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• v.68(3); 2021 Sep

Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study

Benard o. botwe.

1 Department of Radiography, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, Accra Ghana

William K. Antwi

Samuel arkoh, theophilus n. akudjedu.

2 Department of Medical Science & Public Health, Faculty of Health & Social Sciences, Institute of Medical Imaging & Visualisation, Bournemouth University, Poole UK

Associated Data

Section B: Attitudinal perspectives on clinical application of artificial intelligence (AI) in medical imaging.

Section C: Perspectives on impact of artificial intelligence (AI) in medical imaging.

Section D: Perspectives on factors that can affect the implementation of artificial intelligence (ai) in medical imaging.

Section E: Decision‐making in the presence of AI.

Introduction

The integration of artificial intelligence (AI) systems into medical imaging is advancing the practice and patient care. It is thought to further revolutionise the entire field in the near future. This study explored Ghanaian radiographers’ perspectives on the integration of AI into medical imaging.

A cross‐sectional online survey of registered Ghanaian radiographers was conducted within a 3‐month period (February‐April, 2020). The survey sought information relating to demography, general perspectives on AI and implementation issues. Descriptive and inferential statistics were used for data analyses.

A response rate of 64.5% (151/234) was achieved. Majority of the respondents ( n  = 122, 80.8%) agreed that AI technology is the future of medical imaging. A good number of them ( n  = 131, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. However, some expressed fears about AI‐related errors ( n  = 126, 83.4%), while others expressed concerns relating to job security ( n  = 35, 23.2%). High equipment cost, lack of knowledge and fear of cyber threats were identified as some factors hindering AI implementation in Ghana.

Conclusions

The radiographers who responded to this survey demonstrated a positive attitude towards the integration of AI into medical imaging. However, there were concerns about AI‐related errors, job displacement and salary reduction which need to be addressed. Lack of knowledge, high equipment cost and cyber threats could impede the implementation of AI in medical imaging in Ghana. These findings are likely comparable to most low resource countries and we suggest more education to promote credibility of AI in practice.

This study assessed the perspectives of radiographers on the integration of artificial intelligence (AI) into medical imaging (MI). The radiographers have a positive attitude towards the clinical application of AI in MI. However, some were concerned about AI‐related errors, job displacement and reduction of basic salary, and many perceived that the lack of knowledge, high equipment costs and cyber threat could affect the implementation of AI in MI in Ghana.

The field of medical imaging is highly reliant on technology, without which, radiographers cannot acquire diagnostic images or deliver care. 1 One of the recent emerging technological trends relates to the integration of artificial intelligence (AI) in medical imaging practice for patient care and research. 2 , 3

AI refers to the theory and development of computer systems capable of performing tasks normally requiring human intelligence, such as visual perception, speech recognition, decision‐making and language translation. 4 The concept of AI in medical imaging was envisaged in the 1960s, however, inadequate technological advancements during the period prevented any rapid progress. 5 AI in medical imaging gained more widespread recognition with the introduction of complex computer systems and development of artificial neural network systems as well as machine learning technologies in the 1980s. 5

Although image interpretation is possibly the most well‐researched task of AI in medical imaging in an attempt to improve the detection of pathologies 3 , 4 , 6 , current studies are focussed on its application beyond this scope to broadly support imaging professionals in achieving optimal results with ease. 1 , 7 , 8 , 9 , 10 , 11 Particularly, AI tools are being used as clinical decision support enhancers and supportive systems for improving imaging workflow, image acquisition, disease identification, research efficiency, radiation exposures and delivering high‐quality care. 1 , 6 , 9 A recent meta‐analysis demonstrated that the diagnostic performance of these technologies is equivalent to that of healthcare professionals. 3

Despite the aforementioned benefits, scarcity of technical expertise, data‐right frameworks, public policies and latest physical resources have impeded the adoption of AI in medical imaging in Ghana and other low‐ and middle‐income countries. 8 Notwithstanding, there are strong attempts by Governments and other non‐governmental organisations (e.g. RAD‐AID) to promote and integrate the use of AI technologies in medical imaging in relatively low resource environments. 8 For the AI systems to be well integrated in medical imaging, there would be a need for radiographers to support the integration process since they are the interface between the technology and their patients. However, limited studies exist involving radiographers and AI systems. Although some studies explored the perspectives of radiographers regarding AI, the views of the radiography workforce in resource‐limited settings such as Ghana, still remain unclear. This study was consequently prompted by this gap and therefore sought to comprehensively explore the perspectives of radiographers practicing in Ghana, on the integration of AI into diagnostic medical imaging practice in order to support policy development to enhance the AI implementation strategy for Ghana.

Ethical considerations

Ethical clearance for the study was first sought and approved by the Ethics and Protocol Review Committee of the School of Biomedical and Allied Health Science, University of Ghana. Permission was also sought from the Ghana Society of Radiographers, the professional body of radiographers in Ghana, to engage its membership for the study.

