Shaping the future of cybersecurity: The convergence of AI, quantum computing, and ethical frameworks for a secure digital era

- We present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts. Specifically, we implement and analyze Quantum Support Vector Machines (QSVMs) vs classical SVMs, and Quantum Convolutional Neural Networks (QCNNs) vs classical CNNs. Using the widely used PlantVillage and complementary field datasets, we describe image preprocessing, classical baseline architectures, quantum data-encoding strategies, circuit-level QSVM and QCNN designs for near-term quantum devices, and hybrid training procedures. Where possible, we review literature-reported performance and propose a reproducible experimental pipeline for empirical evaluation on simulated/noisy quantum backends. We discuss expected strengths and limitations of quantum approaches (expressivity, kernel advantages, resource constraints), provide detailed evaluation metrics and ablations, and propose directions for real-device experiments and field deployment. Key takeaways: QSVM/quantum-kernel methods can provide superior separability on certain feature maps and small-to-medium-sized datasets, while QCNNs show promise as compact feature extractors for hybrid pipelines — but both approaches currently require careful circuit design and error-mitigation to outperform well-tuned classical models in realistic field settings. Key Words: QCNN, Plant Disease, SVM, CNN, QSVM

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The proliferation of Internet of Things (IoT) devices has introduced a paradigm shift in connectivity and automation, but it has also surfaced critical cybersecurity challenges due to the heterogeneous and resource-constrained nature of these networks. Traditional and classical machine learning-based intrusion detection systems (IDS) have achieved commendable success, yet they often struggle with scalability, data dimensionality, and the detection of novel attack patterns. In this research, QML-IDS is proposed, which is a quantum machine learning-based IDS framework that leverages quantum computational advantages to enhance intrusion detection in IoT environments. Utilizing the ToN-IoT benchmark dataset designed specifically for IoT/IIoT security multiple QML models are evaluated, including Quantum Convolutional Neural Networks (Q-CNN), Quantum Support Vector Machines (QSVM), Quantum K-means Clustering, and Quantum Random Forest (Q-RF). Implemented using the Pennylane framework, proposed experiments reveal that Q-RF achieved the highest detection accuracy of 95% on a reduced training set, followed closely by Q-CNN with 94% accuracy. The results suggest that QML models hold significant promise in offering efficient and scalable security solutions for the next generation of IoT systems.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Abstract: As Artificial Intelligence (AI) advances, so do the risks associated with deepfakes, misinformation, and algorithmic bias, posing significant threats to security, privacy, democracy, and societal trust. "Securing AI: Combating Deepfakes, Misinformation, and Bias with Trustworthy Systems" provides a comprehensive analysis of AI security vulnerabilities, adversarial machine learning, AI-driven misinformation, and bias in automated decision-making. The book explores how AI-generated synthetic media, data poisoning attacks, and biased algorithms are being weaponized for cyber fraud, political manipulation, and unethical automation. It delves into defensive strategies, AI forensic tools, cryptographic AI verification, and fairness-aware machine learning techniques to combat these emerging threats. Additionally, the book examines global AI regulations, governance frameworks, and ethical deployment standards that ensure transparency, accountability, and security in AI-driven ecosystems. Through real-world case studies, technical insights, and policy recommendations, this book serves as an essential resource for AI researchers, cybersecurity professionals, policymakers, and technology leaders aiming to develop trustworthy AI systems that resist adversarial manipulation, misinformation campaigns, and algorithmic bias while fostering fair, transparent, and secure AI adoption. Keywords: AI security, adversarial machine learning, deepfake detection, AI-generated misinformation, synthetic media, bias mitigation, AI ethics, AI governance, trustworthy AI, explainable AI (XAI), fairness-aware machine learning, cryptographic AI, federated learning security, digital forensics, algorithmic bias, data poisoning attacks, model robustness, cybersecurity in AI, misinformation detection, deep learning security, AI regulatory policies, zero-trust AI, blockchain-based content verification, ethical AI deployment, secure AI frameworks, AI transparency, AI-driven cyber threats, fake news detection, AI fraud prevention.

