| Issue |
Radioprotection
Volume 60, Number 4, Octobre-Décembre 2025
|
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|---|---|---|
| Page(s) | 310 - 317 | |
| DOI | https://doi.org/10.1051/radiopro/2025013 | |
| Published online | 15 December 2025 | |
Article
Knowledge and perception of Moroccan onco-radiotherapists on the contribution of artificial intelligence to their practices
1
Biotechnology and Medicine (BioMed) Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
2
Faculty of Medicine and Pharmacy, Mohammed V University, Rabat, Morocco
3
National Institute of Oncology, Rabat, Morocco
4
Faculty of Science, Mohammed 1 St University, Oujda, Morocco
5
Team Health Techniques (ETechS), Research Laboratory in Health and Environmental Sciences (LabReSSE), Higher Institute of Nursing Professions and Health Techniques (ISPITS), Agadir, Morocco
* e-mail: b.amaoui@uiz.ac.ma
Received:
19
December
2024
Accepted:
14
April
2025
Introduction: The introduction of AI into medical practice increasingly evident. healthcare professionals and institutions in the sector in Morocco must support this change to optimise the expected benefits of this technology. Objective: This retrospective study aimed to assess the knowledge and perceptions of Moroccan onco-radiotherapists regarding the contribution of artificial intelligence to clinical practice. Materials and methods: A anonymised questionnaire of 19 questions distributed via email address to two participant groups: the onco-radiotherapists (G1) and the Onco-Radiotherapy Residents (G2). To compare the responses between the two participant groups, Fisher’s exact test of the statistical tool for the social sciences (SPSS version 21.0) was used. The value P < 0.05 indicates that the difference is statistically significant. Results: 60% of the G1s stated that they had moderate knowledge of AI, whereas 72% of the G2s stated that they reported limited knowledge of AI. 50% of G1s and 61.5% of G2s have not received sufficient knowledge or training to use AI technologies safely and effectively. The majority of participants believed that AI would positively impact the productivity of radiotherapy practices in image acquisition and reconstruction. In addition, most participants believed that AI techniques would enhance the quality of interventions and image reconstruction functions. Most participants were optimistic about the use of AI in radiotherapy. However, almost 40% of G1s and 46% of G2s believe AI might impact their current role. Conclusion: AI certainly brings many benefits to medical practices; nevertheless, the investment in infrastructure, workforce training, and legal frameworks are urgent measures to be taken.
Key words: artificial intelligence / onco-radiotherapist / image acquisition / image reconstruction / radiotherapy / Morocco
© B. Amaoui et al., Published by EDP Sciences 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
The artificial intelligence (AI) is increasingly being integrated into various aspects of healthcare, bringing notable improvements across domains such as the diagnosis and treatment of diseases. These technological advances have enabled better quality of care and facilitated the analysis of large volumes of patient data, which has had a enhanced diagnostic accuracy of medical diagnoses and the more effective treatment plans (Pulimamidi, 2023; Mittal and Mantri 2023;). In fact, AI has the potential to transform many aspects of healthcare (Kulkarni et al., 2020;). In this context, AI technologies can improve the ability to identify patient responses to treatments, normalise patient data to eliminate redundancies in stored data, reduce patient waiting times, target therapies to improve therapeutic outcomes, enhance advanced analytics to include predictive, descriptive, diagnostic, and prescriptive analysis, and minimise waste (Anom, 2020). Furthermore, AI has also yielded substantial benefits to the field of medical radiology, particularly in automating the quantitative assessment of complex features in medical images, which has considerably improving diagnostic precision and operational efficiency. The introduction of AI into medical imaging has led to more accurate diagnoses and a significant reduction in doctors’ workloads (Zhou et al., 2019).
