Artificial intelligence: Who is responsible for the diagnosis?

The aim of the paper is to find an answer to the question “Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?” When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional’s responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional’s ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots

Artificial intelligence: Who is responsible for the diagnosis?

The aim of the paper is to find an answer to the question “Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?” When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional’s responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional’s ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots

Results of an Italian survey on teleradiology

OBJECTIVES : The aim of this study is to present the results of the Italian survey on teleradiology (TR). METHODS: Two radiologists created an online electronic survey using the Survey Monkey web-based tool. The questionnaire was then improved by suggestions from a multidisciplinary group of experts. In its final form, the survey consisted of 19 multiple-choice questions. Space was left below each question for participants to add their personal comments. Members of Italian Society of Medical Radiology (SIRM) were given 2 weeks to perform the survey. RESULTS: A total of 1599 radiologists, corresponding to 17 % of all SIRM radiologists, participated into the online survey. As a result, 62 % of participants have a positive opinion on teleradiology, while 80 % including 18 % with a negative opinion believe that teleradiology will have a future. 55 % of responders (n = 874) use teleradiology in their clinical practice. The majority of users adopt intra-mural teleradiology for coverage of emergencies (47 %), of night and weekend shifts (37 %) or to even out distribution workload (33 %). Most responders still show concern on the use of teleradiology. In particular, they think that teleradiology is too impersonal (40 %), and that it is responsible for insufficient communication with the referring clinician (39 %). CONCLUSIONS: The majority of Italian radiologists are favorable to teleradiology. However, they have concerns that teleradiology may further reduce communication with the referring clinician ad patient

Results of an Italian survey on teleradiology.

OBJECTIVES: The aim of this study is to present the results of the Italian survey on teleradiology (TR). METHODS: Two radiologists created an online electronic survey using the Survey Monkey web-based tool. The questionnaire was then improved by suggestions from a multidisciplinary group of experts. In its final form, the survey consisted of 19 multiple-choice questions. Space was left below each question for participants to add their personal comments. Members of Italian Society of Medical Radiology (SIRM) were given 2 weeks to perform the survey. RESULTS: A total of 1599 radiologists, corresponding to 17 % of all SIRM radiologists, participated into the online survey. As a result, 62 % of participants have a positive opinion on teleradiology, while 80 % including 18 % with a negative opinion believe that teleradiology will have a future. 55 % of responders (n = 874) use teleradiology in their clinical practice. The majority of users adopt intra-mural teleradiology for coverage of emergencies (47 %), of night and weekend shifts (37 %) or to even out distribution workload (33 %). Most responders still show concern on the use of teleradiology. In particular, they think that teleradiology is too impersonal (40 %), and that it is responsible for insufficient communication with the referring clinician (39 %). CONCLUSIONS: The majority of Italian radiologists are favorable to teleradiology. However, they have concerns that teleradiology may further reduce communication with the referring clinician ad patient

Cost determinants of tuberculosis management in a low-prevalence country

Division of respiratory medicine in a specialised infectious disease hospital in Rome, Italy

Artificial intelligence: radiologists\u2019 expectations and opinions gleaned from a nationwide online survey

Purpose: To report the results of a nationwide online survey on artificial intelligence (AI) among radiologist members of the Italian Society of Medical and Interventional Radiology (SIRM). Methods and materials: All members were invited to the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 13 questions about the participants\u2019 demographic information, perceived advantages and issues related to AI implementation in radiological practice, and their overall opinion about AI. Results: In total, 1032 radiologists (equaling 9.5% of active SIRM members for the year 2019) joined the survey. Perceived AI advantages included a lower diagnostic error rate (750/1027, 73.0%) and optimization of radiologists\u2019 work (697/1027, 67.9%). The risk of a poorer professional reputation of radiologists compared with non-radiologists (617/1024, 60.3%), and increased costs and workload due to AI system maintenance and data analysis (399/1024, 39.0%) were seen as potential issues. Most radiologists stated that specific policies should regulate the use of AI (933/1032, 90.4%) and were not afraid of losing their job due to it (917/1032, 88.9%). Overall, 77.0% of respondents (794/1032) were favorable to the adoption of AI, whereas 18.0% (186/1032) were uncertain and 5.0% (52/1032) were unfavorable. Conclusions: Radiologists had a mostly positive attitude toward the implementation of AI in their working practice. They were not concerned that AI will replace them, but rather that it might diminish their professional reputation