Study design, setting and sample size

A cross‐sectional survey design utilising a questionnaire was employed for this study. This design allowed the collection of the required data within a short time (3‐months). At the time of the study, there were 234 radiographers registered with the Allied Health Professions Council (the national regulatory body) to practice in Ghana. The required minimum sample size ( n  = 146) for the study was calculated using the G*Power version 3.1.9.7.

Research instrument development

The questionnaire used in the study was developed after review of relevant literature relating to AI in medical imaging. The initial questionnaire was put together by a 2‐member committee with experience in survey instrument development for radiography research. To eliminate the risk of biased responses, the questions were developed to generate acceptable positive or negative answers. This was to help the respondents to think more about their responses. The questionnaire went through several rounds of reviews before it was approved by the committee. The questionnaire had 37 items including closed‐ended questions and 5‐point Likert Scale statements (1 = strongly disagree to 5 = strongly agree). The questionnaire sought information in relation to (1) demography (6 closed‐ended questions), (2) attitudinal perspectives on clinical application of AI (6 Likert Scale statements), (3) perspectives on impact of AI in medical imaging (17 Likert Scale statements), (4) potential AI implementation issues (4 Likert Scale statements), (5) decision‐making in the presence of AI (4 Likert Scale statements) and (6). free text/open responses option for additional commentary. A panel of academics with 7‐ and 10‐years’ experience in radiography research and practice subsequently assessed and approved the questionnaire for the study (See Supporting Information for details of the questionnaire). During the questionnaire evaluation phase, assessors were given a categorical rater scale (important or not important) to rate the importance of each question/item in the study. The raters were also tasked to make recommendations to improve the questionnaires where applicable. All the questions were rated important for the achievement of the main objective of the study. However, corrections regarding typographical errors were recommended and addressed. A test‐retest analysis was conducted using the Interclass Correlation Coefficient (ICC) test to assess the reliability of the questionnaire which was considered to be acceptable (ICC score = 0.85, P  < 0.001). Subsequently, a pilot study was conducted among radiographers ( n  = 3) at the Korle Bu Teaching Hospital, Accra to further address any unforeseen issues and to ensure the questionnaire was explicit and clear. No issues and/or recommendations were made from the pilot study for changes to the questionnaire.

Data collection procedure

Google Forms (Google, Mountain View, CA) was used to host the questionnaire electronically. Participants were mainly reached via the Ghana Society of Radiographers’ official social media platforms, including WhatsApp and Facebook to maximise response. Radiographers who wanted to participate in the study but did not have access to these online platforms were emailed the questionnaires. Hard copies of the questionnaire were also handed in person to a few ( n  = 3) of radiographers who requested. The first page of the questionnaire (both electronic and hard copy) contained an introductory information sheet that explained the purpose, the risk, benefit, study duration and what AI was about to radiographers. It also explained the opportunity to withdraw from the study at any time. They were also informed the questionnaire was only opened to radiographers practicing in Ghana who consented to participate in the study. Moreover, the first page of the questionnaire required each radiographer to electronically consent their participation for access to the survey. The security features of the online portal were designed to allow single participation from a radiographer and for those who preferred hardcopies, the researchers asked specific questions to enforce single participation in the study. Once an online questionnaire was completed, it was automatically sent to the survey platform hosted by one of the researchers for collation. For the hard copies, a research team member visited and collected the completed questionnaires sealed in an envelope from the participants. These responses were copied and added to the electronically acquired data for analyses. The survey was opened for a 3‐month period (February‐April, 2020) during which colleagues’ networks were also employed to promote the study. To ensure anonymity and protection of rights, participants’ identities were not sought. Participants were automatically assigned codes in Google Forms, rather than the use of personal identification details to ensure anonymity. Data obtained were encrypted with a password for safety and confidentiality.

Data analysis

Both descriptive and inferential statistics were used to analyse the data obtained. The descriptive statistics were used to generate frequencies, percentages and means while inferential statistics were used to generate association/correlation coefficients and P ‐values. The Statistical Package for Social Sciences (SPSS) version 23 (SPSS Inc., Chicago, IL, USA) was used for data analyses. The response to rating questions/items were assigned scores (1–5) on the Likert scale, corresponding to responses (strongly agree = 5, agree = 4, neutral = 3, disagree = 2, strongly disagree = 1) and aggregate mean scores (MS) were generated for the study themes/components. Spearman correlation was used to assess the relationship between radiographers’ perspectives about AI and demographic characteristics. Mann‐Whitney U test was used to independently test the perspective variables against gender and age categories since the data variables were non‐parametric. A p ‐value of less than 0.05 was considered statistically significant. For easy presentation of results in Tables, the strongly agree and agree responses were grouped together, similarly, responses for strongly disagree and agree were grouped together. The free text/open responses were grouped into themes and their frequencies presented graphically using bar charts.

