Integrating Artificial Intelligence Into Radiologic Practice: A Look to the Future

Summary of the proceedings of the International Forum 2021: “A more visible radiologist can never be replaced by AI”

The ESR International Forum at the ECR 2021 discussed effects of artificial intelligence on the future of radiology and the need for increased visibility of radiologists. The participating societies were invited to submit written reports detailing the current situation in their country or region. The European Society of Radiology (ESR) established the ESR International Forum in order to discuss hot topics in the profession of radiology with non-European radiological partner societies. At the ESR International Forum 2021, different strategies, initiatives and ideas were presented with regard to radiology community’s response to the changes caused by the emerging AI technology.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

Women's attitudes to the use of AI image readers: a case study from a national breast screening programme

Background

Researchers and developers are evaluating the use of mammogram readers that use artificial intelligence (AI) in clinical settings.

Objectives

This study examines the attitudes of women, both current and future users of breast screening, towards the use of AI in mammogram reading.

Methods

We used a cross-sectional, mixed methods study design with data from the survey responses and focus groups. We researched in four National Health Service hospitals in England. There we approached female workers over the age of 18 years and their immediate friends and family. We collected 4096 responses.

Results

Through descriptive statistical analysis, we learnt that women of screening age (≥50 years) were less likely than women under screening age to use technology apps for healthcare advice (likelihood ratio=0.85, 95% CI 0.82 to 0.89, p<0.001). They were also less likely than women under screening age to agree that AI can have a positive effect on society (likelihood ratio=0.89, 95% CI 0.84 to 0.95, p<0.001). However, they were more likely to feel positive about AI used to read mammograms (likelihood ratio=1.09, 95% CI 1.02 to 1.17, p=0.009).

Discussion and Conclusions

Women of screening age are ready to accept the use of AI in breast screening but are less likely to use other AI-based health applications. A large number of women are undecided, or had mixed views, about the use of AI generally and they remain to be convinced that it can be trusted.

Etiology of Burnout in Canadian Radiologists and Trainees

PURPOSE

There is worsening of burnout symptoms experienced by radiologists and trainees. We explored potential factors that exacerbate burnout symptoms observed in the Canadian radiological community and currently available protective factors as next steps for establishing viable solutions for burnout.

METHODS

An 11-question electronic survey was distributed to Canadian radiologists and trainees through the Canadian Association of Radiologists (CAR). Approval from a local ethics board and the CAR were obtained. The survey contained demographics-related questions as well as questions based on common risk factors for burnout. Qualitative and quantitative analyses were performed.

RESULTS

The survey was distributed to 2200 CAR members, and a response rate of 23.3% was achieved. Most radiologists experienced frequent unexpected high workload with no statistically significant difference by the type of practice. Trainees experienced a statistically significantly (P < .0001) higher frequency of on-call shifts compared to staff radiologists. A statistically significant difference (P < .0001) was observed for perceived threats to career longevity dependent on length of career. Although support mechanisms for radiology were perceived as available, survey commentary suggested inefficiency in their usage and lack of prioritization, which was a trend observed across all types of practice.

CONCLUSIONS

While there is awareness for radiology needs, changes are required at the workplace level to reduce burnout symptoms at their source. Communication between radiologists and hospital administration, as well as among radiology group members, is key to prioritize radiology needs in our imaging-driven era of health care.

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

The first use of artificial intelligence (AI) in the ER: triage not diagnosis

Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at all. The use of AI appears to show significant promise in ER triage in the present. We briefly discuss the emerging effectiveness of AI in the ER imaging setting by looking at some of the products approved by the FDA and finding their way into "practice." The FDA approval process to date has focused on applications that affect patient triage and not necessarily ones that have the computer serve as the only or final reader. We describe a select group of applications to provide the reader with a sense of the current state of AI use in the ER setting to assess neurologic, pulmonary, and musculoskeletal trauma indications. In the process, we highlight the benefits of triage staging using AI, such as accelerating diagnosis and optimizing workflow, with few downsides. The ability to triage patients and take care of acute processes such as intracranial bleed, pneumothorax, and pulmonary embolism will largely benefit the health system, improving patient care and reducing costs. These capabilities are all available now. This first wave of AI applications is not replacing radiologists. Rather, the innovative software is improving throughput, contributing to the timeliness in which radiologists can get to read abnormal scans, and possibly enhances radiologists' accuracy. As for what the future holds for the use of AI in radiology, only time will tell.