Table 1.
- Exposure assessment to AI in radiology corresponding to gender, training level and familiar with big data.
| Questions/category | n (%) | P-value | ||
|---|---|---|---|---|
| Gender | Training level | Big data | ||
| Which radiological subspecialties do you foresee will be more influenced by AI in the next 5-10 years? (choose up to 3) | ||||
| Breast | 99 (64.3) | |||
| Molecular/nuclear imaging | 56 (36.4) | |||
| Neuroradiology | 54 (35.1) | |||
| Thoracic | 54 (35.1) | |||
| Emergency | 32 (20.8) | |||
| Musculoskeletal | 24 (15.6) | |||
| Cardiovascular | 22 (14.3) | |||
| General | 22 (14.3) | |||
| Gastrointestinal/abdominal | 20 (13) | |||
| Interventional | 17 (11) | |||
| Oncologic imaging | 16 (10.4) | |||
| Head and neck | 11 (7.1) | |||
| Urogenital | 3 (1.9) | |||
| Pediatric | 2 (1.3) | |||
| Which techniques do you foresee will be the most important fields of AI applications in the next 5-10 years? (choose up to 3) | ||||
| Mammography | 91 (59.1) | |||
| PET/nuclear | 72 (46.8) | |||
| CT | 69 (44.8) | |||
| Radiography | 61 (39.6) | |||
| MRI | 46 (29.9) | |||
| DXA | 37 (24) | |||
| Angiography/fluoroscopy | 18 (11.7) | |||
| Hybrid imaging | 9 (5.8) | |||
| Ultrasound | 8 (5.2) | |||
| Experimental imaging (animal models) | 6 (3.9) | |||
| Optical imaging | 4 (2.6) | |||
| Which of the following AI applications do you think are more relevant as aids to the radiological profession? (choose up to 3) | ||||
| Detection in asymptomatic subjects (screening) | 82 (53.2) | |||
| Detection of incidental findings | 74 (48.1) | |||
| Image post-processing | 73 (47.4) | |||
| Imaging protocol optimization | 54 (35.1) | |||
| Support to structured reporting | 44 (28.6) | |||
| Lesion characterization/diagnosis in symptomatic subjects | 43 (27.9) | |||
| Staging/restaging in oncology | 43 (27.9) | |||
| Quantitative measure of imaging biomarkers | 31 (20.1) | |||
| Prognosis | 12 (7.8) | |||
| Do you foresee an impact of AI on the professional life of radiologists in terms of the number of job positions in the next 5-10 years? | ||||
| No | 67 (43.5) | |||
| Yes, job positions will be reduced | 64 (41.6) | 0.742 | 0.919‡ | 0.869 |
| Yes, job positions will increase | 23 (14.9) | |||
| Do you foresee an impact of AI on the professional life of radiologist in terms of total reporting workload in the next 5-10 years? | ||||
| No | 29 (18.8) | |||
| Yes, it will increase | 43 (27.9) | 0.44 | 0.192 | 0.905 |
| Yes, it will be reduced | 82 (53.2) | |||
| In the next 5-10 years, the use of AI-based applications will make radiologists’ duties | ||||
| More technical | 28 (18.2) | |||
| More clinical | 38 (24.7) | |||
| Unchanged | 9 (5.8) | 0.566‡ | 0.269‡ | 0.244‡ |
| More technical and clinical | 79 (51.3) | |||
| Do you think that in the next 5-10 years, the use of AI-based applications will help to reduce the need for subspecializing? | ||||
| No, radiologists will be more focused on radiology subspecialties | 102 (66.2) | |||
| Yes, radiologists will be less focused on radiology subspecialties | 16 (10.4) | 0.685 | 0.033 | 0.065 |
| The rate of dedication to subspecialties will remain unchanged | 36 (23.4) | |||
| In the next 5-10 years, who will take the legal responsibility of AI-system output? | ||||
| Radiologists | 105 (68.2) | |||
| Other physicians (namely, clinicians asking for the imaging study) | 9 (5.8) | |||
| Developers of AI applications | 79 (51.3) | |||
| Insurance companies | 35 (22.7) | |||
| In the next 5-10 years, will patients accept a report from AI applications without supervision and approval by a physician? | ||||
| Yes | 17 (11) | |||
| No | 79 (51.3) | 0.381 | 0.489‡ | 0.847 |
| Difficult to estimate at present | 58 (37.7) | |||
| What will be the role of radiologists in the development/validation of AI applications to medical imaging? (choose at most 3) | ||||
| Supervise all stages needed to develop an AI based application | 97 (63) | 0.320 | 0.169 | 0.687 |
| Help in task definition | 67 (43.5) | 0.255 | 0.663 | 0.381 |
| Develop AI-based applications | 59 (38.3) | 0.175 | 0.070 | 0.742 |
| Provide labelled images | 49 (31.8) | 0.114 | 0.268 | 0.762 |
| None | 7 (4.5) | 1.00‡ | 0.452‡ | 1.00‡ |
PET: positron emitted tomography, CT: computed tomography, MRI: magnetic resonance imaging, DXA: dual-energy x-ray absorptiometry, AI: artificial intelligence, ‡Fisher’s exact test