A response rate of 64.5% ( n  = 151) was obtained, comprising of 73.5% ( n  = 111) males and 26.5% ( n  = 40) females of the registered radiography workforce in Ghana during the study period. The mean age (± standard deviation) of the respondents was 33.6 ± 7.3 years. Respondents’ demographic details are presented in Table  1 . Generally, the respondents scored AI an average of 3.7 on a scale of 1–5, to suggest a very positive attitude towards the integration of this technology in medical imaging. The findings (Table  2 ) show that a good number ( n  = 122, 80.8%) of the respondents embrace AI technology as the future of medical imaging. A similarly large majority of respondents ( n  = 132, 87.4%) indicated that AI would have an overall positive impact on medical imaging practice. Others ( n  = 104, 68.8%) also indicated that AI will reduce radiation dose levels while maintaining optimal image quality (Table  3 ). Table  4 shows the respondents’ perspectives on the negative impacts of AI in medical imaging where the majority expressed fears about potential machine errors associated with the used of AI‐integrated equipment in radiography practice ( n  = 126, 83.4%). Table  5 further presents the respondents’ perspective in relation to factors that can affect AI implementation and decision‐making with AI in medical imaging. High equipment cost ( n  = 118, 78.1%), lack of knowledge ( n  = 125, 82.8%) and perceived cyber threats ( n  = 109, 72.2%) were some of the factors identified to hinder AI implementation in Ghana (Table  5 ). Figure  1 presents some themed free‐text comments provided by respondents relating to AI in medical imaging practice.

Demographic distribution of respondents.

Respondents’ attitudinal perspectives on clinical application of AI in medical imaging.

MS* = mean score out of an aggregated total of 5 on the attitudinal perspectives on AI in medical imaging. AI = artificial intelligence.

Respondents’ perspectives on the positive impact of AI in medical imaging.

MS +  = mean score out of an aggregated total of 5 on the positive impact of AI in medical imaging. AI = artificial intelligence.

Respondents’ perspectives on the negative impact of AI in medical imaging.

MS #  = mean score out of an aggregated total of 5 on the negative impact of AI in medical imaging. AI = artificial intelligence.

Perspectives on factors that can affect the AI implementation and decision‐making in medical imaging.

AI = artificial intelligence.

Responses provided by respondents in the comment section of the questionnaire on the integration of AI in medical imaging.

There was no statistically significant difference in gender in terms of attitude towards AI ( P  = 0.066), perspective on the positive impact of AI ( P  = 0.112) and perspective on the negative impact of AI ( P  = 0.449). Furthermore, the study observed no statistically significant difference between those < 40 years and ≥ 40 years in terms of their attitudes towards AI ( P  = 0.771), perspective on the positive impact of AI ( P  = 0.965) and perspective on the negative impact of AI ( P  = 0.261). Table  6 presents the results of tests of associations between respondents’ demographic characteristics and their perspectives towards AI.

Associations between respondents’ demographic characteristics their perspectives towards AI.

This is the first study that has examined the perceptions of radiographers practicing in Ghana on the potential impact of AI in medical imaging. A good response rate of 64.5% (151/234) was achieved suggesting a representative sample. This response rate was similar to that of previous studies 12 , 13 conducted with this workforce cohort that obtained 64.3% and 57.3%, respectively. In general, radiographers reported positive attitudes about the potential benefits of AI, however, concerns around AI‐related errors, cyber security, data protection and decision‐making issues were identified.

Specifically, majority of respondents (86.1%) expressed an awareness of AI as an emerging trend in the field of medical imaging with 80.8% of them embracing it as the future of the discipline. This finding is comparable to the work of Sarwar et al 14 in which majority of the respondents (80.6%) predicted full integration of AI within the next five to ten years. Generally, the respondents scored AI an average of 3.7 on a scale of 1–5, to suggest a very positive attitude towards AI in medical imaging. However, no apparent statistically significant association between respondents’ attitudinal perspectives and their demographic parameters such as age ( P  = 0.761), years of work experience ( P  = 0.938) and level of education ( P  = 0.370) was observed. Furthermore, the observed attitudinal perspectives exhibited by the respondents did not vary by gender ( p  = 0.066) or by the age categories: below 40 years and 40 years and above ( p  = 0.771). These findings suggest that the observed attitudes towards AI were independent of respondents’ demographic parameters. Contrary to these findings, Sarwar et al 14 found that those above 40 years old were more positive about AI than their counterparts below 40 years. The regional and economic backgrounds of the two groups of respondents could potentially account for the observed difference.

In relation to the positive impact of AI, majority of the participants reported that AI could be an assistive tool to ease their work as radiographers (82.2%), optimise radiation dose levels (68.8%) and have an overall positive impact in medical imaging (87.4%) in line with several other previous studies. 1 , 7 , 9 , 15 The fact that AI‐related decision support systems can accurately produce diagnostic results through triaging and flagging of abnormal images of patients 3 , 6 suggests that its integration in medical imaging is improving practice and has the capacity to help more patients without access to prompt radiological interpretation, like the rural parts of Ghana and other resource poor regions of the world. 8 This is believed to increase the levels of accuracy in diagnosing diseases in a short time and improve decision‐making on diagnostic results of patients and quality assurance in many aspects of radiography practice. In academia, AI tools are also thought to improve education in medical imaging and promote research productivity in radiology which supports the findings of Sarwar et al. 14 These assertions reflected in a very high total positive impact mean score of 4.1/5. Furthermore, we observed no statistically significant association between respondents’ perspectives on the positive impact of AI and their demographic parameters ( P ‐values > 0.05) (Table  6 ).

