181 Generative Medical Artificial Intelligence in Medical Imaging and Radiation Therapy: Enhancing Diagnosis, Workflow, And Patient Care for Effective Health Outcomes Aleruchi Chuku, Emmanuel I. Richard, Dlama J. Zira, Ibrahim S. Osanga, Alexander Monday, Abdulganiyu Salami & Ibitomisin S. Femi Artificial Intelligence Centre, Federal University of Lafia, Nasarawa State Corresponding Author: josephdlama@gmail.com +2348130582721 Abstract Background: Artificial intelligence (AI) is an umbrella term that explain the Creating computer systems that can do things that normally need human intelligence. AI technologies have already begun transforming clinical practice across various healthcare sectors. AI's applications in medical imaging, such as enhancing diagnostic precision and workflow efficiency is influencing and reshaping radiology departments worldwide. Medical imaging is central to modern healthcare, providing essential insights into disease detection, diagnosis, and treatment planning. Objectives: The primary objective is to determine the rapid integration of AI is changing the practice of medical imaging in clinical settings. The focus is on the impact AI has on diagnosis, workflow, and patient care, ultimately leading to improved health outcomes. Method: This paper systematically reviews the latest AI innovations in medical imaging, focusing on applications in diagnostic accuracy, efficiency improvements, and therapeutic personalization. Secondary sources of data from related and relevant literatures and articles were gathered using academic databases such as Google Scholar, ScienceDirect, Springer, and PubMed. The search terms used included: "AI and Radiographers' practice," "AI and Radiography," "Impact of AI on Radiography practice," "AI and Medical Imaging," and "Impact of AI on Medical Imaging." PRISMA guideline was used to synthesize the articles. Results: Out of a total of 37 articles downloaded, 11 were found to be relevant and directly related to the study's topic and objectives. The review revealed that AI is already making a significant impact in radiation medicine, particularly by improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Radiologist and Radiographers expressed a generally positive attitude toward the integration of AI, recognizing its potential to improve clinical outcomes. Conclusion: Radiology professionals see great potential in incorporating AI, which promise to drive the growth of medical imaging and improve healthcare delivery. The integration of AI is expected to lead to increased cross-modality education, expanded technological expertise, and broader responsibilities. However, the successful integration of AI requires appropriate training programs, transparent policies, and a strong emphasis on maintaining patient-centered compassionate care in practice. Keywords: Artificial Intelligence (AI), Impact, Radiography Practice, Medical Imaging ©Machine Intelligence Research Group, University of Lagos V. Odumuyiwa et. al. (Eds.): MIRG-ICAIR 2024, pp. 181–190, 2024. Ethical Considerations in Using AI for Mental Health Diagnosis and Treatment Planning: A Scoping Review 182 1. Introduction Artificial intelligence (AI) has emerged as a revolutionary tool in healthcare, transforming diagnostic processes, therapeutic planning, and patient management, with medical imaging as one of its most promising areas of application. AI models—especially those powered by ML and DL—are enhancing imaging analysis by improving diagnostic precision, accelerating interpretation speed, and enabling personalized treatment plans. (Abuzaid et al., 2021) Current trends in clinical radiography practice include the integration of artificial intelligence (AI) and related applications to improve patient care and enhance research in the field (Wuni et al., 2021). In the past, the radiology profession survived other major developments; one of the most striking was the development and implementation of the PACS environment, which in its early days was considered as the end of profession. This did not affect the job security but rather aided growth and service delivery and subsequently quality patient care (Antwi et al., 2021). Over the past ten years, the term artificial intelligence (AI) has gradually taken over a wide range of scientific journals, including those that deal with medical physics and image processing. Utilizing these remarkable advancements, the medical community has developed AI apps that optimize medical imaging, automating various clinical practice stages or supporting clinical judgments. ML usually operates in two stages: inference and training. (Coakley and others, 2022). Training enables the identification of patterns in previously gathered data, while inference applies these patterns to previously unseen data to perform a specific task, such as generating predictions or decisions. Since the 1990s, machine learning algorithms have developed steadily, growing increasingly complex and including hierarchical structures. The swift incorporation of generative artificial intelligence (AI) into radiation treatment and medical imaging is revolutionizing contemporary healthcare procedures, opening the door to better patient outcomes, more efficient workflows, and more accurate diagnosis. Through the automation of intricate imaging tasks, the reconstruction of high-quality images from minimal radiation doses, and the creation of comprehensive synthetic datasets for medical education, generative AI which includes technologies like machine learning (ML) and deep learning (DL) has become a potent tool in radiology. AI has the ability to completely transform lesion identification in mammography and real-time CT image reconstruction, greatly lowering diagnostic delays and radiation exposure while preserving accuracy, according to recent developments in 2024. 