In radiotherapy, AI has rapidly facilitating the complex and lengthy process of preparing a patient for treatment sessions. AI solutions can harmonize structure definitions and nomenclature, allocate tasks across clinical teams, help improve treatment techniques, accelerate the adoption of best practices in clinical routine for a greater number of patients (Warren et al., 2023), and address challenges linked to limited staffing resources (Poortmans et al., 2020). Moreover, AI is expected to impact the roles of radiation oncology professionals. Automation may replace manual tasks such as the delineation of organs at risk and even target volumes, manual treatment planning, verification of treatment position, and treatment administration. These are all procedures that, when replaced by AI, will increase efficiency and reduce the time spent on planning and treatment (Korreman et al., 2020). Therefore, the role of professionals will shift from manual tasks to the development, individualiSation, and evaluation of radiotherapy treatment (Korreman et al., 2020). However, even if AI can outperform radiologists in cancer detection, there is a need to validate these tools in real clinical settings (McKinney et al., 2020). In his book Deep Medicine, Eric Topol emphasised the limitations of AI in medicine and the importance of maintaining a central role for clinicians in decision-making (Eric Topol, 2019). Furthermore, Char et al., addressed the ethical and practical concerns of using AI in medicine, such as the risks of bias and the necessity of human oversight (Char et al., 2018).
In Morocco, several studies on the perceptions of onco-radiotherapists have been carried out, such as on radiological risks when prescribing CT scans (Amaoui et al., 2023) and also on the practices of current practices in the management of cervical cancer (Amaoui et al., 2024), but concerning the use of AI in this field remains limited. This study aimed to assess the knowledge and perceptions of Moroccan onco-radiotherapists regarding the contribution of artificial intelligence (AI) to clinical practice.
2 Materials and methods
2.1 Study population
This retrospective study was conducted between February and May 2024 that aimed to assess the knowledge and perceptions of Moroccan onco-radiotherapists regarding the contribution of artificial intelligence (AI) to clinical practice. The study population was divided into two groups: onco-radiotherapists (G1) and Onco-Radiotherapy Residents (G2).
2.2 Questionnaire
To assess the knowledge and perceptions of the two groups medical, a standardised anonymous questionnaire of 19 questions was developed with reference to the literature (Ryan et al., 2021; Hindocha et al., 2023). It was created on the platform (Google form) and then sent to the study population via their email address. The first 4 questions explored the demographic characteristics of the population studied (gender, seniority, sector and field of practice), The remaining questions covered the following areas: Areas of RT mastered by participants, participants’ knowledge of AI, use of AI in the acquisition and reconstruction of radiotherapy images, impact of AI on the productivity of radiotherapy practices in image acquisition and reconstruction, influence of AI on the quality of radiotherapy practices in image acquisition and reconstruction, and impact of AI in the field of radiotherapy.
2.3 Statistical analysis
Responses were compared using between the two groups of participants, Fisher’s exact test of the statistical tool for the social sciences (SPSS version 21.0) was used. with statistical significance set at P < 0.05 the difference is statistically significant.
3 Results
A total of 115 participants completed the questionnaire. They were distributed as follows: 50 onco-radiotherapists and 65 onco-radiotherapy residents.
3.1 Socio-professional characteristics of the study population
The socio-professional characteristics of the population who took part in our survey are summarised in Table 1. Of the participants, 26.1% were male and 73.9% female. In terms of professional experience, 65.2% had less than 5 years’ experience, 4.3% between 5 and 10 years, 17.4% between 10 and 20 years, and 13% more than 20 years. In terms of sector of activity, 34.8% worked in the public sector, 8.7% in the private sector and 56.5% in teaching hospitals.
Demographic data of participants to this study expressed as a percentage %.
3.2 Areas of RT mastered by participants
Concerning the areas of radiotherapy mastered, all the onco-radiotherapists (G1) reported proficiency in CT simulation and CT fusion/contouring, 70% were proficient in dosimetry and treatment, and only 30% were proficient in quality assurance. As for the Onco-Radiotherapy Residents (G2), all of them stated that they were proficient in CT simulation, 84.6% were proficient in CT Fusion/Contouring, 46.2% were proficient in dosimetry and treatment, and only 7.7% were proficient in quality assurance.
