This workshop is designed to capacitate cancer care stakeholders with critical insights into the integration of digital solutions and artificial intelligence within cancer care. The workshop provides a critical overview of AI applications in early cancer detection, diagnostics, and patient management, utilising practical examples such as deep learning algorithms for mammogram analysis and AI-powered pathology automation. Participants will also explore digital tools like telemedicine and wearable devices that enhance patient engagement and symptom management. Challenges, including data privacy, algorithmic bias, and ethical considerations in AI implementation are discussed. Through selected cancer care case studies reflecting successful practices, participants will engage in discussions aimed at developing advanced insights for improving clinical outcomes, operational efficiencies, and the overall cancer patient experience.
This workshop provides an in-depth examination of the applications of AI into oncology exploring different AI algorithms and methods that have, so far, significantly enhanced cancer diagnosis, treatment planning, and patient management. Predictive modelling in cancer treatment, reflecting AI algorithms effectiveness in identifying patient-specific treatment regimens are explored, in addition to deep learning models, such as Convolutional Neural networks (CNNs), utilised for image analysis for demonstrating superior accuracy in detecting tumours in mammograms, CT and MRI scans. Selected case studies are utilised to discuss the effectiveness of key AI implementations, like IBM Watson's tailored treatment recommendations and GRAIL's multi-cancer early detection test. In addition to the need for continuous performance analysis and validation of AI systems in clinical settings, critical challenges such as data bias, interpretability, and regulatory compliance are addressed in a collaborative environment amongst oncologists, data scientists, and healthcare administrators.
The workshop is aimed at capacitating participants with essential knowledge and skills in deriving requirements specifications for AI-enabled cancer care. Focused on identifying, defining, and validating functional and non-functional requirements, the workshop develops a deep understanding of how these specifications can lead to effective digital and AI-driven cancer care solutions. Participants are guided through a process-driven approach to identify functional requirements such as diagnostic precision, predictive modelling, and personalised treatment strategies. Non-functional requirements such as cancer care systems’ scalability, data security, AI algorithms performance, AI-enabled cancer care systems regulatory compliance, interoperability, and cancer patient experience design are collaboratively explored in group-based settings. Attendees will learn how to prioritise these requirements based on cancer care stakeholders’ requirements and clinical needs. Overall, the workshop not only enhances participants' knowledge but also inspires them to effectively apply requirements engineering principles to advance the development of digital and AI-enabled cancer care systems that can significantly improve patient outcomes.
This workshop explores the transformative potential of Generative AI technologies within oncology. Participants will gain foundational knowledge about various Generative AI models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), focusing on their innovative applications in enhancing cancer diagnosis, personalising treatment, and facilitating advanced research methods. Participants, through group-based arrangements and selected case studies, will lean how Generative AI can improve diagnostic processes and will engage in hands-on activities to analyse diagnostic cases, showcasing practical uses of Generative AI within clinical oncology settings. Additionally, the workshop discusses personalised treatment planning with emphasis on the role of AI’s ability to tailor interventions based on individual patient data while addressing integration challenges faced by healthcare systems. Ethical and regulatory considerations are also discussed reflecting on the importance of navigating biases in AI models and ensuring data privacy.
This workshop capacitates participants with the essential skills for conducting health technology assessments (HTAs) tailored to digitally and AI-enabled cancer care solutions. It provided a collaborative environment for healthcare professionals and policymakers to deepen their understanding of HTAs effective evaluations, ultimately enhancing cancer patient outcomes and strategic resource allocation in oncology practices.
Participants will gain insights into HTA principles, focusing on clinical effectiveness, economic impacts, and ethical considerations in oncology. Through engaging presentations and interactive group activities, attendees will explore methods for assessing the value and feasibility of AI technologies, while discussing the critical importance of stakeholder engagement and collaboration in the HTA cancer care process. Selected case studies will be providing practical insights into how HTA can influence evidence-based decision-making in diverse cancer care settings. Also, the workshop addresses challenges, such as data compatibility and regulatory compliance, when integrating innovative technologies.
