Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting

Promoting sustainability activities in clinical radiography practice and education in resource-limited countries: A discussion paper

OBJECTIVE

Urgent global action is required to combat climate change, with radiographers poised to play a significant role in reducing healthcare's environmental impact. This paper explores radiography-related activities and factors in resource-limited departments contributing to the carbon footprint and proposes strategies for mitigation. The rationale is to discuss the literature regarding these contributing factors and to raise awareness about how to promote sustainability activities in clinical radiography practice and education in resource-limited countries.

KEY FINDINGS

The radiography-related activities and factors contributing to the carbon footprint in resource-limited countries include the use of old equipment and energy inefficiency, insufficient clean energy to power equipment, long-distance commuting for radiological examinations, high film usage and waste, inadequate training and research on sustainable practices, as well as limited policies to drive support for sustainability. Addressing these issues requires a multifaceted approach. Firstly, financial assistance and partnerships are needed to adopt eco-friendly technologies and clean energy sources to power equipment, thus tackling issues related to old equipment and energy inefficiency. Transitioning to digital radiography can mitigate the environmental impact of high film usage and waste, while collaboration between governments, healthcare organisations, and international stakeholders can improve access to radiological services, reducing long-distance commuting. Additionally, promoting education programmes and research efforts in sustainability will empower radiographers with the knowledge to practice sustainably, complemented by clear policies such as green imaging practices to guide and incentivise the adoption of sustainable practices. These integrated solutions can significantly reduce the carbon footprint of radiography activities in resource-limited settings while enhancing healthcare delivery.

CONCLUSION

Radiography-related activities and factors in resource-limited departments contributing to the carbon footprint are multifaceted but can be addressed through concerted efforts.

IMPLICATIONS FOR PRACTICE

Addressing the challenges posed by old equipment, energy inefficiency, high film usage, and inadequate training through collaborative efforts and robust policy implementation is essential for promoting sustainable radiography practices in resource-limited countries. Radiographers in these countries need to be aware of these factors contributing to the carbon footprint and begin to work with the relevant stakeholders to mitigate them. Furthermore, there is a need for them to engage in education programmes and research efforts in sustainability to empower them with the right knowledge and understanding to practice sustainably.

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI

Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.

Artificial intelligence in orthopedics: A qualitative exploration of the surgeon perspective

Artificial intelligence (AI) is currently integrated into many medical services. AI is utilized in many aspects of orthopedic surgery. The scope ranges from diagnosis to complex surgery. To evaluate the perceptions, attitudes, and interests of Sudanese orthopedic surgeons regarding the different applications of AI in orthopedic surgery. This qualitative questionnaire-based study was conducted through an anonymous electronic survey using Google Forms distributed among Sudanese orthopedic surgeons. The questionnaire entailed 4 sections. The first section included the participants' demographic data. The remaining 3 sections included questions for the assessment of the perception, attitude, and interest of surgeons toward (AI). The validity and reliability of the questionnaire were tested and piloted before the final dissemination. One hundred twenty-nine surgeons responded to the questionnaires. Most respondents needed to be more aware of the basic concepts of AI. However, most respondents were aware of its use in spinal and joint replacement surgeries. Most respondents had doubts regarding the safety of (AI). However, they were highly interested in utilizing (AI) in many orthopedic surgical aspects. Orthopedic surgery is a rapidly evolving branch of surgery that involves adoption of new technologies. Therefore, orthopedic surgeons should be encouraged to enroll in research activities to generate more studies and reviews to assess the usefulness and safety of emerging technologies.

The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research

INTRODUCTION

The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of People living with disease and the Public (PPI) in research is becoming mandatory in the EU and UK. The goal of this research was to elucidate the important values for our relevant stakeholders: People with MS, Radiologists, neurologists, Registered Healthcare Practitioners and Computer Scientists concerning AI in radiology and synthesize these in an ethical matrix.

METHODS

An ethical matrix workshop co-designed with a patient expert. The workshop yielded a survey which was disseminated to the professional societies of the relevant stakeholders. Quantitative data were analysed using the Pingouin 0.53 python package. Qualitative data were examined with word frequency analysis and analysed for themes with grounded theory with a patient expert.

