You are a first-year graduate student. You

European Society of Radiology (ESR) Insights into Imaging (2022) 13:107 https://doi.org/10.1186/s13244-022-01247-y

S TAT E M E N T

Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology European Society of Radiology (ESR)*

Abstract A survey among the members of European Society of Radiology (ESR) was conducted regarding the current practi- cal clinical experience of radiologists with Artificial Intelligence (AI)-powered tools. 690 radiologists completed the survey. Among these were 276 radiologists from 229 institutions in 32 countries who had practical clinical experience with an AI-based algorithm and formed the basis of this study. The respondents with clinical AI experience included 143 radiologists (52%) from academic institutions, 102 radiologists (37%) from regional hospitals, and 31 radiologists (11%) from private practice. The use case scenarios of the AI algorithm were mainly related to diagnostic interpreta- tion, image post-processing, and prioritisation of workflow. Technical difficulties with integration of AI-based tools into the workflow were experienced by only 49 respondents (17.8%). Of 185 radiologists who used AI-based algorithms for diagnostic purposes, 140 (75.7%) considered the results of the algorithms generally reliable. The use of a diagnos- tic algorithm was mentioned in the report by 64 respondents (34.6%) and disclosed to patients by 32 (17.3%). Only 42 (22.7%) experienced a significant reduction of their workload, whereas 129 (69.8%) found that there was no such effect. Of 111 respondents who used AI-based algorithms for clinical workflow prioritisation, 26 (23.4%) considered algorithms to be very helpful for reducing the workload of the medical staff whereas the others found them only moderately helpful (62.2%) or not helpful at all (14.4%). Only 92 (13.3%) of the total 690 respondents indicated that they had intentions to acquire AI tools. In summary, although the assistance of AI algorithms was found to be reliable for different use case scenarios, the majority of radiologists experienced no reduction of practical clinical workload.

Keywords: Professional issues, Artificial intelligence in imaging, Artificial intelligence and workload, Artificial intelligence in radiology

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.

Key points

• Artificial Intelligence (AI) algorithms are being used for a large spectrum of use case scenarios in clinical radiology in Europe, including assistance with inter- pretive tasks, image post-processing, and prioritisa- tion in the workflow.

• Most users considered AI algorithms generally reli- able and experienced no major problems with techni- cal integration in their daily practice.

• Only a minority of users experienced a reduction of the workload of the radiological medical staff due to the AI algorithms.

Background and objectives Digital imaging is naturally predisposed to benefit from the rapid and exciting progress in data science. The increase of imaging examinations and the associated diagnostic data volume have resulted in a mismatch

Open Access

Insights into Imaging

*Correspondence: [email protected] European Society of Radiology (ESR), Am Gestade 1, 1010 Vienna, Austria

Page 2 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

between the radiologic workforce and workload in many European countries. In an opinion survey conducted in 2018 among the members of the European Society of Radiology (ESR), many respondents had expectations that algorithms based on artificial intelligence (AI) and particularly machine learning could reduce radiologists’ workload [1]. Although a growing number of AI-based algorithms has become available for many radiological use case scenarios, most published studies indicate that only very few of these tools are helpful for reducing radi- ologists’ workload, whereas the majority rather result in an increased or unchanged workload [2]. Furthermore, in a recent analysis of the literature it was found that the available scientific evidence of the clinical efficacy of 100 commercially available CE-marked products was quite limited, leading to the conclusion that AI in radiology was still in its infancy [3]. The purpose of the present sur- vey was to get an impression of the current practical clin- ical experience of radiologists from different European countries with AI-powered tools.

