The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. ...Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.
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.
Background
The COVID-19 outbreak has played havoc within healthcare systems, with radiology sharing a substantial burden. Our purpose is to report findings from a survey on the crisis impact among ...members of the Italian Society of Medical and Interventional Radiology (SIRM).
Methods
All members were invited to a 42-question online survey about the impact of the COVID-19 outbreak on personal and family life, professional activity, socioeconomic and psychological condition. Participants were classified based on working in the most severely affected Italian regions (“hot regions”) or elsewhere.
Results
A total of 2150 radiologists joined the survey. More than 60% of respondents estimated a workload reduction greater than 50%, with a higher prevalence among private workers in hot regions (72.7% vs 66.5% elsewhere,
p
= 0.1010). Most respondents were concerned that the COVID-19 outbreak could impact the management of non-COVID-19 patients and expected a work overload after the crisis. More than 40% were moderately or severely worried that their professional activity could be damaged, and most residents believed that their training had been affected. More than 50% of respondents had increased emotional stress at work, including moderate or severe symptoms due to sleep disturbances, feeling like living in slow motion and having negative thoughts, those latter being more likely in single-living respondents from hot regions log OR 0.7108 (CI95% 0.3445 ÷ 1.0770),
p
= 0.0001.
Conclusions
The COVID-19 outbreak has had a sensible impact on the working and personal life of SIRM members, with more specific criticalities in hot regions. Our findings could aid preserving the radiologists’ wellbeing after the crisis.
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our ...professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
Background
Our purpose was to assess the performance of ESR iGuide for assisting the selection of the most appropriate imaging tests based on clinical signs and symptoms in patients with ...hepatocellular carcinoma (HCC) or cholangiocarcinoma (CC).
Methods
We retrospectively reviewed the medical records of 113 patients with a final diagnosis of HCC or CC. Data from a cohort of 40 patients with a reported clinical history suggestive for either disease, who had undergone at least their first imaging test related to their condition at the same Institution, were entered into ESR iGuide. The appropriateness level of the diagnostic tests suggested by ESR iGuide was compared with that of the tests actually performed.
Results
All patients underwent several imaging examinations, ranging from a minimum of 1 to a maximum of 4, for a total of 98 diagnostic procedures. Of these, 79.6% (78/98) were considered “
usually appropriate
” by ESR iGuide, 11.2% (11/98) were designated as “
may be appropriate
”, and 9.2% (9/98) were not even suggested. Given a total estimated cost of €14,016 for the 98 examinations performed within the regional (BLINDED) healthcare system, the usage of ESR iGuide would have allowed saving €3033 (21.6%) due to inappropriate testing.
Conclusions
In patients with HCC or CC, ESR iGuide can be effective in guiding the selection of the appropriate imaging examinations and cutting costs due to inappropriate testing.
Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside ...healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.
Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.
Background and objective
The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling ...the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks.
Methods
Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: “imaging” AND “biobanks” and “imaging” AND “repository”. Moreover, the Community Research and Development Information Service (CORDIS) database (
https://cordis.europa.eu/projects
) was searched using the terms: “imaging” AND “biobanks”, also including collections, projects, project deliverables, project publications and programmes.
Results
Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request.
Conclusions
Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks.
Key Points
• Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data.
• Most imaging biobanks retrieved were European, disease-oriented and accessible under user request.
• While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
Highlights • Medical students tend to overstate their knowledge of radiation protection (RP). • Overall RP knowledge of young doctors and students is suboptimal. • RP teaching to undergraduates and ...postgraduates needs to be substantially improved.
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