Introduction

My name is Mbuya Benjamin; I had the honor to be named the valedictorian of the JKUAT 41st Graduation Ceremony held in December 2023. I hold a Bachelors of Radiography (Radiotherapy option). I am deeply fascinated with healthcare and technology; that motivated me to pursue Radiography. As I grew up, I looked up to how vital radiologists and radiographers were in diagnosing and saving lives through their expertise in imaging; that was the path I wanted to follow. At JKUAT, though challenging to navigate, I met many obstacles and opportunities that spearheaded my growth. Being the valedictorian is not only a personal success but also a bigger testament to the never-ending encouragement from my family, peers and mentors.

Academic Journey and Key Milestones

Being a Radiography student at JKUAT was intellectually stimulating and emotionally demanding at the same time. As medical technologies became more complex, mastering the coursework was quickly overshadowed by the requisite of learning how to use these complex, life-saving technologies. An early hurdle I hit was to have to make up the difference between academic work and acquiring hands-on experience through clinical placements and internships. However, these real-world experiences are some of the most rewarding experiences I have had where I could use theoretical knowledge in the clinical setting.

Besides this, I had the opportunity to embark on cutting-edge research to evaluate the implications of radiotherapy treatment for patients with head and neck tumors. This project was tremendously gratifying for the achievement and my love for innovativeness in healthcare.

My clinical placements were attended at KNH, and my internship was at Nakuru Regional Cancer Centre. This gave me invaluably diverse exposure to how radiotherapy was practiced worldwide, and the firsthand experience was exactly how things like technology make for a more accurate diagnosis and better therapy. My involvement in community outreach programmes also gave me an understanding of how radiography and healthcare services affect the community.

Advice to Aspiring Radiographers

I have adapted to sudden change by learning to make the most of the time and persevere. The more challenges and each obstacle faced helped shape the person I am today, and I discovered and appreciated what it meant to excel in practice and theory. At JKUAT I developed leadership skills through serving in various leadership capacities such as; College Representative for the Coll ege of Health Sciences, School Student Representative for the School of Biomedical Sciences and also the Chairperson for the KYGN JKUAT Chapter.

am excited to enter the profession at such a time of technology-altering healthcare. I am also happy to join a dynamic and changing field for the better. The opening of new frontiers for more accurate and efficient diagnoses, such as artificial intelligence (AI), 3D imaging, digital radiography and Advanced Radiotherapy Techniques comes through such advances. Seeing these works being adopted in major medical centres in Kenya allows us to take a step in the right direction and continue providing good quality patient care.

From what I see, the profession still needs constant reinforcement of the continuing education and professional development. Thus, healthcare technology will develop, and radiographers will need to keep up to date and ahead of the new rates of technology to be able to use these new methods correctly. Moreover, more specific radiography fields, for example, Forensic, Industrial or Veterinary Radiography still require further expertise. However, emphasis should be placed on integrating radiography education with primordial experience. Partnerships with hospitals, research institutions, and technology providers will be needed to ensure students are prepared for the rapidly changing healthcare environment. A bridge between the needs of patients and healthcare providers and the learning of future radiographers will assist in bridging the gap between academia and industry.

Stay curious and learn for those who hope to become radiographers. It can be a demanding field, but offering equally rewarding prospects. Radiographers are essential to patient care, and success in this profession depends on mastering both the technical and interpersonal sides of the profession.

Networking is another critical factor. Form relationships with your lecturers, mentors and fellow students; your peers today could be your colleagues and collaborators tomorrow. You will get new inspirations by participating in conferences, workshops, and online forums with professionals who are familiar with radiography.

Conclusion

Finally, the journey to excellence in radiography is individualized. This is about committing to becoming a master of technical skills, continuing to learn for a lifetime, and being passionate about improving the outcomes of the patients. In our role as young professionals, we have a responsibility to continue upholding the high standards of profession not only in our education but by contributing to furthering healthcare in the country and improving upon it wherever possible. I urge all those who wish to become radiographers to keep pushing for excellence, embrace new challenges, and feel a sense of pride at the profound impact we can have on the health and well-being of others.