This workshop provides participants with critical insights into application of descriptive data analytics to cancer care. Attendees will gain a thorough understanding of key statistical concepts and methods, enabling them to analyse and interpret cancer care data effectively. By engaging in hands-on activities, participants will be able to compute descriptive statistics and derive meaningful insights relevant to cancer patient care and treatment strategies. A significant portion of the workshop is focused on data visualisation techniques, allowing attendees to transform raw data into impactful visual representations for within clinical settings. Furthermore, discussions on identifying trends and patterns in patient outcomes for driving informed healthcare decisions and improving treatment efficacy. The workshop also addresses the ethical considerations surrounding data handling, and the importance of compliance with regulations such as HIPAA. Overall, this workshop contributes to effectively capacitate cancer care stakeholders with the analytical competencies necessary to implement descriptive data analytics in cancer care, aiming to enhance decision-making and improve patient outcomes in oncology practices.
This workshop reinforces the significant role predictive data analytics plays in improving cancer patient journey outcomes and enhancing clinical decision-making in oncology. Through engaging presentations and hands-on activities, attendees explore a range of predictive modelling techniques, including those derived from data in Electronic Health Records (EHRs) and genomic data analysis, which are critical for individualized patient care. The workshop also addresses predictive analytics for optimizing cancer diagnosis and treatment by using case studies from notable global oncology institutions to illustrate impactful examples of predictive models for identifying patient risks, stratifying treatment options, and anticipating complications. Legal, social, ethical, and professional requirements surrounding cancer patients’ data, as well as the importance of navigating biases, maintaining patient privacy, and adhering to regulatory compliance, are discussed considering cancer care practices and predictive data analytics. Aimed at cancer care stakeholders, data scientists and analysts, and decision and policy makers, this workshop empowers participants with the necessary knowledge and skills to utilise predictive analytics effectively, facilitating a data-driven approach to oncology that enhances the overall cancer patient journey and streamlines treatment processes.
This workshop underscores the critical role that social factors play in influencing patient outcomes and healthcare delivery in oncology. Participants will learn various methods of social modelling, including bipartite and tripartite modelling, illustrating complex relationships between social determinants and cancer care pathways. By mapping out interactions between patients, healthcare providers, and social resources, social modelling provides insights into how these relationships affect treatment adherence and clinical outcomes. Attendees are engaged in practical examples and real-world case studies, such as the Chicago Cancer Health Equity Collaborative, to learn how social modelling can enhance patient-cantered care and address health disparities. Also, the workshop addresses ethical considerations associated with social modelling, including potential biases in data and privacy concerns. Overall, the workshop empowers cancer care stakeholders and policymakers with the necessary knowledge and tooling to apply social modelling effectively, aiming at enhance cancer care delivery and ensure equitable access to treatment for all patients. The workshop facilitated discussions on integrating social data into health technology assessments and predictive models, allowing participants to apply these concepts to their clinical settings.
This workshop provides participants with a critical examination of how Artificial Intelligence (AI) is transforming the landscape of cancer clinical trials. Focused on the applications of AI in clinical trial design, patient recruitment, data analysis, and outcome prediction, the workshop aims to enhance the efficiency, accuracy, and success of clinical trials in oncology. Participants, including clinical researchers, healthcare IT specialists, and policymakers, are capacitated with the tools and knowledge necessary to utilise AI technologies effectively, to improve patient outcomes and streamline research efforts. Through hands-on activities and selected case studies, participants will learn to apply advanced predictive analytics techniques in clinical trials’ context. Legal, Social, Ethical, Professional and regulatory considerations surrounding AI utilisation are critically discussed with particular reflections on data privacy, transparency, and compliance. Through collaborative learning and practical applications, this workshop seeks to facilitate the development of more effective, patient-cantered, and data-driven clinical trials to advance cancer care comprehensively.
This workshop provides participants with the knowledge and practical skills necessary to enhance oncology workflows through an innovative, comprehensive cancer care process architecture design. The workshop engages participants in how to create efficient, interoperable, and patient-centric process architectures using the Riva process architectural modelling method and BPMN workflow modelling language. Participants will engage in hands-on activities to map existing clinical workflows, identify key inefficiencies, and design workflows that prioritise patient engagement and incorporate AI technologies, such as predictive analytics and clinical decision support processes. The workshop also underscores the importance of stakeholder collaboration in process architecture and cancer care workflow design. Through interactive discussions and group-based cancer care scenarios, attendees will practice effective communication strategies to engage stakeholders, including clinicians, IT specialists, and administrators, developing a holistic approach to implementing AI-based and digital solutions in cancer care. Real-world case studies of cancer care process architecture and workflow models developed by the workshop tutors will enrich the learning experience and provide insights into successful workflow redesigns and integration strategies from leading cancer care centres.