Methods A survey was created by the members of the ESR eHealth and Informatics Subcommittee and was intentionally kept brief to allow responding in a few minutes. A few demographic questions included the country, type of institution (i.e. academic department, regional hospital, or private practice), and the main field of radiological practice as summarised in Tables 1, 2 and 3. For the more specific questions about the use of AI-based algorithms it was clearly stated that the answers were intended to reflect experience from clinical routine rather than research and testing purposes. The questions related to the use of AI addressed the respondents’ working expe- rience with certified AI-based algorithms, possible diffi- culties in integrating these algorithms in the IT system, and different use case scenarios for which AI-based algo- rithms were used in clinical routine, mainly distinguish- ing between tools aiming at facilitating the diagnostic interpretation process itself (questions shown in Fig.  1) from those that were aiming at facilitating the prioritisa- tion of examinations in the workflow. Specific questions addressed the technical integration of the algorithms (question mentioned in Table 4); radiologists’ confidence in the diagnostic performance (question mentioned in Table  5); quality control mechanisms to evaluate diag- nostic accuracy (questions mentioned in Tables  6, 7 and 8); communication of the use of diagnosis-related algo- rithms towards patients or in the radiology reports (ques- tions mentioned in Tables 9 and 10); and the usefulness of algorithms for reducing the radiologists’ workload (ques- tions mentioned in Tables  11 and 12). Respondents also had the opportunity to offer free text remarks regarding

their use of AI-based tools. Those respondents who did not use AI-based algorithms for the purpose of clinical practice were asked to skip all the questions related to clinical AI-use and to proceed directly to the last ques- tion about acquisition of AI-based algorithms, so that the opinions of all participating radiologists were taken into consideration for the final questions about their inten- tions regarding acquisition of such tools (question men- tioned in Fig. 2).

The survey was created through the ESR central office using the “Survey Monkey platform” (SurveyMonkey Inc., San Mateo, CA, USA) and 27,700 radiologist mem- bers of the ESR were invited by e-mail to participate in January 2022. The survey was closed after a second reminder in March 2022. The answers of the respond- ents were collected and analysed using an Excel software (Microsoft, Redmond, WA, USA).

Results A total of 690 ESR radiologist members from 44 coun- tries responded to the survey, for a response rate of 2.5%. The distribution per country and the proportion of respondents with practical clinical experience with AI- based algorithms per country are given in Table 1.

The 276 respondents with practical clinical experi- ence with AI-based algorithms were affiliated to 229 institutions in 32 countries; their answers formed the main basis of this study. Table 2 shows that 143 (52%) of the respondents with practical clinical experience with AI algorithms were affiliated to academic institutions, whereas 102 (37%) worked in regional hospitals, and 31 (11%) in private practice.

Table  3 characterises the same group of respondents as in Table  2 regarding their main field of activity show- ing that a wide range of subspecialties was represented in the survey and that abdominal radiology, neuroradiol- ogy, general radiology, and emergency radiology together accounted for half of the respondents. A detailed analysis of the results according to subspecialties was beyond the scope of the study because of the relatively small number of resulting groups.

The experience regarding technical integration of the software algorithms into the IT system or work- flow is summarised in Table  4, showing that only 17.8% of respondents reported difficulties with integration of these tools, whereas a majority of 44.5% observed no such difficulties, although 37.7% of respondents did not answer this question.

Algorithms were used in clinical practice either for assistance in interpretation or for prioritisation of work- flow. An overview of the scenarios for which AI- powered algorithms were the used by the respondents is given in Fig. 1.

Page 3 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

Table 1 Distribution of all 690 respondents by countries and proportion of radiologists with practical clinical experience with AI algorithms

Country Number of respondents per country

Number of respondents with practical clinical experience with AI per country

Percentage of radiologists with practical clinical experience in AI per country (%)

