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Vens C, van Luijk P, Vogelius RI, El Naqa I, Humbert-Vidan L, von Neubeck C, Gomez-Roman N, Bahn E, Brualla L, Böhlen TT, Ecker S, Koch R, Handeland A, Pereira S, Possenti L, Rancati T, Todor D, Vanderstraeten B, Van Heerden M, Ullrich W, Jackson M, Alber M, Marignol L. A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy. Radiother Oncol 2024; 196:110277. [PMID: 38670264 DOI: 10.1016/j.radonc.2024.110277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines.
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Affiliation(s)
- C Vens
- School of Cancer Science, University of Glasgow, Glasgow, UK; Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
| | - P van Luijk
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - R I Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
| | - I El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48103, United States.
| | - L Humbert-Vidan
- University of Texas MD Anderson Cancer Centre, Houston, TX, United States; Department of MedicalPhysics, Guy's and St Thomas' NHS Foundation Trust, London, UK; School of Cancer and Pharmaceutical Sciences, Comprehensive Cancer Centre, King's College London, London, UK
| | - C von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen 45147, Germany
| | - N Gomez-Roman
- Strathclyde Institute of Phrmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - E Bahn
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany; Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - L Brualla
- West German Proton Therapy Centre Essen (WPE), Essen, Germany; Faculty of Medicine, University of Duisburg-Essen, Germany
| | - T T Böhlen
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - S Ecker
- Department of Radiation Oncology, Medical University of Wien, Austria
| | - R Koch
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen 45147, Germany
| | - A Handeland
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway; Department of Physics and Technology, University of Bergen, Bergen, Norway
| | - S Pereira
- Neolys Diagnostics, 7 Allée de l'Europe, 67960 Entzheim, France
| | - L Possenti
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - T Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - D Todor
- Department of Radiation Oncology, Virginia Commonwealth University, United States
| | - B Vanderstraeten
- Department of Radiotherapy-Oncology, Ghent University Hospital, Gent, Belgium; Department of Human Structure and Repair, Ghent University, Gent, Belgium
| | - M Van Heerden
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | | | - M Jackson
- School of Cancer Science, University of Glasgow, Glasgow, UK
| | - M Alber
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
| | - L Marignol
- Applied Radiation Therapy Trinity (ARTT), Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland
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Davis SE, Embí PJ, Matheny ME. Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance. J Am Med Inform Assoc 2024; 31:1195-1198. [PMID: 38422379 PMCID: PMC11031208 DOI: 10.1093/jamia/ocae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. OBJECTIVE Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles. DISCUSSION We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.
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Affiliation(s)
- Sharon E Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Peter J Embí
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Michael E Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Geriatric Research, Education, and Clinical Care, Tennessee Valley Healthcare System VA Medical Center, Veterans Health Administration, Nashville, TN 37212, United States
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Moodley K. Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities. S Afr Med J 2023; 114:22-26. [PMID: 38525617 PMCID: PMC11296939 DOI: 10.7196/samj.2024.v114i1.1631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Indexed: 03/26/2024] Open
Abstract
The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.
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Affiliation(s)
- K Moodley
- Division of Medical Ethics and Law, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
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Adus S, Macklin J, Pinto A. Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. BMC Health Serv Res 2023; 23:1163. [PMID: 37884940 PMCID: PMC10605984 DOI: 10.1186/s12913-023-10098-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/01/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care. METHODS We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis. RESULTS We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development. CONCLUSION Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.
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Affiliation(s)
- Samira Adus
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Jillian Macklin
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, Institute of Health Policy, Management, and Evaluation, Toronto, ON, Canada
| | - Andrew Pinto
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, Institute of Health Policy, Management, and Evaluation, Toronto, ON, Canada
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, ON, Canada
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Papachristou N, Kotronoulas G, Dikaios N, Allison SJ, Eleftherochorinou H, Rai T, Kunz H, Barnaghi P, Miaskowski C, Bamidis PD. Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field. Semin Oncol Nurs 2023; 39:151433. [PMID: 37137770 DOI: 10.1016/j.soncn.2023.151433] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVES To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. DATA SOURCES Peer-reviewed scientific publications and expert opinion. CONCLUSION The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services. IMPLICATIONS FOR NURSING PRACTICE As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.
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Affiliation(s)
- Nikolaos Papachristou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | | | - Nikolaos Dikaios
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Mathematics Research Centre, Academy of Athens, Athens, Greece
| | - Sarah J Allison
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Newcastle, UK; School of Bioscience and Medicine, Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | | | - Taranpreet Rai
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, UK; Datalab, The Veterinary Health Innovation Engine (vHive), Guildford, UK
| | - Holger Kunz
- Institute of Health Informatics, University College London, London, UK
| | - Payam Barnaghi
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, UK
| | - Christine Miaskowski
- School of Nursing, University California San Francisco, San Francisco, California, USA
| | - Panagiotis D Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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van Leeuwen A, Strauß S, Rummel N. Participatory design of teacher dashboards: navigating the tension between teacher input and theories on teacher professional vision. Front Artif Intell 2023; 6:1039739. [PMID: 37304525 PMCID: PMC10248228 DOI: 10.3389/frai.2023.1039739] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
In the field of AI in education, there is a movement toward human-centered design in which the primary stakeholders are collaborators in establishing the design and functionality of the AI system (participatory design). Several authors have noted that there is a potential tension in participatory design between involving stakeholders and, thus, increasing uptake of the system on the one hand, and the use of educational theory on the other hand. The goal of the present perspective article is to unpack this tension in more detail, focusing on the example of teacher dashboards. Our contribution to theory is to show that insights from the research field of teacher professional vision can help explain why stakeholder involvement may lead to tension. In particular, we discuss that the sources of information that teachers use in their professional vision, and which data sources could be included on dashboards, might differ with respect to whether they actually relate to student learning or not. Using this difference as a starting point for participatory design could help navigate the aforementioned tension. Subsequently, we describe several implications for practice and research that could help move the field of human centered design further.
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Affiliation(s)
| | - Sebastian Strauß
- Institute for Educational Research, Ruhr University Bochum, Bochum, Germany
| | - Nikol Rummel
- Institute for Educational Research, Ruhr University Bochum, Bochum, Germany
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