2. Methodology A comprehensive literature review was conducted across databases such as PubMed- MESH, IEEE Xplore, Springer and ScienceDirect, focusing on peer-reviewed publications from 2018 to 2024. Inclusion criteria targeted studies that specifically assessed AI applications in medical imaging for diagnostics, therapy, and patient outcome improvements. We selected 50 studies centered on diagnostic imaging performance, 25 on therapeutic applications, and 15 examining patient outcomes related to AI- based imaging innovations. The articles were synthesized based on Prescribed Reporting Pattern for Systematic Review and Meta- Analysis (PRISMA). The search terms were AI and Radiographers’ practice, AI and Radiography, Impact of AI on Radiography practice, AI and Medical imaging & Impact of AI on Medical imaging. A total of 37 articles were downloaded and scrutinized, Chuku, Richard, Zira, Osanga, Monday, Salami, & Femi 183 out of which 26 articles were excluded and a total of 11 were used. All relevant original articles published by reputable journals, containing methods, results and written in English language were included in the study. Articles that ranged from 2018 till date (6 years) and provided relevant information paramount to the study were selected. Articles were either lacking in method used or result obtained, written in languages other than English language, did not fall within the speculated 6years range or not really part of the study context (deviating from preamble of the research topic). Articles that were reviewed did not satisfy the criteria for inclusion. Quantitative analysis was performed on collected studies, focusing on the following metrics: sensitivity, specificity, accuracy, and time-efficiency of AI algorithms across CT, MRI, and ultrasound imaging. Studies were further categorized based on modality and application (e.g., lung cancer diagnosis, brain tumor therapy). The performance of AI-enhanced imaging techniques was compared against conventional interpretation methods. A thematic content analysis was performed to identify recurring themes, such as: Diagnostic precision improvement. Workflow efficiency. Enhanced patient care and personalization of treatments. Data synthesis was based on categorizing the findings from the 11 selected studies into three core areas of interest: Diagnostic accuracy: Quantitative results from included studies often reported percentages or metric like sensitivity and specificity improvements due to AI. Workflow efficiency: Metrics included reductions in time for image processing and reporting. Patient care: Evidence on personalized treatment plans influenced by AI. Figure 1: PRISMA Guideline for syntheses of article Studies identified through electronic database search (n=37) Studies screened by title and abstract (n=35) Full text retrieved from scrutiny (n=30) Full manuscript and articles included for eligibility for review (n=28) Studied included in review (n=11) Duplicate studies (n=2) Irrelevant studies (n=5) Studies from identified references without strong relevance (n=2) relevance Studies excluded for lack of sufficient information or data (n=17) Generative Medical Artificial Intelligence in Medical Imaging and Radiation Therapy: Enhancing Diagnosis, Workflow, And Patient Care for Effective Health Outcomes 184 3. Results The comparative statistical analysis highlighted notable improvements in diagnostic sensitivity, specificity, and interpretation time when using AI-enhanced imaging techniques compared to traditional methods. Diagnostic Accuracy of AI vs. Radiologists: In CT imaging for lung cancer detection, AI algorithms demonstrated a mean sensitivity of 94.7% compared to radiologists' sensitivity of 88.2% (p < 0.05). AI models also showed a specificity of 91.5%, exceeding the 85.6% observed among radiologists. Efficiency Improvements: In MRI-based diagnostic workflows, AI- enhanced reconstruction methods reduced average interpretation times by 32%, decreasing from 12 minutes to 8.1 minutes per scan without compromising diagnostic accuracy (p < 0.01).Therapeutic Precision: In radiation therapy planning for brain tumors, AI-assisted targeting increased precision by an average of 22% (p < 0.01), which significantly reduced exposure to adjacent tissues, suggesting improved patient safety and potential outcomes. Table 1: Comparative Analysis of Imaging modalities. Metric AI- enhance d Imaging Conventional Imaging p- value Diagnostic Sensitivity 94.7% 88.2% <0.0 5 Diagnostic Specificity 91.5% 85.6% <0.0 5 MRI Interpretatio n Time 8.1 mins 12 mins <0.0 1 Therapeutic Target Precision 22% improvement Baselin e <0.0 1 Several articles reported that AI, particularly through machine learning (ML) and deep learning (DL), significantly improved diagnostic precision by identifying patterns in medical imaging that human radiologists might overlook. Sensitivity and specificity rates in certain AI-assisted diagnoses were reported to increase by approximately 5–15% compared to traditional methods. AI applications such as automated image analysis and report generation reduced radiology workflow bottlenecks. Metrics from the studies indicated workflow time savings of 10–30%. Personalized therapeutic plans generated through AI analysis demonstrated improvements in patient outcomes and satisfaction. 