Despite the above benefits of AI, respondents scored the technology a mean of 2.7 on a negative impact scale ranging from 1–5 to indicate that they have concerns about it which need to be addressed. Particularly, some respondents (83.4%) were worried about the possibility of AI system errors affecting practice. However, a recent meta‐analysis has demonstrated that AI tool are reliable. 3 The dichotomy between the perspective and literature could be due to a lack of knowledge on the operations and functions of AI tools. In free text comments (Figure  1 ), some respondents ( n  = 6) further demonstrated that AI tools are a necessary evil and are prone to mistakes just like humans. Greater assurance and education on the safe use of AI is needed to help alleviate some of these concerns. Moreover, the majority of respondents (53.6%) believed that most radiologists will be negatively affected by the introduction of AI in diagnostic imaging. This is a widely held belief because image interpretation is the most well‐researched task of AI in medical imaging to find a way to quickly flag the numerous pathologies that are often encountered. 3 , 4 , 6 Sit and colleagues 16 reported that a significant number (49% of the 484 studied) of UK medical students (from 19 UK medical schools) do not consider radiology as a possible career choice due to the introduction of AI. The perception is that AI would take over the job of image interpretation. Of note, a small majority of respondents (45.7%) in this current study also thought that the integration of AI would limit the work of the radiographer; a speculative assertion which has not been presently substantiated. 6 These professionals would still be required to approve the results of AI systems as they are supporting tools and would rather create new positions and increase employment prospects in medical imaging. 9 , 17 Some respondents (38.4%) also expressed concerns that the use of AI tools could lead to unethical utilisation of patient data for unwarranted commercial purposes. This notion could stem from the fact that current AI‐driven machines require patient data for the purpose of training deep learning algorithms to automate tasks, 4 , 6 , 11 and if data ‘truthfulness’ and ethical measures are not adhered to, data of patients could be compromised. 10 Meanwhile, there were no apparent statistically significant associations between respondents’ perspective on the negative impacts of AI and demographic parameters ( P ‐values > 0.05) (Table  6 ), which implies that all radiographers would require similar training, irrespective of age or gender to alleviate some of their negative perspectives about AI.

With respect to the factors that can affect the implementation of AI in medical imaging, the majority of the respondents acknowledged that the lack of robust cyber security measures (77.5%) and knowledge on the emergence of AI technology (82.2%) in Ghana poses a significant barrier in AI implementation. Similarly, Sit and colleagues 16 reported that respondents who had received some form of education in AI felt more ready to work with these tools. This suggests that medical imaging equipment manufacturing firms and hospitals must initiate frequent organisation of workshops and conferences aimed at enlightening professionals on cyber security issues and the clinical applications of AI tools in practice. 18 In addition, 78.1% of respondents believed that the high cost of AI systems could limit its implementation in Ghana. Already, technological advancements in healthcare continues to be a challenge for Ghana’s healthcare sector 19 and many other developing countries, therefore, their assertion may be true.

As to who make decisions in the use of AI tools, the majority (73.4%) of the respondents agreed that diagnostic decision‐making should be a shared responsibility between the AI algorithm and practitioners (73.4%). In contrast, the findings from Sarwar et al 14 indicate that diagnostic decision‐making should predominantly remain a human task. 2 This could be because the AI tools are just supportive systems. 2 When respondents were asked about who should be held responsible in the event of misdiagnosis due to an error attributed to the AI tools, some believed that machine manufacturers (53%), radiological staff including radiographers‐in‐charge (23.2%) and the supervising radiologist (9.9%) should be held accountable. Greenemeier 20 argued that if an institutions’ AI is completely autonomous, the blame could be solely placed on the manufacturer when an error occurs. Otherwise, non‐autonomous AI institutions could have in place policies and guidelines which would direct the appropriate handling of the technology in order to identify the cause of the error if the guidelines were not followed by the operators. The findings relating to shared responsibility in the case of AI misdiagnosis is thought‐provoking and therefore requires attention in future studies.

One limitation of the study is that it was not reported how many of the study participants use AI in their clinical practice. Therefore, findings from this study cannot be used solely for future AI implementation strategies.

The radiographers practicing in Ghana that responded to this survey demonstrated positive attitudes about the potential benefits of AI in medical imaging. However, concerns around AI‐related errors, cyber security, data protection and decision‐making issues were identified. Lack of knowledge/technical expertise, high equipment cost and cyber threats were identified as potential barriers affecting the implementation of AI in medical imaging in Ghana. We suggest the implementation of a rigorous AI education programme modelled after that of other successful organisations to promote the credibility and adoption of AI in practice in Ghana. Future research on the educational needs of radiographers relating to AI is highly recommended to inform the radiography education and training curricula/programmes.

