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JMIR Cancer
Patient-centered innovations, education, and technology for cancer care, cancer survivorship, and cancer research.
Background: Patients receiving systemic anti-cancer therapy can deteriorate rapidly between appointments, yet acute oncology services often rely on reactive helplines with limited symptom visibility. Objective: To evaluate the feasibility, safety, and workflow integration of OncsCare, a digital symptom triage platform mapping patient-reported symptoms to UK Oncology Nursing Society (UKONS) acuity tiers with episode-based clinician review. Methods: This 10-week service evaluation (July–September 2025) implemented OncsCare within a UK tertiary acute oncology service. Patients completed daily symptom check-ins mapped to UKONS-informed green/amber/red tiers. Alerts were grouped into episode-level triage events using prespecified rules (48-hour window, symptom-domain continuity) to represent operational workload. Outcomes included engagement, alert distribution, escalation pathways, review timeliness, and safety signals via structured case-finding. Results: Thirty-two patients participated (none withdrew). Daily check-in completion rate was 91.7% (1444/1574 expected patient-days). From 362 amber/red alerts, 62 episodes were generated; 38.7% (24/62) were clinically actionable, resulting in telephone management (50%), acute care assessment (37.5%), emergency referral (8.3%), or admission (4.2%). Median review time for in-hours red alerts was 47 minutes. Predefined safety case-finding identified no intervention-attributable safety signals. Patients reported increased home reassurance (85%) and clinicians reported improved situational awareness without increased workload. Conclusions: UKONS-informed digital triage with episode-based review demonstrated feasibility and safety in routine acute oncology care. This operational model addresses alert fragmentation and supports multicentre evaluation. Clinical Trial: Not applicable
(Toronto, February 15, 2026) JMIR Publications is pleased to announce that JMIR Cancer has been accepted and is now indexed in CABI’s Global Health database.
As a premier resource for international public health, CABI’s Global Health covers literature from 158 countries with a specialized focus on tropical diseases, nutrition, and community health. This inclusion ensures that research published in JMIR Cancerreaches a wider global audience of practitioners and researchers.
JMIR Cancer joins an elite group of JMIR journals in this database. You can view the full list in our Knowledge Base.
JMIR Cancer (JC, ISSN: 2369-1999) is a peer-reviewed journal focusing on education, innovation and technology in cancer care, cancer survivorship and cancer research, and participatory and patient-centred approaches. This journal also includes research on non-Internet approaches to improve cancer care and cancer research.
We invite submissions of original research, viewpoints, reviews, tutorials, and non-conventional articles (e.g. open patient education material and software resources that are not yet evaluated but are free for others to use/implement).
In our "Patients' Corner," we invite patients and survivors to submit short essays and viewpoints on all aspects of cancer. In particular, we are interested in suggestions on improving the health care system and suggestions for new technologies, applications and approaches (this section has no article processing fees).
Background: The number of cancer survivors is steadily increasing worldwide, leading to an increased demand for long-term follow-up and supportive care. Many survivors face ongoing physical and psychosocial issues that highlight the need for innovative management approaches. Mobile health applications offer potential benefits by facilitating patient-led follow-ups, self-management, and more efficient use of healthcare resources. Although the market for cancer-related mobile apps has grown rapidly, their sustainability and scientific basis remain unclear. In the EU, the Medical Device Regulation (MDR), which has been in effect since May 2021, has introduced stricter criteria for classifying medical devices, including certain software applications. While aiming to improve patient safety, MDR could pose challenges for small companies and academic developers, potentially limiting the availability of such applications. No scoping review has delineated the changes in active applications before and after the implementation of the new legislation regulating medical devices. Objective: This scoping review aimed to evaluate the current availability and longevity of mobile applications supporting cancer recovery, with a specific focus on changes before and after the implementation of the EU Medical Device Regulation (MDR), and to assess the extent to which these applications are supported by clinical evidence. Methods: Searches were conducted in mobile application stores (Apple App Store and Google Play) and literature databases (MEDLINE, Embase, Cochrane Library, and Web of Science), using predefined terms. Mobile applications targeting cancer recovery and published articles on their effectiveness were included. Two reviewers independently extracted the data. A descriptive analysis was conducted to report trends in mobile device application availability and updates over time. Results: A total of 151 mobile applications were identified in 2018. However, by 2024, only 30% (45/151) were still available. Among these, 25 (17%) were updated within the past two years. During the search in December 2024, one new mobile application supported by scientific evidence was discovered. This mobile application was developed to assist cancer survivors in managing insomnia through cognitive behavioural therapy. Rapid turnover and a potential lack of sustainability in the mobile health application market for cancer survivors were evident, with most mobile applications identified in 2018 no longer available by 2024. Conclusions: This review revealed a significant lack of publicly available mobile applications that support cancer recovery. The longevity of existing mobile applications is limited, potentially because of regulatory and financial barriers. Prioritising rigorous effectiveness trials, addressing implementation barriers, and developing sustainable business models are essential to ensure the long-term availability and success of mobile health applications in cancer survivorship care.
