The Radiographers’ Perceptions of Artificial Intelligence and Theranostics

Background: Artificial Intelligence (AI) and theranostics are transforming radiographic practice by enhancing diagnostic precision and enabling personalized treatment. However, these innovations raise concerns about professional adaptation and job security. Objectives: To investigate Nigerian radiographers’ awareness, perceptions, and adaptability regarding these technologies, with a focus on job security and professional relevance. Method: A cross-sectional survey of 349 Nigerian radiographers assessed awareness, perceptions, and adaptability regarding AI and theranostics. Data were analyzed using descriptive and inferential statistics (SPSS v25). Results: Awareness was higher for AI than theranostics: 31.2% were moderately familiar and 28.7% very familiar with AI, whereas 34.4% were not familiar with theranostics. Most respondents (64.5%) had not attended training or seminars. Regarding job security, 41.8% agreed AI and theranostics could reduce the demand for manual radiography skills, and 30.7% agreed automation might cause role displacement. Nevertheless, adaptability was promising: 51.3% expressed interest in further education or certification, and 48.1% viewed adoption as an opportunity for career growth. Key adaptation factors included mentorship (50.7%), financial support (45.6%), and curriculum integration (41.5%). Conclusion: Nigerian radiographers demonstrate moderate familiarity with AI but limited understanding of theranostics. While job security concerns persist, willingness to upskill is strong. Structured training, mentorship, and curriculum reform are essential to support professional adaptation and safeguard radiographers’ relevance in the era of AI and theranostics. Contribution: This study highlights the need for radiography professional bodies and policymakers to prioritize structured training pathways, financial incentives, and cross-disciplinary collaborations to ensure radiographers remain central to technological transitions. --------------------------------------------------------------------- Introduction Artificial intelligence (AI) and theranostics are rapidly transforming healthcare, particularly within medical imaging and radiographic practice, some of which include workflow optimization, image acquisition, diagnostic interpretation, and treatment planning (1). Recent advances in deep learning and neural networks have accelerated the integration of AI into clinical imaging, offering greater efficiency, improved accuracy, and enhanced patient safety (2). In parallel, theranostics — an approach that combines diagnostic imaging with targeted therapy — has gained increasing importance in nuclear medicine and precision oncology (3). By enabling clinicians to both detect and treat specific pathologies, theranostics exemplifies the move toward personalized medicine. Globally, radiographers’ perceptions of these innovations have been mixed. While many acknowledge AI’s capacity to reduce errors, optimize workflows, and advance radio-graphic practice, others express concerns about automation leading to job displacement (4, 5). A study in Zimbabwe found that radiographers anticipated reduced roles and altered professional practices with AI adoption (6). Similarly, Saudi Arabian radiographers largely agreed that AI would play a significant role in imaging, but cited barriers such as cost, lack of expertise, and machine reliability (7). Badera et al. (8) reported generally favorable attitudes toward AI among radiographers and radiologists but highlighted mentorship gaps as a key obstacle to adoption. These findings illustrate the global tension between optimism about technological progress and apprehension about professional security. Despite these developments, the Nigerian context remains underexplored. Although AI and theranostics are increasingly discussed in international literature, limited research has examined how Nigerian radiographers perceive these technologies or how prepared they are to adapt. Considering Nigeria’s evolving healthcare infrastructure, absence of theranostics training in most radiography curricula, and limited access to mentorship and continuing education, this knowledge gap is particularly significant. Without understanding local awareness and readiness, the professional practice in Nigeria risks falling behind in adapting to global technological shifts. This study therefore investigates Nigerian radiographers’ awareness, perceptions, and adaptability regarding AI and theranostics. Specifically, it examines their concerns about job security, willingness to upskill, and the structural and educational supports necessary for effective professional adaptation. Materials and Methods This study employed a prospective cross-sectional survey design, conducted between October 2024 and January 2025 across private and federal government hospitals in Nigeria. Ethical clearance was obtained from an institutions review board (NHREC/05/01/2008B-FWA00002458-1RB00002323). The study population consisted of licensed radiographers registered with the Radiographers Registration Board of Nigeria (RRBN). Only practicing radiographers who consented to participate were included, while other healthcare workers, academic radiographers, industrial radiographers, and radiographers unwilling to participate were excluded. A convenience sampling technique was used and data were collected using a structured, self-administered questionnaire designed in Google Forms. The instrument consisted of three sections: Section I covered demographic characteristics such as age, gender, years of practice, specialty, and geopolitical zone of practice; Section II assessed awareness and understanding of AI and theranostics; while Section III focused on perceptions of job security, willingness to adapt, and factors influencing adaptation. The questionnaire was pretested on 15 radiographers at a university teaching hospital to assess internal consistency using Cronbach’s alpha, and this yielded a coefficient of 0.79, indicating acceptable reliability. Content validity was ensured through expert review by radiography lecturers and clinical practitioners. The questionnaire link was distributed via professional WhatsApp groups, LinkedIn, email lists, and direct contacts of practicing radiographers. Participation was voluntary, and informed consent was obtained prior to completing the survey. Completed responses were exported from Google Forms into IBM SPSS version 25 for analysis. Descriptive statistics such as frequencies, percentages, means, and standard deviations were used to summarize demographic and awareness data, while inferential statistics, including cross-tabulations and mean comparisons, were applied to explore perceptions and adaptability patterns. Results were presented in tables for clarity. Anonymity was maintained by ensuring that no personal identifiers were collected, and data were stored securely for academic purposes only. Results Demographic Characteristics Of the 349 radiographers who participated in the study, majority were male (56.7%) and within the 25–34 years age group (46.1%). Most respondents had practiced for fewer than five years (61.6%), reflecting a predominantly early-career workforce. The South-East geopolitical zone contributed the highest proportion of respondents (39.5%), while diagnostic radiography was the most common specialty (79.7%) (Table 1). Awareness and Understanding of AI and Theranostics From Table 2, familiarity with AI was generally higher than with theranostics. About 31.2% reported being moderately familiar with AI and 28.7% very familiar, while only 7.4% were not familiar at all. In contrast, awareness of theranostics was low, with 34.4% reporting no familiarity and only 7.7% describing themselves as extremely familiar. Knowledge sufficiency was also limited, as 42.7% either disagreed or strongly disagreed that their current knowledge of AI and theranostics was adequate to meet future professional demands. Furthermore, 64.5% of respondents had never attended training or seminars on either technology. Perceptions of Job Security Concerns about job security were evident. More than two-fifths of respondents (41.8%) agreed that AI and theranostics could reduce the demand for manual radiography skills, while 30.7% agreed that automation might lead to role displacement. At the same time, 74.5% acknowledged that integration of these technologies would require radiographers to acquire new skills to remain relevant (Table 3). Willingness to Adapt Despite concerns, respondents expressed a strong willingness to adapt. Over half (51.3%) indicated interest in pursuing further education or certifications in AI and theranostics, while 48.1% agreed that adoption of these technologies represented an opportunity for personal growth. Although peer resistance was reported as a possible barrier, most respondents viewed adaptation positively (Table 4). Factors Influencing Adaptation In Table 5, more than half (50.7%) agreed that access to structured mentorship programs would facilitate adaptation, and 45.6% emphasized the need for financial support or incentives from employers. A further 41.5% supported the inclusion of AI and theranostics training in undergraduate curricula, while 44.4% highlighted the importance of collaboration with other healthcare professionals. However, workplace infrastructure was identified as a barrier, as only 40.1% agreed that their facilities had sufficient technological support. Discussion Radiographers in this study demonstrated higher familiarity with artificial intelligence (AI) than with theranostics, a pattern that reflects the relative availability of these technologies in Nigeria. While AI is beginning to attract interest in some private institutions, it has not yet been integrated into routine clinical practice or healthcare policy (9 – 12). By contrast, theranostics remains entirely theoretical in developing countries, with no establis