Conflict of Interest

The authors declare that they have no competing interests.

Supporting information

Appendix S1 Section A: Demographics.

Acknowledgement

We are thankful to our colleague radiographers who participated in this study.

J Med Radiat Sci . 68 (2021) 260–268 [ PMC free article ] [ PubMed ] [ Google Scholar ]

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Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging

Affiliation.

• 1 Department of Imaging Technology and Sonography, School of Allied Health Sciences, College of Health and Allied Health Sciences, University Cape Coast, Cape Coast, Ghana.
• PMID: 37362195
• PMCID: PMC10287516
• DOI: 10.1155/2023/4704342

Introduction: Recent advancements in technology have propelled the applications of artificial intelligence (AI) in various sectors, including healthcare. Medical imaging has benefited from AI by reducing radiation risks through algorithms used in examinations, referral protocols, and scan justification. This research work assessed the level of knowledge and awareness of 225 second- to fourth-year medical imaging students from public universities in Ghana about AI and its prospects in medical imaging.

Methods: This was a cross-sectional quantitative study design that used a closed-ended questionnaire with dichotomous questions, designed on Google Forms, and distributed to students through their various class WhatsApp platforms. Responses were entered into an Excel spreadsheet and analyzed with the Statistical Package for the Social Sciences (SPSS) software version 25.0 and Microsoft Excel 2016 version.

Results: The response rate was 80.44% (181/225), out of which 97 (53.6%) were male, 82 (45.3%) were female, and 2 (1.1%) preferred not to disclose their gender. Among these, 133 (73.5%) knew that AI had been incorporated into current imaging modalities, and 143 (79.0%) were aware of AI's emergence in medical imaging. However, only 97 (53.6%) were aware of the gradual emergence of AI in the radiography industry in Ghana. Furthermore, 160 people (88.4%) expressed an interest in learning more about AI and its applications in medical imaging. Less than one-third (32%) knew about the general basic application of AI in patient positioning and protocol selection. And nearly two-thirds (65%) either felt threatened or unsure about their job security due to the incorporation of AI technology in medical imaging equipment. Less than half (38% and 43%) of the participants acknowledged that current clinical internships helped them appreciate the role of AI in medical imaging or increase their level of knowledge in AI, respectively. Discussion . Generally, the findings indicate that medical imaging students have fair knowledge about AI and its prospects in medical imaging but lack in-depth knowledge. However, they lacked the requisite awareness of AI's emergence in radiography practice in Ghana. They also showed a lack of knowledge of some general basic applications of AI in modern imaging equipment. Additionally, they showed some level of misconception about the role AI plays in the job of the radiographer.

Conclusion: Decision-makers should implement educational policies that integrate AI education into the current medical imaging curriculum to prepare students for the future. Students should also be practically exposed to the various incorporations of AI technology in current medical imaging equipment.

Copyright © 2023 James William Ampofo et al.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Distribution of the responses to…

Distribution of the responses to knowledge about artificial intelligence in medical imaging.

Distribution of responses to questions…

Distribution of responses to questions on impact of clinical practice and curriculum.

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• Indrani B. K., Asrafi S. A study on students’ awareness towards artificial intelligence. International Journal of Advanced Science and Technology . 2019;28(19):350–356.
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Radiologic Technology

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• CURRENT ISSUE
• © 2018 American Society of Radiologic Technologists

Women in Radiography Practice in Ghana: Motivating and Demotivating Factors

• Samuel Anim-Sampong , PhD ,
• Lawrence Arthur , MSc ,
• Josephine Abena Nkansah , BSc and
• Benard Ohene Botwe , MSc

Purpose To investigate and establish reasons for the smaller but increasing population of female radiographers in Ghana, and to evaluate the motivating and demotivating contributory factors associated with women’s choice of the profession.

Methods A descriptive quantitative survey design and a purposive sampling technique were used to recruit participants from hospitals, clinics, and private imaging centers in Ghana. Forty registered female radiographers were invited to participate in the survey, and 30 participants completed and returned the questionnaires.

Results Fear of the possible effects of radiation exposure (57%) and other associated risks were identified by female radiographers as the primary reasons for the low number of women practicing radiography. Lack of training centers and lecturers (27%) and lack of books (23%) were other major demotivating factors. Job security (23%) and a desire to work in a hospital (30%) motivated their continued practice.

Discussion Most of the motivating and demotivating factors found in this study were in agreement with the literature; however, approximately 27% of the respondents observed that lack of training centers and lecturers were the main challenges encountered during their professional studies, which is inconsistent with 1 study that found that the primary detractors for radiography students included poor remuneration, radiation hazards, poor societal recognition, lifestyle of radiographers, curriculum content, lack of a professional title, and male dominance.

Conclusion The number of women practicing radiography in Ghana is increasing. The male-to-female ratio is estimated to be 3 to 1. Although fear of the biologic effects of working with radiation was demotivating, job availability and a desire to work in health facilities were motivating. The need for effective career guidance and continued education at the high school level was identified as necessary to abate fears about the risks of working with radiation and to increase the number of women practicing radiography in Ghana.