Introduction

Artificial Intelligence (AI) uses computers to model intelligent behaviors with minimal human intervention, enabling tasks such as decision-making and prediction (Ayorinde et al., 2024). As a data-driven paradigm, AI aligns seamlessly with technology-driven fields like radiography (Hardy & Harvey, 2020). Radiographers are members of the multidisciplinary healthcare team, educated, clinically competent, and legally authorized to perform radiography, medical imaging, and radiotherapy procedures for diagnostic, therapeutic, and research purposes using ionizing radiation, sound waves, magnetically induced signals, or radioactive materials (International Society of Radiographers and Radiological Technologists, 2021).

Radiographers bridge technology and patient care by operating advanced imaging and radiotherapy equipment while ensuring patient safety, positioning them as key agents in AI integration (Akudjedu et al., 2023; Stogiannos et al., 2025). The vast patient data generated in radiography practices supports big data analytics and machine learning, driving the development of AI-driven diagnostic tools. This expertise makes radiographers essential in implementing AI to enhance diagnostic accuracy, efficiency, and overall healthcare delivery (Hardy & Harvey, 2020).

The Future of AI in Medical Radiography, Imaging, and Radiotherapy

The increase in the burden of lifestyle diseases, rising healthcare costs, and challenges such as pandemics, conflicts, and workforce shortages globally is driving the need for AI-driven interventions (ShiftMed Team, 2024). Therefore, the application of AI in medicine has come to revolutionize and fast-track the achievement of the quadruple aims of healthcare: to improve population health, enhance patient care, support providers, and reduce healthcare costs (Colvin, 2020). For example, AI is transforming medical imaging and radiotherapy by enhancing diagnostics, optimizing workflows, reducing errors, and improving efficiency (Bajwa, 2021).

AI tools can analyze large datasets generated in clinical areas, predict outcomes, and recommend treatments with greater accuracy (Colvin, 2020). The incorporation of AI insights in medical imaging and radiotherapy has supported better diagnosis, treatment protocols, workflow efficiency, and referral systems (Bajwa, 2021). From a radiographer’s perspective, AI applications span the entire imaging workflow, from pre-examination planning to image acquisition and post-processing and beyond (Akudjedu et al., 2023). This underscores the surge of AI applications into the radiographers’ clinical practice.

Pre-examination assessment

Radiographers play an essential role in patient care before, during, and after imaging or radiotherapy procedures. While AI cannot replace these responsibilities, it enhances efficiency by automating patient queue management, vetting referrals, and validating clinical indications against appropriate imaging modalities and techniques. Additionally, AI interacts with electronic health records to verify patient identification, streamlining workflows and improving overall radiographer efficiency.

Examination planning

AI optimizes patient parameters through intelligent systems, supporting personalized healthcare during imaging planning. AI aids radiographers in carrying out their roles, like patient positioning, contrast administration, and protocol selection, subsequently enhancing precision and efficiency in imaging procedures.

Image acquisition

Selecting the appropriate imaging protocol based on patient presentation, clinical questions, and the region of interest is a key responsibility of radiographers. However, protocols vary across hospitals, imaging modalities, and individual radiographers. Automating imaging protocols and dose optimization through AI software will help standardize the practices, reduce turnaround times, and enhance patient safety.

Image processing

AI enhances medical imaging by automating post-processing, improving image quality, segmenting anatomical structures, and detecting abnormalities, reducing radiographers' workload. It aids in triaging by flagging critical cases for faster review, improving workflow efficiency. Machine learning models identify disease patterns, supporting early detection and decision-making. AI-driven synthetic modality transfer enables the conversion of images between modalities (e.g., CT to MRI), thereby minimizing radiation exposure and reducing the need for repeat scans. Additionally, AI ensures consistency in image interpretation, standardizes quality control, and eliminates interpersonal variability across healthcare facilities (Mohammad et al., 2024; Colvin, 2020).