Background: A peripherally inserted central catheter (PICC) for vesicant or long-term chemotherapy (CTx) is recommended for safe and sustainable drug delivery. However, maintaining its benefits requires regular and careful self-management. Although medical staff provide education and telephone consultation, proactive support accessible at any time or location remains limited. Therefore, we developed a rule-based chatbot to support PICC self-management. Objective: To evaluate the feasibility of a chatbot designed to support PICC self-management by examining chatbot use rate, usability, and user experience. Methods: A mixed-methods study was conducted from September to December 2022, adhering to the Good Reporting of a Mixed Methods Study (GRAMMS) guideline. Patients with cancer scheduled for PICC insertion and their caregivers were recruited, as PICC care is commonly performed by patients or cohabiting caregivers. All participants provided written informed consent. The chatbot was designed to provide structured responses based on prespecified dialog trees and to recognize users’ intent using natural language processing. It was delivered through KakaoTalk and accessed on participants’ personal mobile phones without requiring a separate application installation. Participants received face-to-face training at enrollment and were asked to voluntarily use the chatbot for one month. Baseline and post-intervention surveys assessing usability were administered using paper-based questionnaires. Usage logs were collected from a secure researcher dashboard and analyzed for inquiry topics, free-text inputs, and fallback situations. Semi-structured interviews were conducted approximately one month after the intervention during outpatient visits, with invitations by telephone, to explore participants’ experiences regarding chatbot use. Quantitative data were analyzed descriptively to summarize participant characteristics, chatbot use, and usability outcomes, while qualitative interview data were analyzed using thematic analysis. Results: A total of 56 participants were included in the final analysis (mean age 55.4 years, SD 13.7; 39/56(70%) female). Among them, 28(50%) used the chatbot at least once. Chatbot users were younger than non-users (51.1 vs. 59.6 years, P = .02). Of the 25 users who agreed to log analysis, 347 inquiries were recorded; frequent topics included catheter care (obs=126), managing daily life (obs=85), symptoms (obs=72), and heparin use (obs=55). Among the 23 users who completed the usability survey, 20/23(87%) reported that the chatbot was helpful for PICC-related issues. Qualitative interviews (N = 56) identified three major benefits ─information accessibility, effective guidance, and psychosocial support─ while also revealing unmet needs related to conversational issues, user experience issues, and lack of personalization. Conclusions: A rule-based chatbot designed to support PICC self-management demonstrates potential to enhance information accessibility, provide practical guidance, and offer psychosocial support. However, limitations related to conversational flexibility, interface usability, and personalization highlight the need for future development incorporating large language models (LLMs). Longitudinal and multi-site studies are warranted to assess sustained user engagement and clinical outcomes.
