Radiology: Is its future bright?

Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study

BACKGROUND

The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes.

OBJECTIVE

This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers' expectations using 2 cross-sectional surveys.

METHODS

The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings.

RESULTS

Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI's ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time.

CONCLUSIONS

Despite these barriers, there was notable consistency in researchers' expectations across the 2 survey periods, indicating a stable and sustained outlook on AI's transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI's benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration.

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor

Background

In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images.

Methods

A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model.

Results

The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively).

Conclusion

The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review

OBJECTIVES

Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration.

METHODS

A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions.

RESULTS

Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care.

CONCLUSIONS

Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications.

KEY POINTS

Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.

Management of abdominal emergencies in adults using telemedicine and artificial intelligence

The terms "telemedicine" and "artificial intelligence" (AI) are used today throughout all fields of medicine, with varying degrees of relevance. If telemedicine corresponds to practices currently being developed to supply a high quality response to medical provider shortages in the general provision of healthcare and to specific regional challenges. Through the possibilities of "scalability" and the "augmented physician" that it has helped to create, AI may also constitute a revolution in our practices. In the management of surgical emergencies, abdominal pain is one of the most frequent complaints of patients who present for emergency consultation, and up to 20% of patients prove to have an organic lesion that will require surgical management. In view of the very large number of patients concerned, the variety of clinical presentations, the potential seriousness of the etiological pathology that sometimes involves a life-threatening prognosis, healthcare workers responsible for these patients have logically been led to regularly rely on imaging examinations, which remain the critical key to subsequent management. Therefore, it is not surprising that articles have been published in recent years concerning the potential contributions of telemedicine (and teleradiology) to the diagnostic management of these patients, and also concerning the contribution of AI (albeit still in its infancy) to aid in diagnosis and treatment, including surgery. This review article presents the existing data and proposes a collaborative vision of an optimized patient pathway, giving medical meaning to the use of these tools.

3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future

Three-dimensional (3D) imaging and post processing are common tasks used daily in many disciplines. The purpose of this article is to review the new postprocessing tools available. Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and relevant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies. Finally, more traditional techniques, such as minimum intensity projection, might find new applications for bone evaluation with the advent of ultra-high-resolution CT scanners. These tools can be used synergistically to provide morphologic, topographic and functional information and increase the versatility of CT.

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

PURPOSE

The purpose of this study was to assess the perception, knowledge, wishes and expectations of a sample of French radiologists towards the rise of artificial intelligence (AI) in radiology.

MATERIAL AND METHOD

A general data protection regulation-compliant electronic survey was sent by e-mail to the 617 radiologists registered in the French departments of Nord and Pas-de-Calais (93 radiology residents and 524 senior radiologists), from both public and private institutions. The survey included 42 questions focusing on AI in radiology, and data were collected between January 16^th and January 31^st, 2019. The answers were analyzed together by a senior radiologist and a radiology resident.

RESULTS

A total of 70 radiology residents and 200 senior radiologists participated to the survey, which corresponded to a response rate of 43.8% (270/617). One hundred ninety-eight radiologists (198/270; 73.3%) estimated they had received insufficient previous information on AI. Two hundred and fifty-five respondents (255/270; 94.4%) would consider attending a generic continuous medical education in this field and 187 (187/270; 69.3%) a technically advanced training on AI. Two hundred and fourteen respondents (214/270; 79.3%) thought that AI will have a positive impact on their future practice. The highest expectations were the lowering of imaging-related medical errors (219/270; 81%), followed by the lowering of the interpretation time of each examination (201/270; 74.4%) and the increase in the time spent with patients (141/270; 52.2%).

CONCLUSION

While respondents had the feeling of receiving insufficient previous information on AI, they are willing to improve their knowledge and technical skills on this field. They share an optimistic view and think that AI will have a positive impact on their future practice. A lower risk of imaging-related medical errors and an increase in the time spent with patients are among their main expectations.

The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease

INTRODUCTION

High-resolution computed tomography (HRCT) has revolutionized the diagnosis, prognosis and in some cases the prediction of therapeutic response in interstitial lung disease (ILD). HRCT represents an essential second step to a patient's clinical history, before considering any other investigation, including lung biopsy. Areas covered: This review describes the current place of HRCT in the diagnosis, prognosis and monitoring of ILD. It also lists some perspectives for the near future. Expert commentary: Since the 1980s, HRCT and its interpretation have improved, the diagnosis value of patterns, and the integration of bio-clinical elements to HRCT have been better standardized. The interobserver agreement has been investigated, allowing a better use of some limits in the interpretation of various signs. It not only takes into account one particular predominant sign, but the combination of patterns and the distribution of findings. Thanks to HRCT, the range of diagnoses and their probability are more accurately identified. The contribution of HRCT has been optimized during the multidisciplinary discussion that a difficult diagnosis calls for. HRCT quantification of the extent of diffuse lung disease becomes possible and is linked to prognosis. In the future, artificial intelligence may significantly modify the practice of radiology.