Cost determinants of tuberculosis management in a low-prevalence country

SETTING: Division of respiratory medicine in a specialised infectious disease hospital in Rome, Italy. OBJECTIVE: Retrospective evaluation of tuberculosis (TB) care associated costs in an integrated in- and outpatient management programme. DESIGN: Review of the medical records of 92 human immunodeficiency virus negative TB cases admitted between September 2000 and May 2003. RESULTS: Length of in-hospital stay (45 +/- 35 days) was the major cost determinant, as hospitalisation accounted for almost 80% of the total costs of the case, with fixed bed-per-day charges amounting to 76% of hospital costs. Factors associated with higher costs were chest X-ray SUMMARY score, fever, sputum bacterial load and multidrug resistance (P < 0.05). Cure/treatment completion was achieved in 82% of patients entering the out-patient programme (63% of all cases). Homelessness, age and comorbidities were associated with unfavourable outcomes. CONCLUSIONS: A closely followed hospital-centred protocol carried out in a high-resource setting may produce acceptable cure/completion treatment rates. As a too high fraction of resources invested in TB control goes toward hospital costs, out-patient treatment strategies should be implemented

Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey

Purpose: To report the results of a nationwide online survey on artificial intelligence (AI) among radiologist members of the Italian Society of Medical and Interventional Radiology (SIRM). Methods and materials: All members were invited to the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 13 questions about the participants’ demographic information, perceived advantages and issues related to AI implementation in radiological practice, and their overall opinion about AI. Results: In total, 1032 radiologists (equaling 9.5% of active SIRM members for the year 2019) joined the survey. Perceived AI advantages included a lower diagnostic error rate (750/1027, 73.0%) and optimization of radiologists’ work (697/1027, 67.9%). The risk of a poorer professional reputation of radiologists compared with non-radiologists (617/1024, 60.3%), and increased costs and workload due to AI system maintenance and data analysis (399/1024, 39.0%) were seen as potential issues. Most radiologists stated that specific policies should regulate the use of AI (933/1032, 90.4%) and were not afraid of losing their job due to it (917/1032, 88.9%). Overall, 77.0% of respondents (794/1032) were favorable to the adoption of AI, whereas 18.0% (186/1032) were uncertain and 5.0% (52/1032) were unfavorable. Conclusions: Radiologists had a mostly positive attitude toward the implementation of AI in their working practice. They were not concerned that AI will replace them, but rather that it might diminish their professional reputation

Dematerialisation of patient’s informed consent in radiology: insights on current status and radiologists’ opinion from an Italian online survey

Purpose: To assess the current status of patient’s informed consent (PIC) management at radiological centres and the overall opinion of radiologist active members of the Italian Society of Medical Radiology (SIRM) about PIC dematerialisation through an online survey. Methods and materials: All members were invited to join the survey as an initiative by the Imaging Informatics Chapter of SIRM. The survey consisted of 11 multiple-choice questions about participants’ demographics, current local modalities of PIC acquisition and storage, perceived advantages and disadvantages of PIC dematerialisation over conventional paper-based PIC, and overall opinion about PIC dematerialisation. Results: A total of 1791 radiologists (amounting to 17.4% of active SIRM members for the year 2016) joined the survey. Perceived advantages of PIC dematerialisation were easier and faster PIC recovery (96.5%), safer storage and conservation (94.5%), and reduced costs (90.7%). Conversely, the need to create dedicated areas for PIC acquisition inside each radiological unit (64.0%) and to gain preliminary approval for the use of advanced digital signature tools from patients (51.8%) were seen as potential disadvantages. Overall, 94.5% of respondents had a positive opinion about PIC dematerialisation. Conclusion: Radiologists were mostly favourable to PIC dematerialisation. However, concerns were raised that its practical implementation might face hurdles due to its complexity in current real life working conditions