The Paradigm to Radiographers’ Perceptions of AI

Several studies have been conducted globally to explore radiographers' levels of knowledge, perspectives, and expectations regarding AI applications in radiography, imaging, and radiotherapy. According to Akudjedu et al. (2023), a lack of AI applications’ education and training among radiographers is the leading cause of poor utilization, perception, and knowledge globally. Equally, Ayorinde et al. (2024) allude that a lack of understanding of inputs and algorithms for AI and AI outputs among healthcare workers contributes to low radiographers' perception and utilization of AI. Radiographers should fully understand the benefits and risks of AI to be able to make informed choices about its integration into radiography practices. The negative perceptions of a significant section of radiographers toward AI are compounded by their suspicion that it might take or substitute their jobs (Stogiannos et al., 2025).

According to Sezgin (2023), AI is complementing radiographers by enhancing their skills rather than replacing them, leading to a paradigm shift in healthcare. As AI becomes an essential component of modern healthcare, organizations must invest in the necessary infrastructure, training, resources, and partnerships to facilitate its successful adoption and ensure equitable access for all.

Ethical Considerations in AI Use in Medical Radiography, Imaging, and Radiotherapy

AI integration in radiography, medical imaging, and radiotherapy holds great promise by enhancing, not replacing, radiographers. However, its adoption must be guided by strong ethical principles, including transparency, accountability, patient safety, data privacy, and equitable access (Sezgin, 2023). Furthermore, according to Davenport and Kalakota (2019), as smart machines begin to assist in clinical decision-making, a range of ethical implications must be carefully considered.

These include concerns around accountability for decisions made or influenced by AI, the transparency of algorithms and processes, the need for informed consent when AI tools are involved in patient care, and the protection of patient privacy and data security. Shinners et al. (2019) found that a lack of trust in AI delivering healthcare and improving patient outcomes among a section of healthcare practitioners also contributed to a negative perception of AI. Furthermore, Akudjedu et al. (2023) found that the lack of or poor AI regulatory and governance framework and alignment across different countries or geopolitical settings has affected full utilization or uptake of AI applications among professionals like radiographers.

According to Stogiannos et al. (2025), radiographers in Europe have not yet fully embraced AI integration in their practice. However, with increasing knowledge and training, the perception that AI will replace their roles is gradually diminishing. The fear of the unknown among radiographers has contributed to a slower-than-expected adoption of AI. Additionally, Ayorinde et al. (2024) note that the integration of AI in radiography faces challenges related to the implementation of innovations. A study by Mohammad et al. (2024) indicated that although there is generally a positive attitude among radiographers and radiologists toward learning AI and its integration into practice, there are barriers such as a lack of training in AI and exposure to resources, which is the greatest setback to radiology AI integration. The UK radiographers, according to Rainey et al. (2024), expressed mixed feelings about AI in radiography practice, with some feeling that AI will kill the profession, while others feel AI brings better professional prospects and synergies.

This calls for ongoing ethical oversight, institutional governance, and a commitment to uphold professional standards in the face of rapid technological advancement.

Foundational Truth

The development of AI in healthcare relies heavily on vast amounts of validated, real-world patient data, which must be treated as foundational truth for algorithm training (Brady; Davenport & Kalakota, 2019). However, this process raises significant ethical concerns, particularly around patient confidentiality and data security.

Furthermore, there is a risk of algorithmic bias when AI systems developed in one context based on specific factors such as race, gender, environment, or disease patterns are used in a different setting without proper adaptation. This can lead to unfair decisions, resulting in unequal treatment and potential harm to certain groups of patients (Geis et al., 2019). To ethically harness AI's potential, healthcare must prioritize transparency, accountability, and the protection of human rights, while resisting the misuse of radiological data for unethical or purely financial purposes.

According to Geis et al. (2019), much of AI operates in a "black box," making it essential to ensure interpretability (the ability to understand how AI systems reach decisions), explainability (the ability to communicate these decisions to non-experts), and transparency (the ability for third parties to review and understand the decision-making process).

Addressing these ethical challenges is essential to ensure that the integration of AI in healthcare upholds trust, equity, and professional integrity. Ensuring that AI systems are used responsibly requires clear guidelines to protect patient rights and uphold ethical standards in clinical practice. To ensure responsible integration of AI, healthcare organizations and professionals must invest in proper infrastructure, training, and oversight, prioritizing human dignity and ethical standards in all AI-supported care.