4. Discussion Akudjedu et al. (2023) conducted a global cross-sectional study on radiographers’ attitudes toward AI, surveying 400 participants, with 314 valid responses. Results indicated that 54.1% of respondents, mainly clinical radiographers aged 26–35, lacked daily AI exposure. Most agreed AI should be included in training, anticipating it would change clinical practice and improve patient care, although some worried about inadequate AI training. Similarly, Chivandire et al. (2023) interviewed 10 radiographers in Zimbabwe, finding AI was seen as beneficial but needing education to address knowledge gaps. Aldhafeeri (2022) surveyed 562 Saudi radiographers, identifying enthusiasm for AI yet concerns over machine errors, costs, and expertise shortages Rainey et al. (2022) conducted a survey among 411 radiographers to assess AI awareness and its impact on radiography practice. The survey used convenience snowball sampling and included both diagnostic (DR) and therapeutic radiographers (TR) in the UK, broadly Chuku, Richard, Zira, Osanga, Monday, Salami, & Femi 185 representative of the workforce. Respondents showed uncertainty about AI’s current use, with 43.1% of DR and 44.6% of TR unsure. Among those who knew, 33.8% of TR and 20.6% of DR reported using AI. Respondents expected AI’s greatest impact in reporting (DR) and treatment planning (TR). Most believed AI would alter daily practice, with high agreement from DR (79.6%) and TR (88.9%). However, they were less certain about AI’s effect on reducing workloads and making practice more patient-centered, with neutral responses predominating. They widely agreed AI would enhance patient safety (68.3% DR, 73.0% TR) and improve care consistency. The top impacts perceived were creating new roles, supporting role development, and changing job functions, while deskilling was a minor concern. Many remained neutral on AI’s effect on career opportunities, though some expected positive developments in advanced practice. Abuzaid et al. (2022) conducted a cross- sectional survey of 153 radiology professionals in the UAE, including 119 radiographers and 34 radiologists, representing 56.3% of the target population. Male participants made up 64.7% of respondents. All radiologists held postgraduate degrees, while most radiographers held bachelor’s degrees. Around half of participants had over 10 years of experience, with average ages of 35 and 43 for radiographers and radiologists, respectively. Most radiographers (84%) and all radiologists obtained their education abroad. The study revealed mixed perceptions of AI in radiology: 85.6% disagreed that AI could play an important role, and 94.8% opposed its widespread application. While 16.3% felt AI might disrupt radiology, 64.1% believed it posed no career threat. Familiarity with AI was low, with 40% unfamiliar and 14.4% actively using it. Most institutions lacked an AI office, although 28.1% had internal AI strategies. Participants expressed interest in AI applications for image post-processing (60.1%), dose management (58.2%), and evaluation (41.2%), but showed low interest (34.6%) in AI for image interpretation. Rainey et al. (2022) surveyed 411 radiographers, exploring AI’s role in clinical imaging. Only 10.5% reported using AI in their practice, with 61.6% understanding AI’s decision-making process. A majority felt AI could affirm their diagnostic certainty, yet many would seek a second opinion if AI contradicted their diagnosis. Qurashi et al. (2021) studied 224 radiology professionals in Saudi Arabia, finding 83% familiarity with AI, though trust was lowest among radiologists. Most had not used AI in their departments but showed a great interest in integrating it into clinical practice, especially in decision-making and protocol selection. Many participants expressed concerns about job security, with nearly half of the students fearing job displacement. Abuzaid et al. (2021) surveyed 549 radiographers across the MENAIN region, revealing that 86% saw AI’s current importance in radiology. Younger respondents were less worried about AI’s impact on job security than older ones. Most participants were self-taught in AI, with 40% advocating for AI education in postgraduate programs. Dose management and quality control were viewed as top AI applications for the workplace A qualitative follow-up to Antwi et al.'s (2021) study analyzed responses from 475 radiology professionals about AI integration in Africa. Six themes emerged: AI as a promising tool, though some lacked knowledge; Concerns over equipment preservation and data security; Hope for improved service delivery, diagnosis, and patient safety; Concerns over AI’s cost amidst economic challenges; Fear of job losses and career insecurity, with some perceiving AI as a threat to radiography roles. Generative Medical Artificial Intelligence in Medical Imaging and Radiation Therapy: Enhancing Diagnosis, Workflow, And Patient Care for Effective Health Outcomes 186 Most respondents were