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

Accelerated development in artificial intelligence (AI). The phrase has encouraged advancements in drug discovery and development. In this study, we probe the constraints of AI models—AlphaFold, AtomNet, and Insilico GANs—on predictive precision and cross-therapeutic generalizability. We propose HybridAI, a hybrid AI framework that combines geometric deep learning (GDL), reinforcement learning (RL), and federated learning (FL) for improved predictive modelling of drug-target interactions. They were evaluated against metrics such as ROCAUC, RMSD, and hit-rate accuracy across four therapeutic categories: oncology, antimicrobial resistance, neurodegenerative disease, and autoimmune disease. HybridAI was implemented and validated on a dataset of 150 structurally diverse compounds from ChEMBL and DrugBank. The model outperformed current AI frameworks, achieving 92 parcent accuracy in predicting drug-kinase interactions, with a 34 parcent reduction in toxicity prediction error compared to conventional ADME models. A case study involving non-small cell lung cancer (NSCLC) illustrated the in vitro applicability of Hybrid AI. The system correctly identified afatinib as a potent kinase inhibitor, with a predicted binding affinity of 89 parcent. The prediction was confirmed by molecular docking and in vitro assays within 14 days. Our findings highlight the limitations of single-purpose AI models and underscore the need for hybrid systems, such as Hybrid AI, to enhance precision, flexibility, and scalability. The research supports the use of advanced learning methodologies to facilitate personalised medicine and expedite the drug development process. By integrating various AI methods, HybridAI raises the bar for intelligent drug discovery architectures. The rapid growth of artificial intelligence (AI) in drug discovery necessitates a critical evaluation of its predictive validity and therapeutic applicability. The current study aims to compare the predictive performance of different AI-based models for predicting the success of drug therapy and to introduce a novel combinational AI method, HybridAI, to enhance predictive strength and cross-therapeutic applicability. Seven AI models, such as AlphaFold, AtomNet, and Insilico GANs, were thoroughly assessed for drug efficacy, toxicity, and binding affinity prediction in four disease areas: oncology, antimicrobial resistance, neurodegenerative disorders, and autoimmune diseases. Normalized metrics such as receiver operating characteristic (ROC-AUC), root mean square deviation (RMSD), and hit-rate accuracy were used to evaluate the models. HybridAI, a new combinational model incorporating geometric deep learning GDL, reinforcement learning RL, and federated learning FL, was tested on a 150-structurally different compound dataset that was extracted from ChEMBL and DrugBank. Comparative analysis revealed that the existing AI models are 78– 85 parcent accurate in target-specific drug design but show extreme variability (12–28 parcent) in cross-therapeutic generalizability. Hybrid AI outperformed individual models by achieving 92 parcent drug-kinase interactions (compared to 79 parcent with AlphaFold) and a 34 parcent reduction in errors in toxicity prediction compared to conventional ADMET predictors. HybridAI was cross-validated through a case study by repurposing kinase inhibitors for non-small cell lung cancer (NSCLC), with a correct prediction of afatinib based on 89 parcent binding affinity, and subsequently confirmed in vitro within 14 days. The findings highlight the limitations of single AI models for drug discovery and underscore the importance of hybrid AI architectures in delivering greater predictive reliability. By utilising multi-modal learning frameworks, Hybrid AI provides an open and adaptable infrastructure that facilitates the acceleration of precision medicine, reduces inefficiencies in drug development, and personalises therapeutic strategies.

Despite all of the power that machine learning and artificial intelligence (AI) models bring to applications, much of AI development is currently a fairly ad hoc process. Software engineering and AI development share many of the same languages and tools, but AI development as an engineering practice is still in early stages. Mining software repositories of AI models enables insight into the current state of AI development. However, much of the relevant metadata around models are not easily extractable directly from repositories and require deduction or domain knowledge. This paper presents a library called AIMMX that enables simplified AI Model Metadata eXtraction from software repositories. The extractors have five modules for extracting AI model-specific metadata: model name, associated datasets, references, AI frameworks used, and model domain. We evaluated AIMMX against 7,998 open-source models from three sources: model zoos, arXiv AI papers, and state-of-the-art AI papers. Our platform extracted metadata with 87% precision and 83% recall. As preliminary examples of how AI model metadata extraction enables studies and tools to advance engineering support for AI development, this paper presents an exploratory analysis for data and method reproducibility over the models in the evaluation dataset and a catalog tool for discovering and managing models. Our analysis suggests that while data reproducibility may be relatively poor with 42% of models in our sample citing their datasets, method reproducibility is more common at 72% of models in our sample, particularly state-of-the-art models. Our collected models are searchable in a catalog that uses existing metadata to enable advanced discovery features for efficiently finding models.

Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the ‘black-box’ nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science’s emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI’s ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science – termed the ‘New Urban Science’ – promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science’s foundational goals.

The paper analyses and investigates the usage of quantum convolutional neural networks in technical, natural, and socio-economic systems. Quantum convolutional neural networks are a novel approach to information processing that is based on the principles of quantum mechanics and artificial intelligence. In technical systems, the potential of using quantum convolutional neural networks for solving complex tasks such as image processing, machine learning, and prediction has been explored. The results have shown that quantum convolutional neural networks can provide more accurate and faster computations compared to classical neural networks. In natural systems, research has been conducted on the use of quantum convolutional neural networks for modeling and predicting complex natural processes. Their effectiveness in understanding genetic data, studying complex molecular structures, and analyzing ecological systems has been investigated. It has been found that quantum convolutional neural networks can deliver more precise and rapid results compared to conventional data processing methods. In socio-economic systems, the possibilities of employing quantum convolutional neural networks for social network analysis, financial market forecasting, and resource management have been studied. The application of quantum convolutional neural networks has the potential to enhance prediction accuracy and facilitate more effective decision-making in socio-economic systems. The research findings confirm that quantum convolutional neural networks have the potential to be utilized in various domains, including technical, natural, and socio-economic systems. They can achieve higher accuracy, processing speed, and predictive capabilities compared to traditional methods.