3.2 Participants’ knowledge of AI
The results do not show a significant association between the two groups and their knowledge of AI (p=0.075). 60% of the G1s stated that they had moderate knowledge of AI, whereas 72% of the G2s stated that they had limited knowledge of AI. In addition, 66.7% of G1s and 33.3% of G2s stated that they had obtained their knowledge of AI through self-directed learning, whereas 33.3% of G1s and 50% of G2s had acquired this knowledge through continuing education, with no significant difference between the two groups (p= 0.567). Furthermore, all G1s and G2s expressed interest in learning more about AI. In this context, 70.0% of G1s and 61.5% of G2s would like to know more about the theoretical foundations of AI, all of the two groups would like to know more about the clinical applications of AI, 70.0% of G1s and 61.5% of G2s would like to know more about the ethical implications of AI, with no significant difference between the two groups (p=1.000).
60% of G1s and 53.8% of G2s prefer face-to-face workshops as a method of acquiring AI technologies safely and effectively, although 40% of G1s and 30.8% of G2s prefer online self-directed resources on an online platform. It should also be noted that 50% of G1s and 61.5% of G2s participants indicated insufficient training to use AI technologies safely and effectively.
3.4 The use of AI in the acquisition and reconstruction of radiotherapy images
According to the Table 2, 70.0% of G1s and 69.2% of G2s said that the image acquisition and reconstruction function is partially automated at their hospitals, 50.0% of G1s and 61. 5% of G2s said that the image fusion function is also partially automated, and around 50% of both groups said that the contouring of target volumes is performed manually, and 40% of G1s and 77% of G2s say that the contouring of organs at risk is done also manually. Approximately 50% of both groups stated that the options for setting up optimisation and plan evaluation, as well as image analysis and matching, are partially automated.
Data for acquisition and reconstruction of images using AI according to the two groups (expressed in %)
3.5 The impact of AI on the productivity of radiotherapy practices in image acquisition and reconstruction
According to Table 3 the majority of G1s supported increased productivity in image intervention and reconstruction functions (70%), image fusion (70%), target volume contouring (60%), organ at risk contouring (70%), plan implementation (80%), plan optimisation and evaluation (70%), quality assurance (60%), and image analysis and matching (90%). The majority of G2s were also in favour of increased productivity in the following functions: Image Intervention and Reconstruction (77%), Image Fusion (79.2%), Target Volume Contouring (61. 5%), Contouring of organs at risk (69.2%), Plan implementation (61.5%), Plan optimisation (84.4%), Plan evaluation (69.2%) and Image analysis and matching (61.5%).
Data concerning the impact of AI on the productivity of radiotherapy practices in image acquisition and reconstruction by participant group.
3.6 The influence of AI on the quality of radiotherapy practices in terms of image acquisition and reconstruction
According to Table 4, most G1s favour an increase in quality in the Image Intervention and Reconstruction functions (90%), image fusion (90%), target volume contouring (70%), organ at risk contouring (60%), plan implementation (80%), plan optimisation (70%), plan evaluation (70%), image analysis and matching (80%). The majority of G2s also supported of an increase in productivity in the functions of intervention and image reconstruction (77%), image fusion (84.6%), contouring of target volumes (54%), contouring of organs at risk (69.2%), implementation of a plan (77%), Optimisation of a plan (77%), evaluation of a plan (69. 2%), and image analysis and matching (77%).
Data concerning the influence of AI on the quality of radiotherapy practices in terms of image acquisition and reconstruction stratified by groups.
3.7 Impact of AI on the field of radiotherapy
Regarding the perceived impact of AI on the field of radiotherapy, 70% of G1s and 84.6% of G2s are optimistic about the use of AI in radiotherapy, 70% of G1s and 84.6% of G2s think that AI will increase job satisfaction, 80% of G1s and 92.3% of G2s believe that AI will have a positive impact on the patient’s treatment pathway, 40% of G1s think AI may impact their current roles, while 53.8 of G2s think AI will have no impact on their current role. Additionally, 40% of G1s and 31% of G2s expressed a neutral stance on the introduction of AI into healthcare until the ’black box’ aspect becomes transparent.
4 Discussion
The results presented in this study underscore several important findings regarding the use and perception of artificial intelligence (AI) tools in radiotherapy, as well as the differences between experienced onco-radiotherapists (G1) and onco-radiotherapy residents (G2).
The results show that onco-radiotherapists (G1) and residents (G2) have different mastery of key steps in radiotherapy. While all G1 and G2 report proficiency in CT simulation, a significantly lower proportion of both groups report limited proficiency in QA (30% of G1 and only 7.7% of G2). This indicates that quality assurance is an under-addressed aspect in training and practice, which could have implications for treatment safety and efficacy. Enhanced training in this area seems necessary, especially for residents.