AI Ethical Considerations in Radiography and Radiotherapy

Beneficence (Do-no-harm)

The ethical integration of AI in healthcare, particularly in radiography, imaging, and radiotherapy, must be grounded in core principles such as respecting human rights and freedoms, ensuring transparency and accountability, and maintaining human control and responsibility over clinical decisions (Varkey, 2020). As AI systems inevitably impact diagnosis and treatment, it becomes essential to define clear accountability frameworks for errors or unintended consequences, ensuring that responsibility is not obscured by technological complexity.

The radiographers’ goal should be to maximize value through the ethical use of AI, prioritizing patient welfare and clinical integrity while resisting the lure of financial gain from unethical exploitation of data or AI tools (Geis et al., 2019). The Code of Conduct for Radiographers emphasizes the importance of compassion, professionalism, and ethical conduct in patient care (Health and Care Professions Council (HCPC), 2016). These human qualities, respect, dignity, empathy, effective communication, and professionalism, are essential in healthcare and cannot be replaced by AI. Radiographers play a crucial role in both diagnosing and supporting patients throughout their care journey, ensuring trust and comfort (Varkey, 2020).

Regulatory frameworks, guidelines, and policies on AI use

The Society of Radiography in Kenya (SORK) has developed various guidelines and manuals to support the professional interests of radiographers in Kenya. These resources include the SORK Constitution, the Radiographers Act No. 28 of 2022, and other policy documents that provide a framework for the practice of radiography in Kenya (www.sork.org.ke). Additionally, the Kenya National AI Strategy 2025 emphasizes the importance of creating unified legal frameworks and ethical guidelines to guide AI development and ensure governance and regulatory frameworks remain agile to accommodate evolving technologies (ICT Authority).

These efforts highlight the need for comprehensive regulatory frameworks, guidelines, and policies to support the integration of AI in radiography and imaging, ensuring responsible development and deployment of AI technologies. Radiographers have an ethical duty to understand the clinical validity of datasets used to develop AI algorithms and how these algorithms process data in clinical settings. They must also ensure that the data reflects the patient population accurately, as biases in the data can negatively affect patient care.

Ethics of data ownership and privacy

Handling AI data in healthcare is complex internationally, as different countries balance personal rights and collective social welfare in varying ways (Geis et al., 2019). While radiology and radiotherapy departments typically own the imaging and treatment data, patients still retain the legal right to a copy of their data and maintain ownership and control over their personal and sensitive information, including both medical and non-medical data.

Therefore, explicit patient consent is required for sharing or using this data to develop AI algorithms. Exploitation, mining, and misuse of patients' data for financial gain without consent borders unethical conduct (Davenport & Kalakota, 2019). Radiographers must ensure that patients' data is consented to before it is used to develop AI algorithms.

Lack of Empathy

The Code of Conduct for Radiographers emphasizes the importance of compassion, professionalism, and ethical conduct in patient care. Radiographers are expected to prioritize patient well-being, communicate clearly, and offer emotional support, especially when delivering life-changing news. These human qualities cannot be replicated by AI.

Radiographers need appropriate training to confidently lead in AI-driven clinical transformation, enhance patient care, and contribute to research and innovation in imaging and radiotherapy services. The relevant line ministry, health regulators, and professional associations such as the Society of Radiography in Kenya (SORK), in consultation with experts and technology drivers, need to integrate the regulatory frameworks, guidelines, and policies to support AI in radiography and imaging.

These frameworks should ensure responsible development and deployment of AI, focusing on accountability, transparency, fairness, and patient rights, while minimizing biases and improving patient care through strict oversight and governance.

Conclusion

AI is set to significantly impact radiography and radiotherapy, with radiographers playing a key role in integrating AI into their clinical practice. While AI will not replace radiographers, it will augment their work, particularly in enhancing efficiency, effectiveness, quality, and standardized imaging, precision, and help in triaging, ultimately reducing turnaround times and improving overall patient care experience.