RESULTS

184 participants were recruited, (54, 60, 17, 12, 41 respectively). There were significant (p < 0.00001) differences in age, gender and ethnicity between groups. Key themes emerging from our results were the importance fast and accurate results, explanations over model performance and the significance of maintaining personal connections and choice. These themes were used to construct the ethical matrix.

CONCLUSION

The ethical matrix is a useful tool for PPI and stakeholder engagement with particular advantages for near-term AI in the pandemic era.

IMPLICATIONS FOR PRACTICE

We have produced an ethical matrix that allows for the inclusion of stakeholder opinion in medical AI research design.

Equity within AI systems: What can health leaders expect?

Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This article discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.

Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey

BACKGROUND

Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals.

METHODS

An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach.

RESULTS

A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation.

DISCUSSION

Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers.

CONCLUSION

Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.

A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift

Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.

Australian perspectives on artificial intelligence in medical imaging

INTRODUCTION

While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI.

METHODS

An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de-identified from commencement.

RESULTS

Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico-legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity.

CONCLUSION

Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.

Quality Assurance of a Cross-Border and Sub-Specialized Teleradiology Service

Background: The current literature discusses aspects of quality assurance (QA) and sub-specialization. However, the challenges of these topics in a teleradiology network have been less explored. In a project report, we aimed to review the development and enforcement of sub-specialized radiology at Telemedicine Clinic (TMC), one of the largest teleradiology providers in Europe, and to describe each step of its QA. Evaluation: The company-specific background was provided by the co-authors—current and former staff members of TMC. Detailed descriptions of the structures of sub-specialization and QA at TMC are provided. Exemplary quantitative evaluation of caseloads and disagreement rates of secondary reviews are illustrated. Description of Sub-specialization and Quality Assurance at TMC: Sub-specialization at TMC is divided into musculoskeletal radiology, neuroradiology, head and neck, a body, and an emergency section operating at local daytime in Europe and Australia. Quality assurance is based on a strict selection process of radiologists, specific reporting guidelines, feedback through the secondary reading of 100% of all radiology reports for new starters, and a minimum of 5% of radiology reports on a continuous basis for all other radiologists, knowledge sharing activities and ongoing training. The level of sub-specialization of each radiologist is monitored continuously on an individual basis in detail. After prospective secondary readings, the mean disagreement rate at TMC indicating at least possibly clinically relevant findings was 4% in 2021. Conclusion: With continuing and current developments in radiology in mind, the essential features of sub-specialization and innovative QA are relevant for further expansion of teleradiology services and for most radiology departments worldwide to respond to the increasing demand for value-based radiology.

Clinical placement challenges associated with radiography education in a low-resource setting: A qualitative exploration of the Ethiopian landscape

INTRODUCTION

Clinical placements (CP) are of paramount importance in the learning and the acquisition of key competencies in terms of knowledge, skill and professional attributes required for clinical radiography practice. This study explored the challenges faced by radiography students and educators in relation to clinical placement and training in Ethiopia.

METHODS

A qualitative approach using focus group discussion and interviews were used to explore the experiences of students and educators, respectively, pertaining to challenges encountered in relation to the clinical placement of students across four university affiliated hospitals. Data obtained was analysed using a structured three step framework and the coding approach employed in a thematic analysis.

RESULTS

Participants comprise of third- and fourth-year undergraduate radiography students (n = 14) and educators [academic faculty (n = 7) and clinical practice educators (n = 8)]. Four main themes were identified, which relate to deficiencies of an existing training curriculum and its implementation strategies, inadequate resource and infrastructure within the CP environments and absence of advanced training opportunities.

CONCLUSION

This research showed that there are many and varied challenges encountered by both students and educators in relation to CP and training of radiography students in Ethiopia. These challenges could potentially affect the future performance of students/practitioners and/or the appropriate application of the core clinical radiography skills and competencies in the world of work.

IMPLICATIONS FOR PRACTICE

Clinical radiography training in resource-limited settings will require urgent attention and support with modern infrastructure including simulation to augment their clinical development to acceptable standards.