• radiography
• motivating and demotivating factors
• Received July 2, 2017.
• Accepted September 6, 2017.

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• radtech March/April 2018 vol. 89 no. 4 337-343
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Opportunities for radiographer reporting in Ghana and the potential for improved patient care

2019, Radiography

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A SYNOPSIS RADIOGRAPHERS

Nov 3, 2020

Radiographers are medical imaging and radiotherapy practitioners who:

• are professionally accountable to the patients’ physical and psychosocial wellbeing, prior to, during and following examinations or therapy;
• take an active role in justification and optimisation of medical imaging and radio therapeutic procedures
• are key-persons in radiation safety of patients and third persons in accordance with the “As Low As Reasonably Achievable (ALARA)” principle and relevant Legislation

There are two types of radiographers: Diagnostic and Therapy Radiographers.

• Diagnostic Imaging Radiographers or Medical Imaging Practitioners are responsible to perform safe and accurate imaging examinations and post processing, using a wide range of sophisticated imaging equipment and techniques. These techniques include the use of:
• very sophisticated X-ray equipment in Computed Tomography  (CT) Scanning and Fluoroscopic Imaging
• high frequency sound  waves in Medical Ultrasonography
• strong magnetic fields and radio waves in Magnetic Resonance Imaging (MRI)
• radioactive tracers in  Nuclear Medicine and
• general X-ray equipment in General Radiography
• Therapeutic Radiographers are responsible for the preparation and performance of safe and accurate high-energy radiation treatments, using a wide range of sophisticated equipment and techniques, such as:
• simulation with X-rays or magnetic fields, to target the area to be treated
• computer planning to produce a plan of the dose distribution across the area to be treated, based on the simulation
• the production of individual immobilization or beam attenuation devices
• irradiation of the tumour with external beams, or with radio-active sources
• Radiographers go through a minimum of 4years undergraduate university education which leads to the award of BSc Diagnostic Radiography or BSc Therapy Radiography with a mandatory 1year post qualification clinical internship at accredited health facility.
• Their training has both 3 basic components:
• practical Clinical work in accredited health facilities under the supervision of Clinical tutors of the Training Institutions
• Classroom course work
• They write a state post-internship examination administered by the Allied Health Professions Council
• They have to register with the Allied health Professional Council before they can practice
• The practice of Radiography is regulated by the Allied health Professional Council, established by The Health Professions Regulatory Bodies ACT, 2013 (ACT 857).
• A number of radiographers already hold post-graduate certificates, diplomas, MSc, MPHil and also PhD in various fields in diagnostic imaging and radiation therapy and are either in clinical practice or in the academia.
• They are members of the Core clinical team;
• Radiographers are in direct contact with the patient
• Radiographers perform diagnostic investigations directly on the patients at the OPD, on the wards, in the theatre and at the Accident and Emergency
• Sonographers perform ultrasound scans at the antenatal clinics
• Therapy radiographers directly treat cancer patients
• Radiographers give 24hour cover of radiologic services for Accident and Emergencies, Trauma, Ward, the Theatres and at the OPD
• Radiographers are on-call duties
• They work to working to cover night and  weekends to cover trauma patients, urgent ward patients and emergencies CT Scans
• Responsibilities of the radiographers
• As professionals, radiographers are responsible for their own actions
• They are responsible for the patient under their care: before, during and after the examination
• They are also responsible for the radiation safety of the patient and their accompanying persons
• Radiographers handle some of the most expensive hospital equipment (eg in 2015 an 1.5T MRI equipment will cost between over USD 500,000 – 1million)
• Radiographers who perform ultrasonography and Sonographers give definitive reports on their investigations
• Radiographers give findings in examinations, for example on emergency CT scans and Emergency X-rays to requesting physicians
• Radiographers who have undertaken Advance Practice radiography programme are trained to give definitive independent clinical comments on radiographs
• The training and practice of radiography is highly specialised
• Few people go into Radiography
• Average of 26 practitioners are  produced out each year by the only two training institutions:

20 Diagnostic Radiographers from SBAHS, University of Ghana

3 Therapy Radiographers from SBAHS, University of Ghana

3 Diagnostic sonographers from SAHS, KNUST

• As at 2015 there were about 250 certified radiographers nationwide to a population of about 26million giving Practitioner to inhabitant ration of one radiographer to more than 100,000 persons in Ghana!
• Attrition rate

A number of Radiographers keep leaving for greener pastures as a result of:

• low remuneration, absence of or poor conditions of service
• the scare of potential immediate or permanent negative effects of ionizing radiation
• Due to the very high population to radiographer ratio radiographers they (radiographers) tend to work many hours than the required 40hours per week
• Many facilities have Radiographers who work 24hours and 7 days in the week
• Radiographers are one group of the health practitioners who are constantly in direct contact with patients
• They are exposed to ionizing radiations in diagnostic imaging, radiotherapy and to radioactive sources in nuclear medicine.