Despite current uncertainty among radiographers about AI’s impact on careers and daily practice, especially in LMICs, studies have shown that useful deployment of AI transforms the radiographers' work, and hence there is a need to embrace and celebrate the technology. However, for successful adoption, AI must be regulated, integrated into systems, and supported by targeted education. Radiographers need appropriate training to confidently lead in AI-driven clinical transformation, enhance patient care, and contribute to research and innovation in imaging and radiotherapy services.

Author: Jevas Kenyanya
President Society of Radiography in Kenya (SORK)

Introduction

Current Trends in Radiology

Radiology is a cornerstone of modern healthcare, offering crucial diagnostic and therapeutic insights through imaging technologies such as X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Since Wilhelm Roentgen’s discovery of X-rays in 1895, radiology has continuously evolved, integrating technological advancements to enhance disease detection, treatment planning, and patient outcomes (Brady et al., 2020).

Advancements in radiology contribute to improved diagnostic accuracy, reduced invasive procedures, and optimized treatment strategies. Innovations such as artificial intelligence (AI), hybrid imaging, and high-resolution modalities have revolutionized the field. However, radiology faces challenges, particularly in low-resource settings where cost, infrastructure, and workforce limitations hinder accessibility. This paper explores the current trends shaping radiology, key challenges, and the future of radiology in Kenya, concluding with recommendations for stakeholders to enhance imaging services.

Hybrid Imaging Modalities

Hybrid imaging combines two or more imaging techniques to improve diagnostic accuracy. The integration of positron emission tomography (PET) with CT (PET/CT) or MRI (PET/MRI) has significantly enhanced oncologic imaging, enabling precise tumor localization and metabolic assessment. Similarly, single-photon emission computed tomography (SPECT/CT) has improved the detection of musculoskeletal and cardiovascular conditions (Kjaer et al., 2017). Hybrid imaging is particularly valuable in oncology, neurology, and cardiology, where multimodal assessment provides comprehensive disease characterization.

Artificial Intelligence (AI) in Radiology

AI has emerged as a transformative force in radiology, improving imaging interpretation, workflow automation, and predictive analytics. AI-powered algorithms assist radiologists in detecting abnormalities, reducing diagnostic errors, and expediting image analysis. Deep learning models, such as convolutional neural networks (CNNs), have demonstrated high accuracy in detecting lung nodules, breast tumors, and brain lesions (Lakhani & Sundaram, 2017). Additionally, AI applications streamline radiology workflows by automating report generation and prioritizing critical cases, enhancing efficiency and reducing workload (Hosny et al., 2018).

Advances in MRI and CT Technologies

MRI and CT technologies have seen substantial improvements in speed, resolution, and functional imaging capabilities. Ultra-high-field MRI (7 Tesla and above) offers superior soft tissue contrast and neuroimaging capabilities, enabling early detection of neurological disorders (Ladd et al., 2018). Spectral CT imaging, including dual-energy CT, enhances tissue characterization by differentiating materials based on atomic composition. Moreover, low-dose CT protocols and iterative reconstruction techniques minimize radiation exposure while maintaining image quality, addressing safety concerns associated with ionizing radiation (Brenner & Hall, 2007).

Teleradiology and Remote Imaging

Teleradiology has become increasingly relevant, particularly in regions with a shortage of radiologists. Digital transmission of medical images allows radiologists to interpret scans remotely, bridging the gap in radiology services between urban and rural areas. Cloud-based radiology platforms and picture archiving and communication systems (PACS) facilitate seamless image sharing and collaboration among healthcare providers, improving access to expert opinions (Shan et al., 2020).

Challenges in Radiology

Despite technological advancements, several challenges persist in radiology, particularly in resource-limited settings.

High Costs and Limited Accessibility

Advanced imaging modalities, such as MRI and PET/CT, require substantial capital investment, making them inaccessible in many low- and middle-income countries (LMICs). The cost of imaging equipment, maintenance, and consumables often limits the availability of radiological services, particularly in rural healthcare facilities (Kawooya, 2012). Additionally, the cost of imaging examinations may be prohibitive for many patients, exacerbating healthcare disparities.