This workshop is aimed at capacitating participants with essential knowledge and skills for implementing performance and quality management practices within AI and digitally enabled cancer care environments. In particular, the workshop discusses the creation of AI-enabled efficient, interoperable and patient-centric workflows that ultimately enhance patient outcomes. Cancer care stakeholders including oncologists, chief medical and nursing directors, policy and decision makers, senior healthcare IT specialists, etc. will gain insights into key performance indicators (KPIs) and quality improvement methodologies tailored to AI and digitally enabled oncology. Through interactive discussions and hands-on activities, participants will learn to map existing clinical workflows, identify inefficiencies, and design patient-centred strategies that incorporate AI technologies. The workshop also addresses the ethical and regulatory considerations related to the deployment of AI solutions, reflecting data privacy, AI technologies bias, and stakeholder collaboration. Selected case studies are utilised to reflect on the integration of AI into oncology practices, and how to navigate the complexities associated with digital cancer care innovations. Overall, the workshop seeks to empower cancer care stakeholders to plan, implement and evaluate the delivery of qualitative AI and digitally enabled cancer care services, for effective and streamlined patient management.
This workshop provides an in-depth exploration of the transformative impact of genomic data and artificial intelligence (AI) on oncology practice. Targeted at multidisciplinary participants, including oncologists, genomics specialists, healthcare IT professionals, and students, the workshop facilitates dynamic, critical, and reflective discussions on the integration of genomic insights and AI-driven analytics into personalised treatment strategies. The principles of precision medicine and the critical role of genomic profiling in developing targeted therapies are introduced, empowered by AI’s capabilities in predictive modelling and data interpretation. Using selected case studies and group-based settings, participants will critically examine advanced technologies, such as next-generation sequencing (NGS) and AI algorithms that can identify biomarkers and suggest individualized treatment pathways. The ethical, legal, and social implications of combining genomic data with AI are explored, including patient privacy, equity, and potential biases in algorithm-driven decisions.
This workshop provides an in-depth exploration of the integration of generative AI into the derivation of requirements modelling for the development of breast cancer clinical practice guidelines (CPGs), using selected international oncology society guidelines, but reflecting the crucial alignment with patient notes. This workshop offers an overview of the significance of CPGs while examining innovations in generative AI that enhance the requirements modelling process for breast cancer. Participants will engage in discussions on strategies to ensure that breast cancer patient note documentation supports adherence to relevant oncology society guidelines, thereby improving the precise specification of breast cancer patient notes aligned with relevant Key Performance Indicators (KPIs). The ethical, legal, and social implications of AI in healthcare are also critically addressed, highlighting the importance of responsible AI deployment in managing the breast cancer patient journey. In a collaborative and group-based activity, participants will execute prototyped AI-driven breast CPGs while aligning them with patient notes, utilising generative AI tools. Participants will develop critical insights from selected breast cancer case studies applying AI to align with the relevant international CPGs and exemplary patient notes. The workshop concludes with a summary of key insights and actionable strategies, empowering participants to implement AI-enhanced requirements modelling and documentation in alignment with their clinical practices, thereby advancing the quality and consistency of breast cancer care outcomes.
This workshop is aimed at capacitating participants with in-depth understanding of the integration of generative AI into the derivation of requirements modelling for the development of lung cancer clinical practice guidelines (CPGs), using selected international oncology society guidelines, but reflecting the crucial alignment with patient notes. This workshop offers an overview of the significance of CPGs while examining innovations in generative AI that enhance the requirements modelling process for lung cancer. Participants will engage in discussions on strategies to ensure that lung cancer patient note documentation supports adherence to relevant oncology society guidelines, thereby improving the precise specification of lung cancer patient notes aligned with relevant Key Performance Indicators (KPIs). The ethical, legal, and social implications of AI in healthcare are also critically addressed, highlighting the importance of responsible AI deployment in managing the lung cancer patient journey. In a collaborative and group-based activity, participants will execute prototyped AI-driven lung CPGs while aligning them with patient notes, utilising generative AI tools. Participants will develop critical insights from selected lung cancer case studies applying AI to align with the relevant international CPGs and exemplary patient notes. The workshop concludes with a summary of key insights and actionable strategies, empowering participants to implement AI-enhanced requirements modelling and documentation in alignment with their clinical practices, thereby advancing the quality and consistency of lung cancer care outcomes.