This explains why their counterparts professionals in some establishments e.g. Ghana Atomic Energy Commission receive 40% of the basic as radiation risk allowance.

• Some of the equipment used by Radiographers employ very strong magnetic fields (1.5 Tesla etc.) and changing radiofrequency pulses.

BSc. Medical Imaging Science (Radiography)

A radiographer, also known as medical radiation technologist generally uses X-rays together with other imaging modalities to perform imaging of the human body for diagnosis or treating medical problems. The place of a radiographer in healthcare practice cannot be overemphasized. Apart from occupying an important position in the health sector, they are also well-paid professionals. They are highly skilled individuals who integrate patient history, supporting clinical data, and imaging protocols with the radiographic examination to obtain quality diagnostic results.

At Klintaps, the students offering our BSc. Imaging Technology (Radiography) programme are equipped with the necessary knowledge and skills to operate a wide range of medical imaging modalities such as:

• X-Ray Machines (both digital and conventional)
• Magnetic Resonance Imaging (MRI) Machines
• Computerized Tomography (CT) Machines
• Mammography Machines
• Fluoroscopy Machines

Our 4-year Radiography programme has been accredited by both the Ghana Tertiary Education Commission (GTEC) and the Allied Health Professions Council (AHPC) of Ghana.

Aims of Our BSc. Medical Imaging Science (Radiography) Programme:

• Providing students with an understanding of the scientific principles and practical application of imaging modalities, and assisting students to acquire the necessary knowledge and skills on how to apply them in medical practice.
• Training and giving career opportunities for students to learn the application of ionizing radiations and other non-radiation-based imaging modalities in the clinical field and to confidently and accurately generate diagnostic images thereof.
• To equip the student with the knowledge and skills to actively participate in the provision of quality health care delivery in Ghana.
• Providing a sound knowledge base in medical imaging, that enables the student to use this knowledge, and integrate underlying theoretical concepts to develop their professional practice and skills.
• To show enough flexibility for the students to understand the various disciplines of imaging and to actively develop the area of medical imaging that corresponds to their area of interest.

BSc. Medical Imaging Science (Radiography) Course Details

Here is some important information regarding the BSc. Imaging Technology course that will give you more clarity related to eligibility, program fee structure, and more.

Entry Requirements for fresh undergraduates to the BSc Medical Imaging Science (Radiography) Programme

• To qualify, the applicant must obtain passes (A1- C6) i.e., have an aggregate of 36 or better in six subjects (3 core and 3 electives).
• The core subjects and minimum grades are: (C4, C5, and C6) or better in Mathematics, English Language, and Integrated Science.
• The elective subjects should include passes in 3 science subjects, i.e., Physics, Chemistry, Biology, or Mathematics
• For mature entry and other modes of entry kindly find our full list of entry  requirements here

Fee Structure for the BSc. Medical Imaging Science programme

Fee Structure For Ghanaian Students (all fees are quoted per annum)

Fee Structure For Foreign Students (all fees are quoted per annum)

Take advantage of our installment payment plans which are  detailed here

Different Industries Where Radiographers Can Work

Diagnostic radiographers serve diverse healthcare needs and work in close collaboration with a wide range of healthcare professionals in a variety of settings including

• Academic Institutions: Universities, Polyclinics, Nursing Training Colleges
• Ghana Health Services, Hospitals, Polyclinics
• Private Health Institutions
• Research Institutions
• Various Industries
• Private practice

Career Outlook for Radiographers

Overall employment of radiographers is projected to grow 9 percent from 2020 to 2030, about as fast as the average for all occupations. Also, about 20,800 openings for radiographers are projected each year, on average, over the decade. Also, data shows that there are just about 300 radiographers in the whole of Ghana for a population of thirty-one (31) million. This shows that there are so many opportunities for Radiography graduates and the demand for these professionals is on the rise.

After completing an educational program at Klintaps College, the students can make a good career in the radiography field. It is one of the booming allied health niches in which professionals and practitioners are paid a good salary, right from the start of their careers. As long as radiographers keep gathering more experience and skills, their financial and professional growth is ensured.

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Course type

Qualification, university name, phd degrees in diagnostic imaging.

11 degrees at 9 universities in the UK.

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Select the start date, qualification, and how you want to study

About Postgraduate Diagnostic Imaging

Diagnostic imaging is a branch of healthcare technology which uses a variety of machines and methods to let doctors look inside the body to diagnose medical issues and prescribe treatments. It’s an important area of medicine as it allows for fast, non-invasive diagnosis and monitoring of health conditions and there is a range of imagine technologies to suit a diversity of use-cases, such as X-rays, CT scans, MRI technology and ultrasound.

PhD courses represent the highest formal academic level of study in this field and contain a significant research component. Applicants are generally expected to hold a minimum 2:1 undergraduate degree in a related medical or biological sciences subject area for entry to a PhD programme. Courses last two to four years full-time or can be studied part-time with a typical duration of four to six years. There are nine such courses available in the UK and provide strong preparation for roles as researchers, educators and advanced practitioners in the field of diagnostic imaging.

