%0 Journal Article %@ 2369-1999 %I JMIR Publications %V 11 %N %P e64611 %T Leveraging Patient-Reported Outcome Measures for Optimal Dose Selection in Early Phase Cancer Trials %A Byrom,Bill %A Everhart,Anthony %A Cordero,Paul %A Garratt,Chris %A Meyer,Tim %K clinical trials %K early phase %K dose finding %K patient-reported outcome %K PRO %K electronic patient-reported outcome %K ePRO %K PRO-CTCAE %K adverse events %K tolerability %K optimal dose %K cancer trials %K dose toxicity %K oncology %K drug development %K electronic collection %K dose level %K pharmacodynamic %K cytotoxic chemotherapy drugs %K cytotoxic %K chemotherapy drug %K life-threatening disease %K Common Terminology Criteria for Adverse Events %D 2025 %7 28.2.2025 %9 %J JMIR Cancer %G English %X While patient-reported outcome measures are regularly incorporated into phase 3 clinical trials, they have been infrequently used in early phase trials. However, the patient’s perspective is vital to fully understanding dose toxicity and selecting an optimal dose. This viewpoint paper reviews the rationale for and practical approach to collecting patient-reported outcome data in early phase oncology drug development and the rationale for electronic collection. %R 10.2196/64611 %U https://cancer.jmir.org/2025/1/e64611 %U https://doi.org/10.2196/64611 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e64406 %T Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models %A Chow,James C L %A Li,Kay %+ Princess Margaret Cancer Centre, University Health Network, 7/F, Rm 7-606, 700 University Ave, Toronto, ON, M5G 1X6, Canada, 1 4169464501, james.chow@uhn.ca %K artificial intelligence %K humanistic AI %K ethical AI %K human-centered AI %K machine learning %K large language models %K natural language processing %K oncology chatbot %K transformer-based model %K ChatGPT %K health care %D 2024 %7 6.11.2024 %9 Viewpoint %J JMIR Bioinform Biotech %G English %X The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably. %M 39321336 %R 10.2196/64406 %U https://bioinform.jmir.org/2024/1/e64406 %U https://doi.org/10.2196/64406 %U http://www.ncbi.nlm.nih.gov/pubmed/39321336 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e57199 %T Impact of Patient Personality on Adherence to Oral Anticancer Medications: An Opportunity? %A Jafari,Mahtab %A Shahverdian,Alex %A Sadigh,Gelareh %A Van Etten,Richard A %+ Department of Pharmaceutical Scienes, University of California, Irvine, 856 Health Sciences Quad 5400, Room 4020, Irvine, CA, 92697-3958, United States, 1 949 8240145, mjafari@uci.edu %K cancer %K medication adherence %K medication persistence %K Five-Factor Model %K Type D personality %K oncology %K cancer medications %K oral anticancer therapy %K chemotherapy %D 2024 %7 30.10.2024 %9 Viewpoint %J JMIR Cancer %G English %X Adherence to prescribed oral anticancer therapy is an important determinant of patient outcomes, including progression-free and overall survival. While many factors (eg, medication side effects and out-of-pocket costs, problems with insurance authorization, and timely medication refills) can affect adherence, one that is relatively unexplored is the impact of a patient’s attitude and personality. Patient personality influences medication adherence and persistence in nonmalignant chronic conditions such as cardiovascular disease and diabetes. In breast cancer and chronic myeloid leukemia, studies suggest that personality also affects adherence to oral chemotherapy which can be targeted to improve adherence. In this viewpoint, we highlight the opportunity of incorporating patient personality as interventions to oral cancer therapy adherence and discuss current barriers to implementation. %M 39475848 %R 10.2196/57199 %U https://cancer.jmir.org/2024/1/e57199 %U https://doi.org/10.2196/57199 %U http://www.ncbi.nlm.nih.gov/pubmed/39475848 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e56935 %T Lessons Learned From Shared Decision-Making With Oral Anticoagulants: Viewpoint on Suggestions for the Development of Oral Chemotherapy Decision Aids %A McLoughlin,Daniel E %A Moreno Echevarria,Fabiola M %A Badawy,Sherif M %+ Northwestern University Feinberg School of Medicine, 420 E Superior St, Chicago, IL, 60611, United States, 1 312 503 8194, daniel.mcloughlin@northwestern.edu %K shared decision-making %K SDM %K decision aids %K decision aids design %K oral chemotherapy %K oral anticoagulants %K drug delivery %K chemotherapy %K chemo %K anticoagulants %K drug deliveries %K cancer %K oncology %K oncologist %K metastases %K literature review %K literature reviews %D 2024 %7 11.9.2024 %9 Viewpoint %J JMIR Cancer %G English %X Oral chemotherapy is commonly prescribed, and by using decision aids (DAs), clinicians can facilitate shared decision-making (SDM) to align treatment choices with patient goals and values. Although products exist commercially, little evidence informs the development of DAs targeting the unique challenges of oral chemotherapy. To address this gap in the literature, our objective was to review DAs developed for oral anticoagulation, DA use in oncology, and patient preference surveys to guide the development of DAs for oral chemotherapy. We focused on reviewing SDM, patient preferences, and specifically the development, efficacy, and patient experience of DAs in oral anticoagulation and oncologic