Artificial Intelligence in Radiology--The State of the Future

Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice

RATIONALE AND OBJECTIVES

This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice.

MATERIALS AND METHODS

An electronically distributed cross-sectional study was conducted among radiologists and radiographers in the United Arab Emirates. The questionnaire captured the participants' demographics, qualifications, professional experience, and postgraduate training. Their knowledge, perception, organisational readiness, and challenges regarding AI integration into radiology were examined.

RESULTS

There was a significant lack of knowledge and appreciation of the integration of AI into radiology practice. Organisations are stepping toward building AI implementation strategies. The availability of appropriate training courses is the main challenge for both radiographers and radiologists.

CONCLUSION

The excitement of AI implementation into radiology practise was accompanied by a lack of knowledge and effort required to improve the user's appreciation of AI. The knowledge gap requires collaboration between educational institutes and professional bodies to develop structured training programs for radiologists and radiographers.

Artificial intelligence and the medical physics profession - A Swedish perspective

BACKGROUND

There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession.

MATERIALS AND METHODS

We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession.

RESULTS

Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low.

CONCLUSIONS

From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.

Systematic Quantification of Cell Confluence in Human Normal Oral Fibroblasts

Background: The accurate determination of cell confluence is a critical step for generating reasonable results of designed experiments in cell biological studies. However, the cell confluence of the same culture may be diversely predicted by individual researchers. Herein, we designed a systematic quantification scheme implemented on the Matlab platform, the so-called “Confluence-Viewer” program, to assist cell biologists to better determine the cell confluence. Methods: Human normal oral fibroblasts (hOFs) seeded in 10 cm culture dishes were visualized under an inverted microscope for the acquisition of cell images. The images were subjected to the cell segmentation algorithm with top-hat transformation and the Otsu thresholding technique. A regression model was built using a quadratic model and shape-preserving piecewise cubic model. Results: The cell segmentation algorithm generated a regression curve that was highly correlated with the cell confluence determined by experienced researchers. However, the correlation was low when compared to the cell confluence determined by novice students. Interestingly, the cell confluence determined by experienced researchers became more diverse when they checked the same images without a time limitation (up to 1 min). Conclusion: This tool could prevent unnecessary human-made mistakes and meaningless repeats for novice researchers working on cell-based studies in health care or cancer research.

Promises of artificial intelligence in neuroradiology: a systematic technographic review

PURPOSE

To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists.

METHODS

We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks.

RESULTS

We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks.

CONCLUSION

Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

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.