What to Expect

A diagnostic imaging PhD programme involves advanced research in medical imaging technologies, such as radiography, CT scans and MRI. Students conduct in-depth research on imaging innovations, diagnostic accuracy and patient outcomes and you can expect to be supervised by leading experts in both diagnostic and therapeutic radiography. Universities which run PhD courses generally have very strong connections with national and international patient groups, research centres and professional bodies.

Trans-disciplinary collaboration with industry partners and major teaching hospitals is also a regular feature of research degrees like this and after graduation, you’ll be ready to take on work at the very highest level of the professional field of diagnostic imaging.

Related subjects:

• PhD Diagnostic Imaging
• PhD Audiology
• PhD Biomedical Engineering
• PhD Cardiovascular Medicine
• PhD Dental Health Education
• PhD Dental Hygiene
• PhD Dental Technology
• PhD Dentistry
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• PhD Emergency First Aid
• PhD Endocrinology
• PhD Endodontics
• PhD Epidemiology
• PhD Forensic Medicine
• PhD Gastroenterology
• PhD Geriatric Medical Studies
• PhD Haematology
• PhD Immunology
• PhD Medical Radiography
• PhD Medical Radiology
• PhD Medical Sciences
• PhD Medical Statistics
• PhD Medical Technology
• PhD Neurology
• PhD Obstetrics
• PhD Oncology
• PhD Ophthalmology
• PhD Optometry
• PhD Orthodontics
• PhD Orthopedics
• PhD Paramedical Services and Supplementary Medicine
• PhD Paramedical Work
• PhD Parenting and Carers
• PhD Pathology
• PhD Pediatrics
• PhD People with Disabilities: Skills and Facilities
• PhD Personal Health and Fitness
• PhD Pharmacology
• PhD Pharmacy
• PhD Prosthetics
• PhD Prosthodontics
• PhD Psychiatry
• PhD Psychoanalysis
• PhD Radiotherapy
• PhD Respiratory & Chest Diseases
• PhD Rheumatology
• PhD Sports Medicine
• PhD Surgery
• PhD Surgery, Medicine and Dentistry
• PhD Women's Health

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Cardiovascular Sciences PhD,MPhil - Biomarkers

University of leicester.

The School of Cardiovascular Sciences offers supervision for the degrees of Doctor of Philosophy (PhD) - full-time and part-time Master Read more...

• 3 years Full time degree: £4,786 per year (UK)
• 6 years Part time degree: £2,393 per year (UK)

Medical Imaging MRes and MPhil/PhD

Ucl (university college london).

In partnership with our NIHR Biomedical Research Centres and Unit, PhD projects will be strongly multi-disciplinary, bridging the gap Read more...

• 3 years Full time degree: £6,035 per year (UK)
• 5 years Part time degree: £3,015 per year (UK)

Medical Physics and Imaging, PhD

Swansea university.

Our Medical Physics and Imaging PhD programme is available on a full-time or part-time basis, over 3 or 6 years. Do you want a career as a Read more...

• 3 years Full time degree: £4,800 per year (UK)

PhD Medical Imaging

University of exeter.

Our research based around four distinct themes • Diabetes, Cardiovascular risk and Ageing • Environment and Human Health • Health Services Read more...

• 4 years Full time degree: £4,786 per year (UK)
• 8 years Part time degree

Biomedical Imaging and Biosensing PhD

University of liverpool.

The Department of Cellular and Molecular Physiology builds on a long and prestigious history and remains a leading international centre Read more...

• 2 years Full time degree: £4,786 per year (UK)
• 4 years Part time degree: £2,393 per year (UK)

PhD/MPhil Biomedical Imaging Sciences

University of manchester.

Programme description Our PhD Biomedical Imaging Sciences programme enables you to undertake a research project that will improve Read more...

PhD in Cognitive Neuroscience and Neuroimaging

University of york.

As an internationally renowned research department we have a vibrant community of research postgraduates. We offer full-time and part-time Read more...

Neuroimaging Research MPhil/PhD

King's college london, university of london.

Neuroimaging at the IoPPN is world-renowned. The Department of Neuroimaging is embedded in the Centre for Neuroimaging Sciences, a joint Read more...

• 3 years Full time degree: £7,950 per year (UK)
• 6 years Part time degree: £3,975 per year (UK)

PhD / MPhil Imaging

Keele university.

The School of Allied Health Professions (SAHP) Research topics within SAHP are aimed at optimising technical elements of imaging Read more...

• 3 years Full time degree: £4,712 per year (UK)
• 6 years Part time degree: £2,356 per year (UK)

Biomedical Engineering & Imaging Sciences MPhil/PhD MD/(Res)

A diverse and talented group working across the whole Medtech sector, we advance research, innovation and teaching progress through our Read more...

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• 6 years Part time degree: £3,300 per year (UK)

PhD / MPhil Diagnostic Science

Specific research areas include Selected Ion Flow Tube mass spectrometry (SIFT-MS) for the trace gas analysis of breath and urine; Read more...

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