Italy 71 23 32

Spain 64 19 30

UK 60 23 38

Germany 50 23 46

Netherlands 50 35 70

Sweden 29 14 48

Denmark 27 15 56

Turkey 27 3 11

Norway 26 12 46

Switzerland 27 14 54

France 25 12 48

Belgium 23 13 57

Austria 21 12 57

Greece 21 5 24

Portugal 17 5 29

Romania 16 4 25

Ukraine 13 3 23

Croatia 11 4 36

Russian Fed 11 4 36

Bulgaria 10 0 0

Poland 10 4 40

Finland 7 4 57

Hungary 7 3 43

Serbia 7 1 14

Slovenia 7 3 43

Slovakia 6 5 83

Ireland 5 2 40

Lithuania 5 2 40

Bos. & Herzegovina 4 0 0

Czech Republic 4 3 75

Israel 4 2 50

Latvia 4 0 0

Armenia 3 0 0

Albania 2 0 0

Azerbaijan 2 0 0

Belarus 2 0 0

Estonia 2 2 100

Georgia 2 0 0

Kazakhstan 2 0 0

Luxembourg 2 1 50

Cyprus 1 0 0

Iceland 1 0 0

Kosovo 1 1 100

Uzbekistan 1 0 0

Total 690 276

Page 4 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

Use of algorithms for assistance in diagnostic interpretation Among the 276 respondents who shared their practi- cal experience with AI-based tool experience, a total of 185 (67%) reported clinical experience with one or more integrated algorithms for routine diagnostic tasks. As seen in Fig.  1 there were different use case scenarios, the commonest being detection or mark- ing of specific findings. The free text remarks of the respondents showed a large range of pathologies in practically all clinical fields and with almost all imag- ing modalities. Typical examples of pathologies were

pulmonary emboli and parenchymal nodules, cerebral haemorrhage and reduced cerebrovascular blood flow, or colonic polyps on CT. Other tasks included the detection of traumatic lesions, e.g. the presence of bone fractures on conventional radiographs or the calcula- tion of bone age. The second most common diagnostic scenario was assistance with post-processing (e.g. using AI-based tools for image reconstruction or quantita- tive evaluation of structural or functional abnormali- ties), followed by primary interpretation (i.e. potentially replacing the radiologist), assistance with differential

Table 2 Respondents with practical clinical experience with AI-based algorithms: distribution of origin by countries and type of institutions

Country Number of respondents per country

Number of institutions per country

Respondents from academic departments

Respondents from private practice

Respondents from regional hospitals

Netherlands 35 20 16 0 19

Germany 23 21 14 3 6

Italy 23 21 13 0 10

UK 23 22 7 2 14

Spain 19 16 14 1 4

Denmark 15 7 11 1 3

Switzerland 14 13 6 6 2

Sweden 14 14 7 1 6

Belgium 13 9 5 1 7

Austria 12 11 7 1 4

France 12 11 5 5 2

Norway 12 9 6 0 6

Greece 5 5 2 2 1

Portugal 5 4 0 4 1

Slovakia 5 5 2 2 1

Croatia 4 4 1 1 2

Finland 4 3 3 0 1

Poland 4 3 3 0 1

Romania 4 2 2 0 2

Russian Fed 4 4 3 0 1

Czech Republic 3 3 1 0 2

Hungary 3 3 2 0 1

Slovenia 3 3 2 0 1

Turkey 3 3 3 0 0

Ukraine 3 2 2 1 0

Estonia 2 2 1 0 1

Ireland 2 2 1 0 1

Israel 2 2 2 0 0

Lithuania 2 2 0 0 2

Kosovo 1 1 1 0 0

Luxembourg 1 1 0 0 1

Serbia 1 1 1 0 0

Total 276 229 143 (52%) 31 (11%) 102 (37%)

Page 5 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

diagnosis, e.g. by facilitation of literature search, and quality control.

Although a detailed analysis of all different diagnostic use case scenarios was beyond the scope of this survey, the respondents’ answers to specific survey questions are shown in Tables 5, 6, 7, 8, 9, 10 and 11. Because some respondents skipped or incompletely answered some questions, the number of yes/no answers per question was not complete. As shown in Table  5, most respond- ents (75.7%) found the results provided by the algorithms generally reliable.

A significant number of respondents declared that they used mechanisms of quality assurance regarding the diagnostic performance of the algorithms. These included keeping records of diagnostic discrepancies

between the radiologist and the algorithms in 44.4%, establishing receiver-operator characteristic (ROC) curves of diagnostic accuracy based on the radiologist’s diagnosis (34.1%) and/ or ROC curves based on the final medical record (30.3%) (Tables 6, 7 and 8).

The use of a diagnostic algorithm was disclosed to patients by 17.3% of the respondents but mentioned in the report by 34.6% (Tables 9 and 10).