With growing applications such as image recognition, speech recognition, ADAS, and AIoT, artificial intelligence (AI) frameworks are becoming popular in various industries. Currently, many choices for neural network frameworks exist for executing AI models in applications, especially for training/inference purposes, including TensorFlow, Caffe, MXNet, PyTorch, Core ML, TensorFlow Lite, and NNAPI. With so many different emerging frameworks, exchange formats are needed for different AI frameworks. Given this requirement, the Khronos group created a standard draft known as the Neural Network Exchange Format (NNEF). However, because NNEF is new, conversion tools for various AI frameworks that would allow the exchange of various AI frameworks remain missing. In this work, we fill this gap by devising NNAPI conversion tools for NNEF. Our work allows NNEF to execute inference tasks on host and Android platforms and flexibly invokes Android neural networks through the API (NNAPI) on the Android platform to speed up inference operations. We invoke NNAPI by dividing the input NNEF model into multiple submodels and let NNAPI execute these submodels. We develop an algorithm named BFSelector that is based on a classic breadth-first search and includes cost constraints to determine how to divide the input model. Our preliminary experimental results show that our support of NNEF on NNAPI can obtain a speedup of 1.32 to 22.52 times over the baseline for API 27 and of 4.56 to 211 times over the baseline for API 28, where the baseline is the NNEF-to-Android platform conversion without invoking NNAPI. The experiment includes AI models such as LeNet, AlexNet, MobileNet_V1, MobileNet_V2, VGG-16, and VGG-19.

The article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression (LR) in enhancing the performance of the fecal immunochemical test (FIT) for the detection of colorectal carcinoma (CRC). The study cohort comprised 544 patients with colorectal neoplasia (CRN), including 58 CRC and 486 non-CRC cases, recruited from the Barretos Cancer Hospital. Each patient provided three consecutive fecal samples, which were analyzed using two fecal occult blood (FOB) assays: ColonView-FIT (CV) and HemoccultSENSA. Four AI models - gradient boosting machine (GBM), neural network (NN), random forest (RF), and support vector machine (SVM) - were developed, incorporating clinical features and CV results. Diagnostic performance was assessed via hierarchical summary receiver operating characteristic (HSROC) curves. In conventional analysis, the area under the curve (AUC) values for different AI models ranged from 0.926 to 0.977, while the highest AUC values were reached by gradient boosting machine (GBM), neural network (NN), and random forest (RF) models (0.974, 0.976 and 0.977, respectively). In the HSROC analysis, the AUC values for i) 'low risk' variables, ii) 'high risk' variables, and iii) AI models were as follows: i) AUC=0.503 (95% CI=0.390-0.613), ii) AUC=0.773 (95% CI=0.713-0.837), and iii) AUC=0.958 (95% CI=0.930-0.989). In all comparisons of the AUC values, the difference was highly significant (p<0.0001). AI models outperformed conventional LR and non-AI diagnostic features in improving FIT-based CRC screening. This is the first study to show that combining clinical data with FIT results in AI frameworks can significantly improve diagnostic accuracy in CRC screening.

The digitalization of various areas of activity to ensure sustainable development is accompanied by the increasing use of artificial intelligence (AI) tools. Adaptation of artificial intelligence models to the target application area can be carried out using subject knowledge models. The use of artificial intelligence in combination with effective knowledge management is crucial for ensuring the competitiveness of organizations in conditions of rapid environmental changes. The integration of artificial intelligence with knowledge models creates several problems related to the coordination of information processing models and the interpretation of their results. These problems are related to technological, organizational and ethical aspects. Large language models (LLM) based on deep learning (DL) methods are used in the field of natural language recognition (NLP). The convergence of LLM and GN aims to use the advantages of both models, providing a convergent model that can work well in both knowledge representation and logical inference. Applying knowledge models to classify AI applications in 5G/6G networks according to their role in network operations and impact on vertical areas such as the Internet of Things (IoT), healthcare, and transportation provides network optimization, predictive analytics, and improved security. The convergence of AI and knowledge models into a metaverse creates specific challenges that arise from the interaction between virtual environments and technologies. The article discusses approaches to ensuring the consistent use of AI and knowledge models in solving various tasks, and also identifies priority tasks related to the integration of AI and knowledge models.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?