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

A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change?

INTRODUCTION

The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI.

METHODS

Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI.

RESULTS

Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography.

CONCLUSION

Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession.

IMPLICATIONS FOR PRACTICE

Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives

BACKGROUND

The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.

METHODS

A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.

RESULTS

Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.

CONCLUSIONS

This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers

INTRODUCTION

Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption.

METHODS

An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables.

RESULTS

In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers.

CONCLUSION

AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks.

IMPLICATIONS FOR PRACTICE

The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.

Graduate competencies, employability and the transnational Radiography workforce shortage: A systematic literature review of current pre-registration Radiography education and training models

INTRODUCTION

Transnational mobility of the Radiography workforce is challenged due to issues with standardisation of current education and training models which has added to the workforce shortage. To address the growing volume, scope and complexity of clinical Radiography service delivery, educational models need to be given a critical look for transnational relevance in modern times. This study aims to synthesise the characteristics of current pre-registration radiography educational programmes linked with effective knowledge, skill acquisition, and graduate employability to address the current workforce challenges through the development of newer training models.

METHODS

Using a mixed methods systematic review approach, secondary data was obtained from an EBSCOhost search involving key databases including MEDLINE, CINAHL, Academic Search Ultimate, ScienceDirect, and SCOPUS. Themes were developed following a result-based convergent data synthesis.

RESULTS

Forty articles met the predefined inclusion criteria following the study identification and screening phases. The included studies were conducted from across diverse settings including both low- and middle-income countries (LMIC) and high-income countries (HIC). Two broad themes were developed from the findings including: 1. Factors influencing graduate employability and 2) Radiography education and training programme characteristics.

CONCLUSION

The findings highlight and advocate for an innovative model for Radiography education and underscores the significance of graduates possessing multi-modality skills, varied competencies, and effective accreditation processes for training. Prioritising alignment with industry needs and holistic skill development is vital to closing the employability gap, ultimately improving graduate skills and competencies to address workforce shortage while improving patient care outcomes.

IMPLICATIONS FOR PRACTICE

Radiography training institutions should explore the development of new innovative models for multi-modality pre-registration education. This should offer adaptable routes that align seamlessly with the evolving regulatory, technological, and clinical trends.

Radiography students' perceptions of artificial intelligence in medical imaging

INTRODUCTION

Education relating to Artificial Intelligence (AI) is becoming critical to developing contemporary radiographers. This study sought to investigate the perceptions of a sample of Australian radiography students regarding AI within the context of medical imaging.

METHODS

Radiography students completed a cross-sectional online questionnaire which obtained quantitative and qualitative data relating to their perceptions and attitudes of AI within the radiographic context. Descriptive and inferential statistics were utilised, and thematic analysis was undertaken for open-text responses.

RESULTS

Responses were gathered from twenty-five participants, in their second, third and fourth year of study. Most participants demonstrated a positive attitude towards AI. Most students view AI to be an assistive tool, though the cohort was less convinced AI would increase future employment in the industry. Females were more likely to disagree that AI will increase work opportunities for the radiographer (p = 0.021), as well as those in their final year of study (p = 0.011). Perceived benefits of AI related to improved work efficiency and image quality. Negative perceptions of AI involved reduced job security, and potential impact on patient care and safety.

DISCUSSION

Students presented a multitude of positive and negative perceptions towards the role that AI may play in their future careers. Education pertaining to AI is central to transforming future clinical practice, and it is encouraging that undergraduate students are intrigued and willing to learn about AI in the radiographic context.

CONCLUSION

This study offers insight into the current perspectives of Australian radiography students on AI within medical imaging, to assist in implementation of future AI-related education in the undergraduate setting.

Towards an improved dementia care experience in clinical radiography practice: A state-of-the-art review

INTRODUCTION

The increasing global incidence rate of dementia and associated co/multimorbidity has consequently led to a rise in the number of people with dementia (PwD) requiring clinical radiography care services. This review aims to explore and integrate findings from diverse settings with a focus on the experiences of PwD and stakeholders, towards the development of a holistic approach for dementia care and management within the context of radiography services.

METHOD

An electronic search was performed across the following databases: PUBMED, CINAHL, Medline, SCOPUS, and ScienceDirect for articles published from January 2009 and June 2023. Articles were included if they fulfilled a predefined criteria mainly focused on experiences of PwD and/or other stakeholders when using the radiography services. Data obtained from the included studies were analysed using a result-based convergent synthesis.

RESULT

Eleven studies from diverse settings met the inclusion criteria. A mix of both positive and negative experiences of PwD and stakeholders were reported following visits to radiology and radiotherapy departments were highlighted across settings. The findings were themed around the need for: person-centred care, effective communication, attitudinal changes of staff, specialised and improved clinical environment and inclusion of caregivers for the care of PwD.

DISCUSSION

This study emphasise the critical importance of adopting holistic approaches to caring for PwD. This involves adopting a person-centred approach, actively involving caregivers, effective communication, and adequate training for radiographers to provide quality services, all in dementia-friendly environments.

CONCLUSION

The experiences of various stakeholders highlight the need for a more holistic approach and strategy for the care and management of PwD within the context of the radiography services. This calls for an urgent need for a comprehensive strategy that includes awareness creation of staff to enhance the quality of care and the overall experience for PwD using the radiography services.

Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal

Artificial Intelligence (AI) is increasingly influential across various sectors, including healthcare, with the potential to revolutionize clinical practice. However, risks associated with AI adoption in medicine have also been identified. Despite the general understanding that AI will impact healthcare, studies that assess the perceptions of medical doctors about AI use in medicine are still scarce. We set out to survey the medical doctors licensed to practice medicine in Portugal about the impact, advantages, and disadvantages of AI adoption in clinical practice. We designed an observational, descriptive, cross-sectional study with a quantitative approach and developed an online survey which addressed the following aspects: impact on healthcare quality of the extraction and processing of health data via AI; delegation of clinical procedures on AI tools; perception of the impact of AI in clinical practice; perceived advantages of using AI in clinical practice; perceived disadvantages of using AI in clinical practice and predisposition to adopt AI in professional activity. Our sample was also subject to demographic, professional and digital use and proficiency characterization. We obtained 1013 valid, fully answered questionnaires (sample representativeness of 99%, confidence level (p< 0.01), for the total universe of medical doctors licensed to practice in Portugal). Our results reveal that, in general terms, the medical community surveyed is optimistic about AI use in medicine and are predisposed to adopt it while still aware of some disadvantages and challenges to AI use in healthcare. Most medical doctors surveyed are also convinced that AI should be part of medical formation. These findings contribute to facilitating the professional integration of AI in medical practice in Portugal, aiding the seamless integration of AI into clinical workflows by leveraging its perceived strengths according to healthcare professionals. This study identifies challenges such as gaps in medical curricula, which hinder the adoption of AI applications due to inadequate digital health training. Due to high professional integration in the healthcare sector, particularly within the European Union, our results are also relevant for other jurisdictions and across diverse healthcare systems.

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.

Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool

Objective

The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation.

Methods

Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia.

Results

The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD)  =  0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD  =  0.70), with higher scores for nursing and those who use or do not know if they use AI.

Conclusions

The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.