Only a minority of 22.7% of respondents who used AI- based algorithms for diagnostic purposes experienced a reduction of their workload, whereas 69.8% reported

Table 3 Respondents with practical clinical experience with AI-based algorithms: main field of activity/subspecialty

Field of practice Number of respondents (%)

Abdominal radiology 45 16.3

Neuroradiology 45 16.3

General radiology 39 14.1

Chest radiology 32 11.6

Cardiovascular radiology 24 8.7

Musculoskeletal radiology 23 8.3

Oncologic imaging 23 8.3

Breast radiology 17 6.2

Emergency radiology 10 3.6

Paediatric radiology 8 2.9

Urogenital radiology 6 2.2

Head and Neck radiology 4 1.5

Total 276 100

111 (40%)

11 (4%)

14 (5%)

19 (7%)

79 (28.6%)

142 (51.5%)

0 50 100 150

Workflow Priori sa on

Quality control

Assistance during interpreta on (e.g., access to literature, facilita ng differen al diagnosis etc.)

Primary interpreta on (=replacing the radiologist)

Assistance for post-processing (e.g., image reconstruc on, quan ta ve evalua on)

Assistance during interpreta on (e.g., detec ng / marking of specific findings like nodules, emboli etc.)

Fig. 1 Which type of scenario (use case) was addressed by the used AI algorithm(s) in clinical routine? The answers of all 276 respondents with practical clinical AI experience are shown, including the number of respondents using one or more algorithms for assistance in diagnostic interpretation (green) and/ or workflow prioritisation (blue)

Table 4 Respondents with practical clinical experience with AI-based algorithms: Have there been any major problems with integration of AI-based algorithms into your IT system/workflow?

Answer Number of respondents (%)

Yes 49 17.8

No 123 44.5

Skipped 104 37.7

Total 276 100

Table 5 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Were the findings of the algorithm(s) considered to be reliable?

Answer Number of respondents (%)

Yes 140 75.7

No 31 16.8

Skipped 14 7.5

Total 185 100

Page 6 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

that there was no reduction effect on their workload (Table 11).

Use of algorithms for prioritisation of workflow Among the 276 respondents who had practical expe- rience with AI-based tools, there were 111 respond- ents (40%) reporting experience with algorithms for

prioritisation of image sets in their clinical workflow. As shown in Table  12, the prioritisation algorithms were considered to be very helpful for reducing the workload of the medical staff by 23.4% respondents who used them, whereas the other users found them only moderately helpful (62.2%) or not helpful at all (14.4%).

Intentions of all respondents regarding the acquisition of an AI‑based algorithm All participants of the survey, regardless of their prac- tical clinical experience, were given the opportunity to answer the question whether they intended to acquire a certified AI- based software. Of the 690 participants, 92 (13.3%) answered “yes”, 363 (52.6%) answered “no,” and 235 (34.1%) did not answer this question. Figure  2

Table 6 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Were discrepancies between the software and the radiologist recorded?

Answer Number of respondents (%)

Yes 82 44.4

No 89 48.1

Skipped 14 7.5

Total 185 100

Table 7 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Was the diagnostic accuracy (ROC curves) supervised on a regular basis in comparison with the radiologist’s diagnosis?

Answer Number of respondents (%)

Yes 63 34.1

No 108 58.4

Skipped 14 7.5

Total 185 100

Table 8 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Was the diagnostic accuracy (ROC curves) supervised on a regular basis in comparison with the final diagnosis in the medical record?

Answer Number of respondents (%)

Yes 56 30.3

No 115 62.2

Skipped 14 7.5

Total 185 100

Table 9 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Were patients informed that an AI software was used to reach the diagnosis?

Answer Number of respondents (%)

Yes 32 17.3

No 139 75.2

Skipped 14 7.5

Total 185 100

Table 10 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Was the use of an AI software to reach the diagnosis mentioned in the report?