This workshop provides an in-depth exploration of the integration of generative AI into the derivation of requirements modelling for the development of colon rectal cancer clinical practice guidelines (CPGs), using selected international oncology society guidelines, but reflecting the crucial alignment with patient notes. This workshop offers an overview of the significance of CPGs while examining innovations in generative AI that enhance the requirements modelling process for colon rectal cancer. Participants will engage in discussions on strategies to ensure that colon rectal cancer patient note documentation supports adherence to relevant oncology society guidelines, thereby improving the precise specification of colon rectal cancer patient notes aligned with relevant Key Performance Indicators (KPIs). The ethical, legal, and social implications of AI in healthcare are also critically addressed, highlighting the importance of responsible AI deployment in managing the colon rectal cancer patient journey. In a collaborative and group-based activity, participants will execute prototyped AI-driven colon rectal CPGs while aligning them with patient notes, utilising generative AI tools. Participants will develop critical insights from selected colon rectal cancer case studies applying AI to align with the relevant international CPGs and exemplary patient notes. The workshop concludes with a summary of key insights and actionable strategies, empowering participants to implement AI-enhanced requirements modelling and documentation in alignment with their clinical practices, thereby advancing the quality and consistency of colon rectal cancer care outcomes.
This workshop is aimed at capacitating participants with an in-depth exploration of the integration of generative AI into the derivation of requirements modelling for the development of cervical cancer clinical practice guidelines (CPGs), using selected international oncology society guidelines, but reflecting the crucial alignment with patient notes. This workshop offers an overview of the significance of CPGs while examining innovations in generative AI that enhance the requirements modelling process for cervical cancer. Participants will engage in discussions on strategies to ensure that cervical cancer patient note documentation supports adherence to relevant oncology society guidelines, thereby improving the precise specification of cervical cancer patient notes aligned with relevant Key Performance Indicators (KPIs). The ethical, legal, and social implications of AI in healthcare are also critically addressed, highlighting the importance of responsible AI deployment in managing the cervical cancer patient journey. In a collaborative and group-based activity, participants will execute prototyped AI-driven cervical CPGs while aligning them with patient notes, utilising generative AI tools. Participants will develop critical insights from selected cervical cancer case studies applying AI to align with the relevant international CPGs and exemplary patient notes. The workshop concludes with a summary of key insights and actionable strategies, empowering participants to implement AI-enhanced requirements modelling and documentation in alignment with their clinical practices, thereby advancing the quality and consistency of cervical cancer care outcomes.
This workshop provides an in-depth exploration of the integration of generative AI into the derivation of requirements modelling for the development of Paediatric Acute Lymphoblastic Leukaemia (pALL) clinical practice guidelines (CPGs), using selected international oncology society guidelines, but reflecting the crucial alignment with patient notes. This workshop offers an overview of the significance of CPGs while examining innovations in generative AI that enhance the requirements modelling process for pALL. Participants will engage in discussions on strategies to ensure that pALL patient note documentation supports adherence to relevant oncology society guidelines, thereby improving the precise specification of pALL patient notes aligned with relevant Key Performance Indicators (KPIs). The ethical, legal, and social implications of AI in healthcare are also critically addressed, highlighting the importance of responsible AI deployment in managing the pALL patient journey. In a collaborative and group-based activity, participants will execute prototyped AI-driven pALL CPGs while aligning them with patient notes, utilising generative AI tools. Participants will develop critical insights from selected pALL case studies applying AI to align with the relevant international CPGs and exemplary patient notes. The workshop concludes with a summary of key insights and actionable strategies, empowering participants to implement AI-enhanced requirements modelling and documentation in alignment with their clinical practices, thereby advancing the quality and consistency of pALL care outcomes.