The results also show that there is no significant difference between the two groups G1 onco-radiotherapists and G2 residents in terms of knowledge of AI (p=0.075). The results reveal that 60% of G1s report moderate knowledge of AI, compared to 72% of G2s who report having little knowledge. This indicates that residents, although younger and potentially more exposed to emerging technologies, still lack structured training in AI. In addition, the majority of AI knowledge is acquired through self-study, which highlights a absence of structured continuing education. In the same context, Ryan et al., 2021 showed that only 19.6% of diagnostic radiologists and 28.36% of radiotherapists had followed formal training in AI (Ryan et al., 2021). Existing educational systems in medicine, in general, do not include AI-focused courses, and there are not enough teachers or practitioners qualified in AI technologies also limits the use of AI (Semghouli et al., 2025). These results argue in favour of a more systematic integration of AI in training courses, particularly in radiotherapy. All participants, both G1 and G2, demonstrated high interest in learning more about AI, especially its clinical applications, theories, and ethical implications. This enthusiasm is a encouraging for AI adoption of AI in radiotherapy. However, the lack of current knowledge and adequate training could hinder adoption. Preferences for face-to-face workshops (60% of G1 and 53.8% of G2) suggest that interactive and practical methods are preferred for learning AI, although a significant proportion of participants are receptive to self-directed online training. In fact, formal training would be useful with more practical applications and more teaching on how to integrate AI into clinical practice (Hindocha et al., 2023).
The results in Table 2 show that several functions in radiotherapy, such as image acquisition and reconstruction, image fusion, and plan optimisation, are partially automated. However, critical tasks such as contouring target volumes and organs at risk remain predominantly manual (50% of G1 and 77% of G2 for organs at risk contouring). This reflects an opportunity for AI to play a greater role in automating these tasks, which could improve both productivity and accuracy. Indeed, in their study, Hindocha et al., 2023 reported that 45% of respondents indicated clinical use of AI contouring clinically in their department. 16% reported that although it was not currently used, their department planned to introduce it in the next year. They added that while this was mainly for OAR contouring, respondents reported using AI for prostate, thoracic and bladder tumour contouring (Hindocha et al., 2023). earlier studies have noted that the concordance of positive results for the clinical use of AI segmentation and OARs is reassuring (Warren et al., 2023). As an increasing number of organs at risk are considered in treatment planning (due to improved imaging quality and treatment accuracy), automation will help to avoid workflow congestion, freeing up valuable time for other tasks requiring human interaction (Korreman et al., 2020).
Tables 3 and 4 reveal that the majority of participants, both G1 and G2, are in favour of increasing productivity and quality in almost all radiotherapy functions through AI. For example, 90% of G1 and 77% of G2 want improved quality in image acquisition and reconstruction. These results indicate that professionals see AI as a potential tool to streamline their workflow and improve patient outcomes. The introduction of machine learning methods in image reconstruction to replace conventional reconstruction techniques has reduced artefacts and potentially increased reconstruction quality and consistency (Kida et al., 2018). In addition, deep learning methods are being developed that can automatically and rapidly perform rigid and deformable image registration (De Vos et al., 2019). Furthermore, the use of automated methods for treatment planning that have been introduced over the last five years has shown strong potential for improving the efficiency and quality of treatment plans (Hansen et al., 2016). In terms of quality assurance, Automation and data mining can be used to optimize quality assurance schedules and to auto-detect and identify errors/deviations. (El Naqa et al., 2019). However, uncertainty persists about the impact of AI on their professional role, particularly among G1s (40% believe AI will affect their role). Participants are generally optimistic about the impact of AI in radiotherapy. A majority of G1s (70%) and G2s (84.6%) believe AI will increase job satisfaction and have a positive impact on the patient care pathway. However, a significant proportion of participants (40% of G1s and 31% of G2s) take a neutral position, waiting for the “black box” aspect of AI to become more transparent before fully adopting it. This highlights the importance of developing explainable and transparent AI systems to gain clinicians’ trust.