Answer Number of respondents (%)

Yes 64 34.6

No 107 57.9

Skipped 14 7.5

Total 185 100

Table 11 Experience of 185 respondents with AI-based algorithms for clinical diagnostic interpretive tasks: Has (have) the algorithm(s) used for diagnostic assistance proven to be helpful in reducing the workload for the medical staff?

Answer Number of respondents (%)

Yes 42 22.7

No 129 69.8

Skipped 14 7.5

Total 185 100

Table 12 Experience of 111 respondents with AI-based algorithms for clinical workflow prioritisation: Has the algorithm proven to be helpful in reducing the workload for the medical staff?

Answer Number of respondents (%)

Not at all helpful 16 14.4

Moderately helpful 69 62.2

Very helpful 26 23.4

Total 111 100

Page 7 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

summarises the reasons given by participants who did not intend to acquire AI-based algorithms for their clinical use.

Discussion While the previous survey on AI [1] was based on the expectations of the ESR members regarding the impact of AI on radiology, the present survey intended to obtain an overview of current practical clinical experience with AI-based algorithms. Although the respondents with practical clinical experience in this survey represent only 1% of the ESR membership, their proportion among all respondents varied greatly among countries. The geo- graphical distribution of the 276 radiologists who shared their experience with such tools in clinical practice shows that the majority was affiliated to institutions in West- ern and Central Europe or in Scandinavia. Half of all respondents with practical clinical experience with AI tools was affiliated to academic institutions, whereas the other half practiced radiology in regional hospitals or in private services. Since it is likely that the respondents in this survey were radiologists with a special interest in AI- based algorithms, it cannot be assumed that this survey reflects the true proportion of radiologists in the Euro- pean region with practical clinical experience with AI- based tools.

Most of the respondents of this brief survey did not encounter major problems related to the integration the AI-based software tools into the local IT systems; less than 18% did have such issues. However, it must be taken into consideration that radiologists are not always directly involved in the technical process of software integration; this fact may perhaps also explain the rela- tively high number of respondents who did not reply to this specific question.

Today, AI-based tools for diagnostic purposes may address a large range of use case scenarios. Although this

was reflected by the free text answers of the respondents of the present study, the present survey distinguished mainly between algorithms for diagnostic purposes and those for the prioritisation of workflow whereas a detailed analysis of all the different individual use case scenarios was beyond the scope of this survey. Since diagnostic tools are usually quite specific and related to organs and pathologies, even radiologists working in the same institution but in different subspecialties may have different experiences with different algorithms related to their respective fields.

In a recent survey among the members of the Ameri- can College of Radiology (ACR) the most common appli- cations for AI were intracranial haemorrhage, pulmonary embolism, and mammographic abnormalities, although it was stated that in the case of mammography, confusion must be avoided between AI-based tools and the more traditional software for computer aided diagnosis (CAD) [4]. It was estimated that AI was used by approximately 30% of radiologists, but concerns over inconsistent per- formance and a potential decrease in productivity were considered to be barriers limiting the use of such tools. Over 90% of respondents would not trust these tools for autonomous use. It was concluded that despite ini- tial predictions the impact of AI on clinical practice was modest [4].

Quality assurance of algorithms that are based on machine–learning may be quite time-consuming and requires considerable resources. Effective supervision of the sensitivity and specificity of a device that adapts itself over time may be done by recording differences between the diagnosis of the radiologist and the algorithm but ide- ally combines regular monitoring by comparison against a final diagnosis as a gold standard—a so-called “ground truth”. Despite the enthusiasm about AI-based tools there are some barriers to be addressed when implementing this new technology in clinical practice. These include

23 (6.3%)

83 (22.9%)

96 (26.4%)

161 (44.4%)

0 20 40 60 80 100 120 140 160 180

No reason given

Adds too much workload

Does not perform as well as advertised

No additional value

Fig. 2 Reasons given by 363 of all 690 participants of the survey (regardless of their experience with AI-based algorithms in clinical workflow) for not intending to acquire a certified AI-based algorithm for their clinical practice

Page 8 of 9European Society of Radiology (ESR) Insights into Imaging (2022) 13:107

the large amount of annotated image data required for supervised learning as well as validation and quality assurance for each use case scenario of these algorithms, and, last but not least, regulatory aspects including certi- fication [5, 6]. A recent overview of commercially avail- able CE-marked AI products for radiological use found that scientific evidence of potential efficacy of level 3 or higher was documented in only 18 of 100 products from 54 vendors and that for most of these products evidence of clinical impact was lacking [3].