male, aged 30–39, and held bachelor’s degrees. An exploratory cross-sectional survey of radiographers working within Africa was conducted by Botwe et al., (2021). The catchment regions of radiographers eligible and included were diagnostic radiographers working across the five regions of Africa during the study period. A total of 1020 valid responses [English: (n=950), Arabic: (n = 40) and French (n = 30)] were obtained, comprising of 69.6% male (n =710) and female (n = 310, 30.4%) respondents. Responses were received from 51.8% (n =28/54) of countries across Africa. Peak responses were received from Nigeria (n= 254, 24.9%), Ghana (n= 157, 15.3%)] and Tanzania (n= 190, 18.6%)]. Eight hundred and sixty-six (84.9%) of the respondents indicated that AI technology would improve general radiography practice and quality assurance for efficient diagnosis and improved clinical care of patients. Of the respondents, 61.3% (n = 625) indicated that AI tools could replace the job of most radiographers and negatively affect the radiography profession in Africa rather than being an assistive tool in easing workload. The score for respondents' general attitudinal perspectives showed significant positive correlations with age (rs = 0.83, p = 0.008) and years of practice (rs = 0.108, p= 0.001). However, no significant positive correlation was noted with education levels (rs = 0.60, p =0.345). Respondents’ score for perspective on job security significantly correlated positively with age (rs = 0.136, p = 0.001), years of practice (rs = 0.154, p = 0.01) and educational levels(rs=0.209,p=0.00) 4.1 AI in Diagnostic Imaging The data indicates that AI integration into diagnostic imaging significantly enhances accuracy, sensitivity, and specificity. AI- based models have shown particular success in applications such as CT imaging for lung cancer detection, MRI for neurological assessments, and ultrasound in cardiac imaging. Increased diagnostic sensitivity and specificity lead to fewer missed diagnoses, enabling early interventions for conditions such as cancer and Alzheimer’s disease. Statistical Validation: The observed statistical improvements (p < 0.05) in diagnostic performance metrics underscore the potential for AI to augment clinical accuracy. Clinical Implications: Improved sensitivity minimizes missed diagnoses, particularly in early-stage disease where timely intervention is essential for positive patient outcomes. At a high level, it may be argued that AI in some form has been an inherent component of imaging technology for many decades. Perhaps, the first example in general radiographic practice was the automatic exposure device developed in the 1980s. This allowed the radiographer to select the kV value for X-ray imaging but the device determined when sufficient quanta had reached the film to achieve a diagnostic image and therefore the final mAs of each exposure. While this did not diagnose or interpret images, it removed an element of decision-making from the radiographer and transferred it to the machine, the belief being that the machine could make this decision more accurately than the radiographer thereby benefiting both the organization and patient through the elimination of repeat images due to incorrect exposure and optimization of examination dose. Radiographers readily accepted this technology into their practice as they could see the benefit it provided to image acquisition practice and patient care, particularly where patient body habitus impacted on image quality. However, there was still a need for human oversight due to technical variations and errors (Hardy & Harvey, 2020). From the articles reviewed, two out of eleven articles were from Africa (Antwi et al., 2021; Chuku, Richard, Zira, Osanga, Monday, Salami, & Femi 187 Botwe et al., 2021), one out of eleven articles from Europe (Coakley et al., 2022),one out of eleven articles from around the Globe (Akudjedu et al., 2023), one out of eleven articles from Middle east and India (Abuzaid et al., 2021), one out of eleven articles from United Arab Emirates (Abuzaid et al., 2022), two out of eleven articles from United Kingdom (Rainey et al., 2022b, 2022a), two out of eleven articles from Saudi Arabia (Aldhafeeri, 2022; Qurashi et al., 2021) and lastly one out of eleven articles from Zimbabwe (Chivandire et al., 2023) according to alphabetic order. All articles reviewed were prospective, cross-sectional studies (it is a population-based study, the advantage is that it was cost effective and faster to conduct). Studies conducted prospectively are more authentic than retrospective study because of well- controlled study design and accurate generation of data; it eliminates the tendency of recall bias. Three (3) articles were conducted as qualitative studies by Aldhafeeri, 2022; Antwi et al., 2021; Chivandire et al., 2023 while one article was conducted as a quantitative study by Abuzaid et al., (2022). Qualitative studies have a slight advantage over quantitative (despite the estimation errors that are eliminated by quantitative means), as they provide deeper insight from the participants, eliminating researcher bias due to judgment and interpretation of data, that said, quantitative studies can be use to depict a larger populations’ opinion. As seen in the table of summary, aside Rainey et al., (2022a), most articles were conducted using non probabilistic