Workforce Shortages and Training Gaps

The global shortage of radiologists remains a significant challenge, particularly in Africa. Many developing countries have a low radiologist-to-population ratio, leading to delays in imaging interpretation and diagnosis (Morris et al., 2019). Moreover, there are training gaps in emerging technologies, such as AI-assisted radiology and hybrid imaging, necessitating continuous professional development for radiologists and radiographers.

Policy and Regulatory Issues

The integration of AI in radiology raises ethical and regulatory concerns regarding data privacy, liability, and algorithm bias. The lack of standardized guidelines for AI implementation in radiology poses challenges in ensuring the safety and accuracy of AI-driven diagnoses (Langlotz, 2019). Additionally, in many developing countries, limited government investment in radiology infrastructure and workforce development hinders the expansion of imaging services.

Radiation Safety Concerns

The increasing use of ionizing radiation in diagnostic imaging raises concerns about radiation exposure, particularly for pediatric and pregnant patients. Efforts to optimize imaging protocols and implement dose-reduction techniques are crucial to minimizing radiation-related risks while maintaining diagnostic accuracy (Smith-Bindman et al., 2012).

The Future of Radiology in Kenya

Expansion of Imaging Infrastructure

Expanding imaging infrastructure, particularly in county and sub-county hospitals, will improve access to diagnostic services. Government and private sector investments in MRI, CT, and ultrasound equipment are essential for addressing disparities in imaging availability. Public-private partnerships (PPPs) can facilitate the acquisition of advanced imaging technology and ensure sustainable radiology services (Okeji et al., 2021).

AI Integration and Digital Health Solutions

Kenya has the potential to leverage AI and digital health solutions to enhance radiology services. AI-driven diagnostic tools can assist radiologists in interpreting scans more efficiently, reducing diagnostic delays. Additionally, mobile health (mHealth) applications and telemedicine platforms can improve radiology access in remote areas, enabling timely diagnosis and treatment (Mwachaka et al., 2021).

Strengthening Radiology Training and Capacity Building

To address the shortage of radiologists, Kenya must strengthen radiology training programs and expand opportunities for specialization. Collaborations between local medical schools and international radiology institutions can facilitate knowledge exchange and skill development. Incorporating AI and hybrid imaging training into radiology curricula will prepare future radiologists for emerging trends in medical imaging.

Policy and Regulatory Reforms

Kenyan policymakers should develop and implement regulations that govern AI use in radiology, ensuring ethical and legal compliance. Establishing national imaging guidelines and radiation safety protocols will standardize imaging practices across healthcare facilities. Additionally, expanding insurance coverage for radiological procedures will improve affordability and access to imaging services.

Conclusion and Call for Action

The evolution of radiology has transformed medical diagnosis and treatment, with advancements in AI, hybrid imaging, and MRI/CT technologies enhancing diagnostic accuracy and efficiency. However, challenges such as high costs, workforce shortages, and policy gaps hinder optimal radiology service delivery, particularly in resource-limited settings like Kenya.

To improve radiology services in Kenya, stakeholders should:

• Expand imaging infrastructure through government and private sector investments.
• Integrate AI and digital health solutions to optimize radiology workflows and access.
• Enhance radiology training programs to address workforce shortages and skill gaps.
• Develop regulatory frameworks to govern AI implementation and radiation safety.
• Increase funding and insurance coverage for diagnostic imaging services.

By addressing these challenges and embracing technological innovations, Kenya can advance its radiology sector, ultimately improving healthcare outcomes for its population.

Author

Author: Dr. Tima Nassir Ali Khamis

She is a Consultant Radiologist and Head of the Radiology Department at Coast General Teaching and Referral Hospital in Mombasa, Kenya. With extensive experience in diagnostic imaging, including CT, MRI, X-rays, and ultrasound, she is also skilled in image-guided procedures. She serves as a part-time lecturer at the Technical University of Mombasa, has a strong research background with published work on pediatric radiation doses, and actively participates in national and international radiology conferences. Her key interests include nuclear imaging and oncologic imaging.