Nonetheless, as a general impression, most of the respondents of this ESR survey who used AI-based algo- rithms in their clinical practice considered their diagnos- tic findings to be reliable for the spectrum of scenarios for which they were used. It is noteworthy that 44% of the respondents recorded discrepancies occurring between the radiologists’ and the algorithms’ findings and that approximately one-third indicated that they gener- ated ROC curves based on the radiological report or the clinical record in order to calculate the performance of algorithms in clinical practice. Details regarding the meth- odologies, e.g. the degree of automation used for estab- lishing these data, were neither asked from nor provided by the respondents. However, since over one-half of the respondents worked in academic institutions, it is possi- ble that some of the algorithms were not only evaluated in the context of clinical routine but also in the context of scientific research studies, thus explaining the relatively high level of quality supervision of the algorithms. Only a small minority of radiologists participating in this survey informed the patients about the use of AI for the diagno- sis and about one-third mentioned it in their reports. This may be understandable as long as the radiologist and not the algorithm makes the final diagnosis.

However, the important question remains to what extent AI-powered tools can reduce radiologists’ work- load. In the previous ESR survey conducted in 2018, 51% of respondents expected that the use of AI tools would lead to a reduced reporting workload [1]. The actual con- tributions of AI to the workload of diagnostic radiologists were assessed in a recent analysis based on large num- ber of published studies. It was concluded that although there was often added value to patient care, workload was decreased in only 4% but increased in 48% and remained unchanged in 46% institutions [2]. The results of the pre- sent survey are somewhat more optimistic since almost 23% of respondents experienced a reduction of their workload when using algorithms for diagnostic assistance in clinical practice, whereas almost 70% did not. Obser- vations with algorithms aiming at workflow prioritisa- tion were comparable. In view of the wide range of use case scenarios for which AI- based tools can be applied,

additional studies are needed in order to determine for which specific tasks and questions in which subspecial- ties AI-based algorithms could be helpful to reduce radi- ologists’ workload. Typically, this could be the case in those scenarios that address the detection of relatively simple diagnostic findings and a high volume of cases.

The previous ESR survey from 2018 included 675 par- ticipants of which 20% were already using AI-powered tools and 30% planned to do so [1]. The present ESR sur- vey included 690 participants of which 276 (40%) had experience with such tools in clinical practice. However, when all the participants of the present survey were asked whether they intended to acquire a certified AI- based algorithm, only a minority (13.3%) answered yes, whereas the majority either answered no (52.6%) or did not answer the question (34.1%). Reasons given for the negative answers included doubts about the added value or the advertised performance or concerns regarding added workload. We must consider, however, that the answers to this particular question included not only the opinions of the respondents who had experience with practical clinical use but also of those who used these algorithms rather in the context of scientific projects including non-commercial, home-grown AI-based tools.

The results of the present ESR survey are difficult to compare with the recent ACR survey [4] not only because the questions were not identical, but also because of the existing diversity among European countries. Nonetheless, both surveys conclude that, compared with initial predic- tions and expectations, the overall impact of AI-based algorithms on current radiological practice is modest.

Several limitations of this brief survey need to be men- tioned. Firstly, the survey data cannot reflect the true proportion of European radiologists using AI. Secondly, the answers to several questions can only provide a gen- eral overview, although some of the issues addressed by this survey would deserve a more detailed analysis. This is true, for example, regarding the differentiation of use case scenarios as well as the methodologies used for the verification of their results. Thirdly, the observations are based on the situation in 2022, and results and opinions may change rapidly in this evolving field.

In summary, this survey suggests that, compared with initial expectations, the use of AI- powered algorithms in practical clinical radiology today is limited, most impor- tantly because the impact of these tool

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.