conditions, ultimately including conclusions and data from 30 peer-reviewed publications in our viewpoint paper. We found that effective DAs in oral anticoagulation improved knowledge, lowered decisional conflict, increased adherence, and covered a broad range of SDM elements; however, limited information on patient experience was a common shortcoming. In oncology, DAs increased knowledge and aligned decisions with the values of the patients. Ineffective oncology DAs provided general, unclear, or overly optimistic information, while providing “too much” information was not shown to do harm. Patients preferred DAs that included pros and cons, side effects, questions to ask, and expected quality of life changes. In developing DAs for oral chemotherapy, patients should be included in the development process, and DA content should be specifically tailored to patient preferences. Providing DAs ahead of appointments proved more effective than during, and additional considerations included addressing barriers to efficacy. There is a need for evidence-based DAs to facilitate SDM for patients considering oral chemotherapy. Developers should use data from studies in oral anticoagulation, oncology, and preference surveys to optimize SDM. %M 39187430 %R 10.2196/56935 %U https://cancer.jmir.org/2024/1/e56935 %U https://doi.org/10.2196/56935 %U http://www.ncbi.nlm.nih.gov/pubmed/39187430 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e57276 %T Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care %A Garcia-Saiso,Sebastian %A Marti,Myrna %A Pesce,Karina %A Luciani,Silvana %A Mujica,Oscar %A Hennis,Anselm %A D'Agostino,Marcelo %+ Pan American Health Organization, 525 23rd st NW, Washington, DC, 20037, United States, 1 7034737961, dagostim@paho.org %K digital health %K public health %K cancer %K artificial intelligence %K AI %K catalyst %K cancer care %K cost %K costs %K demographic %K epidemiological %K change %K changes %K healthcare %K equality %K health system %K mHealth %K mobile health %D 2024 %7 12.8.2024 %9 Viewpoint %J JMIR Cancer %G English %X As we enter the era of digital interdependence, artificial intelligence (AI) emerges as a key instrument to transform health care and address disparities and barriers in access to services. This viewpoint explores AI's potential to reduce inequalities in cancer care by improving diagnostic accuracy, optimizing resource allocation, and expanding access to medical care, especially in underserved communities. Despite persistent barriers, such as socioeconomic and geographical disparities, AI can significantly improve health care delivery. Key applications include AI-driven health equity monitoring, predictive analytics, mental health support, and personalized medicine. This viewpoint highlights the need for inclusive development practices and ethical considerations to ensure diverse data representation and equitable access. Emphasizing the role of AI in cancer care, especially in low- and middle-income countries, we underscore the importance of collaborative and multidisciplinary efforts to integrate AI effectively and ethically into health systems. This call to action highlights the need for further research on user experiences and the unique social, cultural, and political barriers to AI implementation in cancer care. %M 39133537 %R 10.2196/57276 %U https://cancer.jmir.org/2024/1/e57276 %U https://doi.org/10.2196/57276 %U http://www.ncbi.nlm.nih.gov/pubmed/39133537 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 10 %N %P e52577 %T “Notification! You May Have Cancer.” Could Smartphones and Wearables Help Detect Cancer Early? %A Scott,Suzanne E %A Thompson,Matthew J %+ Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, United Kingdom, 44 2078823550, suzanne.scott@qmul.ac.uk %K wearables %K early diagnosis %K cancer %K challenges %K diagnosis %K wearable %K detect %K detection %K smartphone %K cancer diagnosis %K symptoms %K monitoring %K monitor %K implementation %K anxiety %K health care service %K mobile phone %D 2024 %7 20.5.2024 %9 Viewpoint %J JMIR Cancer %G English %X This viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources. %M 38767941 %R 10.2196/52577 %U https://cancer.jmir.org/2024/1/e52577 %U https://doi.org/10.2196/52577 %U http://www.ncbi.nlm.nih.gov/pubmed/38767941 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 9 %N %P e44693 %T How I Built My Personal, Patient-Centered Health Care Team: Instead of Doctors, I Started With Students %A Wassersug,Richard %+ University of British Columbia, 168 Chadwick Court, Apt 1003, North Vancouver, BC, V7M 3L4, Canada, 1 604 563 9915, richard.wassersug@ubc.ca %K prostate cancer %K mentorship %K medical education %K students %K patient with cancer %K urologist %K support %K researchers %K patient-centered %K colleagues %K health care training %D 2023 %7 6.2.2023 %9 Patient Perspective %J JMIR Cancer %G English %X As a patient with cancer, I witnessed how beneficial it was to be treated by a multidisciplinary health care team. I realized I already had my own team, in a sense. That is because I had treated my research students as colleagues from the get-go, and I did not abandon them when they graduated and moved on. %M 36745488 %R 10.2196/44693 %U https://cancer.jmir.org/2023/1/e44693 %U https://doi.org/10.2196/44693 %U http://www.ncbi.nlm.nih.gov/pubmed/36745488