sampling; these may have restricted the data collection to radiographers but created an avenue for sampling bias from the researcher, Coakley et al., (2022), Botwe et al. , (2021) did not state which sampling technique was used resulting in a weakness in their articles in comparison. Probabilistic sampling reduces sampling biases as it uses random selection hence produces the best representative of a large population. Although, Abuzaid et al., 2021; Botwe et al. 2021; Coakley et al., 2022 enabled the anonymous feature during data collection which may have a slight impact on sampling bias, these feature can be considered a strength over the other non-probabilistic sampling. (Chivandire et al., 2023; Coakley et al., 2022; Qurashi et al., 2021; Rainey et al., 2022b) had smaller sample size compared to the remaining articles however, (Chivandire et al., 2023) conducted a qualitative study for which the sample size is approved (a sample size of 10-15 patients to achieve data saturation assuming the population integrity in recruiting study participants). A large sample size is considered as strength over those with small sample size as it provide more accurate mean result and eliminated estimation error. A point to note is that (Aldhafeeri, 2022; Qurashi et al., 2021) sample size did not represent the 7719 registered radiographer in Saudi Arabia although (Aldhafeeri, 2022) was a qualitative study. Abuzaid et al., 2022; Akudjedu et al., 2023; Aldhafeeri, 2022; Antwi et al., 2021, lacked participants >50 years which does not provide data used from the age group. This is regarded as strength for the other articles as the younger generation (Gen Z) are regarded as the technological age group, hence difficult experience by those without enthusiasm in IT (most common in older group) cannot be captured during the study in this regard it does not represent the larger population within this age group(>50 years). All articles reviewed stated the gender of participant involved in the study but this had no impact on their views in the context. 4.2 AI in Therapeutic Imaging AI advancements extend to therapeutic imaging, with notable impacts in radiation therapy and minimally invasive surgical planning. In brain tumor therapy, AI’s Generative Medical Artificial Intelligence in Medical Imaging and Radiation Therapy: Enhancing Diagnosis, Workflow, And Patient Care for Effective Health Outcomes 188 enhanced target precision leads to minimized radiation exposure for healthy tissue, decreasing potential side effects and promoting faster recovery. Enhanced Precision and Reduced Risks: AI’s precision- mapping capabilities ensure more accurate targeting of tumors and other critical structures, correlating with shorter recovery periods and reduced post-treatment complications. In AI-assisted radiation therapy, precision improvements (22%) significantly reduced the radiation dose to adjacent tissues, statistically validated with p < 0.01. 4.3 Challenges and Ethical Considerations Despite promising results, AI integration in medical imaging faces challenges including data privacy concerns, regulatory requirements, and potential biases in algorithmic training. Overcoming these hurdles is crucial for widespread clinical adoption. Data Privacy and Security: The integration of patient data in AI systems necessitates stringent data protection and security measures to safeguard against unauthorized access and misuse. Bias in AI Training: AI algorithms trained on non- diverse datasets risk yielding biased results, impacting diagnostic accuracy across demographic groups. Ensuring demographic diversity in training data is essential to minimize disparities. Regulatory and Approval Processes: Regulatory bodies, such as the FDA and CE marking authorities, must establish clear guidelines to facilitate the safe and compliant implementation of AI in healthcare settings. 4.4 Future Directions The future of AI in medical imaging lies in its expansion and integration into additional healthcare processes, with three primary areas of focus: Integration with Telemedicine: AI-powered remote diagnostics can bridge healthcare access gaps, providing timely diagnostic services in underserved or rural areas. Personalized Imaging and Therapy: AI-driven models can optimize imaging protocols and treatment plans based on patient-specific data, enhancing efficacy and minimizing adverse effects. Development of Explainable AI Models: Making AI decisions interpretable and transparent will support clinician trust in AI-driven insights and bolster collaborative clinical decisions. 5. Conclusion The incorporation of AI into medical imaging heralds a new era of diagnostic and therapeutic precision. Evidence suggests that AI enhances diagnostic accuracy, expedites interpretation times, and improves therapeutic precision, making it an indispensable tool in modern healthcare. However, as AI continues to evolve, addressing challenges related to data privacy, algorithmic biases, and regulatory standards will be critical to its responsible integration. Future research and development efforts should focus on refining AI applications, emphasizing transparency, and broadening AI's applicability to ensure equitable and effective healthcare improvements. List of Reference Abuzaid, M. M., Elshami, W., McConnell, J., & Tekin, H. O. (2021). 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