctors could be compromised.

Few respondents consider the use of AI to be suitable for chest X-rays. Relevant AI solutions are hardly used by the pediatric radiologists surveyed. This applies particularly to diagnostic AI applications, while 13.8% of respondents use AI applications to improve image quality in chest X-rays. One explanation for the low level of use is the focus of diagnostic AI applications on individual diseases such as tuberculosis. The more intensive dissemination of AI applications is also hampered by the lack of pediatric cohort studies and data sets [5] [26]. In contrast, AI is used more frequently in image acquisition for quality improvement and dose savings [9]. Using virtual scatter grids in X-ray bed scans can improve image quality and reduce radiation exposure [5] [7] [27]. The smallest discrepancy between potential and actual use of AI applications can be observed in the application for dose reduction in CT. More than half of respondents use AI for image enhancement and dose optimization in CT. AI methods are making it possible to develop ultra-low-dose protocols [6] [28]. Iterative reconstruction algorithms have long played an important role in dose reduction in CT. Even though iterative reconstruction techniques do not correspond to AI applications such as deep learning, we have included them in this question because they are currently combined with AI methods [29].

The pediatric radiologists surveyed in our study consider the biggest future benefits of AI to be reduced workloads, time savings, and error prevention. This reflects the generally positive attitude towards AI that was reported by surveys in general radiology [20] [30]. Our results confirm the positive attitude of pediatric radiologists towards AI as observed in the international pediatric radiologist survey by Shelmerdine et al. [18]. AI applications to optimize radiation exposure are being used more frequently, particularly in German-speaking countries [9]. The main limiting factors for the use of AI are costs and technical limitations. These can be exacerbated by inadequate funding of pediatric medicine, as is the case in Germany, for example. Interestingly, in contrast to teleradiology, significantly fewer legal concerns were expressed with regard to AI.

Conclusions

Our survey shows that teleradiology and artificial intelligence are seen as future technologies for pediatric and adolescent radiology. However, there are still several obstacles to their more widespread use. Increased integration of these technologies could be promoted through clear legal frameworks, adapted billing options, and technical improvements. Additional studies and targeted pilot projects could help to establish these technologies further in clinical practice and subsequently evaluate whether the anticipated benefits, such reduced workloads, have actually materialized.

Interessenkonflikt

Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.

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Publication History

Received: 11 September 2024

Accepted after revision: 09 January 2025

Article published online:
06 February 2025