The survey revealed several barriers to artificial intelligence (AI) adoption in Moroccan healthcare, especially in radiation oncology. Like other African countries, Morocco lacks essential digital infrastructure such as electronic medical records, integrated databases, and reliable connections between hospital departments (Eric Naab Manson et al., 2023). Staff must manually gather data from paper records, which hampers historical research and AI development (Hassan Abdelilah Tafenzi et al., 2025). The country has no publicly available AI models that use biomarkers or common imaging data (MRI, CT) required for AI training and testing. Financial and technical constraints, including limited cloud storage and insufficient computing resources, also restrict broader AI implementation. Healthcare professionals have few training opportunities, with many radiation oncologists and residents lacking necessary AI knowledge and experience to use these tools effectively. Additional concerns include questions about AI system reliability and accuracy. Clinicians are also concerned that AI might undermine their professional autonomy and expertise, while creating potential ethical and legal issues.
The introduction of AI methods into medical practice in Morocco is remains at an early stage. The absence of structured patient data and limited training in AI continue to be major barriers. Therefore, ongoing research is essential on the subject, respond to concerns and expectations of healthcare professionals, and advocate for the benefits of AI in the healthcare sector (Sami et al., 2023). In addition, it is necessary to identify practical solutions to the constraints linked to workforce training, data rights frameworks, local equipment, infrastructure, and national regulatory frameworks (Edzie et al., 2023). Staff and students be adequately trained to prepare them to work with these new technologies (Doherty et al., 2024).
Several studies conducted in Morocco have raised the issue of the variability of radiation protection practices (Amaoui et al., 2023; El fahssi et al., 2024a) and the substantial dose variability administered to patients for equivalent CT procedures (El fahssi et al., 2023; Semghouli et al., 2024a). In fact, AI tools may offer effective approaches to this issue. In this context, AI techniques can be used at several stages of a radiotherapy protocol, particularly during CT scans for radiotherapy planning, to enhance image quality and minimise radiation exposure to the patient (McCollough and Leng 2020). AI applications are can optimise medical imaging practices, particularly CT scanning, due to superior image quality, have the potential to reduce radiation dose due to AI-driven reconstruction algorithms, and can help prevent overscanning (Eberhard and Alkadhi, 2020).
In addition, a recent study showed that most Moroccan medical physicists believe that AI solutions are expected to significantly reduce radiation exposure in the field of medical imaging in the coming years (Semghouli et al., 2025). Furthermore, multiple studies have been carried out at the national level to estimate the doses delivered to patients in conventional radiology (Douama et al., 2021; El fahssi et al., 2023; Semghouli et al., 2024b) and CT scanning (Lamrabet et al., 2017; Amaoui et al., 2019; Semghouli et al., 2022; Benamar et al., 2023; El fahssi et al., 2024b; Semghouli et al., 2024c, Khajmi et al., 2025), which provides a strong foundation for projects to develop AI tools to improve practices, reduce radiation exposure, and mitigate potential effects of ionising radiation in medical imaging.
5 Conclusion
The findings indicate that most participants report moderate to limited knowledge of AI. Although the majority are positive about the integration of AI into radiotherapy practice, significant efforts are required to overcome barriers associated with the introduction of this technology into the practices of Moroccan onco-radiotherapists.
Funding
This research did not receive any specific funding.
Conflicts of interests
The authors declare that they have no conflict of interest.
Data availability statement
The research data associated with this article are included within the article.
Ethics approval
Ethical approval was not required.
Informed consent
This article does not contain any studies involving human subjects.
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Cite this article as: Amaoui B, El Fahssi M, El Kacemi H, Zerfaoui M, Semghouli S. 2025. Knowledge and perception of Moroccan onco-radiotherapists on the contribution of artificial intelligence to their practices. Radioprotection 60(4): 310–317. https://doi.org/10.1051/radiopro/2025013
All Tables
Data for acquisition and reconstruction of images using AI according to the two groups (expressed in %)
Data concerning the impact of AI on the productivity of radiotherapy practices in image acquisition and reconstruction by participant group.
Data concerning the influence of AI on the quality of radiotherapy practices in terms of image acquisition and reconstruction stratified by groups.
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