Background: Mobile Health (mHealth) represents a promising instrument for optimizing symptom management and important lifestyle strategies that enhance self-care and the quality of healthcare for cancer patients. The ALIBIRD mHealth platform is a digital health solution specifically designed for the telemonitoring of oncology patients, fostering patient empowerment and supporting clinical decision-making. Objective: The primary objective of this study was to evaluate the patient experience with the ALIBIRD platform. Additionally, the study aimed to assess clinical outcomes, particularly in symptom management, nutritional status, and lifestyle, using patient-reported outcome measures. Methods: The evaluation was conducted over a 30-week period in patients with advanced lung cancer receiving active treatment. Outcome variables included usability, patient experience, symptom burden, lifestyle behaviors (diet, physical activity, and sleep), nutritional status, patient-reported outcome measures (PROMs), and system-generated clinical alerts. Through the mobile app, patients reported symptoms and completed integrated REDCap questionnaires assessing lifestyle behaviors and patient-reported outcome measures (PROMs), while receiving personalized recommendations informed by nutrigenetic and gut microbiota assessments. Daily activity and sleep data were automatically captured using the Fitbit Inspire wearable. Clinicians remotely monitored patient data using a web-based dashboard and performed clinical actions when required, including phone calls, therapeutic adjustments, referrals, and appointment rescheduling. Statistical analysis included descriptive summaries and pre–post comparisons of clinical and patient-reported outcomes (PROs). Results: Out of 20 patients recruited for the study, 14 completed the intervention. The System Usability Scale yielded a score of 90, indicating high usability. Among the 14 completers, adherence to scheduled questionnaires ranged from 94% to 100% for several instruments, and wearable‑based monitoring ranged from 66% to 96% across visits. Overall, the ALIBIRD platform collected and processed 3,589 PROs related to physical activity, 3,468 related to sleep, 679 on-demand symptom entries, and 1,524 completed questionnaires. Clinically, 143 alerts were resolved within an average of 2.05 days, resulting in two referrals to emergency rooms and two early detections of disease progressions. Furthermore, over 2,100 personalized recommendations contributed to a 21% (3 of 14 patients) increase in adherence to the Mediterranean diet and a 14% (2 of 14 patients) increase in moderate physical activity. Conclusions: The evaluation of the ALIBIRD implementation yielded promising results in that it facilitated the adoption of healthier life-style habits while enhancing health self-management among oncology patients. The ALIBIRD mHealth platform emerges as an effective digital health tool that enables closer monitoring of patients and thereby more informed clinical decision-making. Clinical Trial: ClinicalTrials.gov NCT05770869; https://clinicaltrials.gov/study/NCT05770869
Background: Reliance on telehealth increased dramatically during the COVID-19 pandemic, introducing new opportunities to consider the use of telehealth across the cancer control continuum. However, patient, clinician, and staff perspectives about the types of cancer care appointments that are considered appropriate and the clinical care needs to support expanded remote care services are limited. Understanding older adults’ diverse technology needs and perspectives is especially important given that they comprise a large and growing proportion of the cancer patient population. Objective: Our objective was to describe the perceptions and experiences of older patients with cancer and their clinical care team members regarding the expansion of telehealth use across the cancer control continuum, and to solicit suggestions about how to support telehealth use for cancer care delivery. Methods: Using a convergent mixed methods design, we surveyed and interviewed patients age ≥60, clinicians, and staff at a Comprehensive Cancer Center in the southern U.S. between December 2020 – November 2021. Interview questions were rooted in the sociotechnical model, which proposes eight interrelated dimensions representing factors influencing the design, use, and outcomes associated with health information technologies. Patient survey domains included telehealth experience and satisfaction, and factors affecting telehealth perceptions and use; clinician survey domains included contexts of telehealth appropriateness, training, and barriers and facilitators to telehealth service provision. Survey data were analyzed using descriptive statistics. Qualitative data were thematically analyzed using a combined deductive and inductive approach. Results: We received completed surveys from 128 patients (567 invited) and 106 clinicians and staff (146 invited). We completed 14 patient (29 invited) and 20 clinicians and staff (22 invited) interviews. Across all participants, most agreed or strongly agreed that multiple cancer care appointment types should be offered via telehealth, including discussing treatment side-effects (n=75/102, 73.5% of patients and n=66/94, 70.2% of clinicians/staff), results communication (n=71/102, 69.6% of patients and n=65/94, 69.1% of clinicians/staff), and treatment follow-up (n=67/102, 65.7% of patients and n=52/93, 55.9% of clinicians/staff). In interviews, participants elaborated on factors influencing the appropriateness of telehealth versus in-person appointments, including symptom severity, type of cancer, and purpose of the appointment. Many patient and staff suggestions focused on ways to address digital literacy gaps, while clinicians recommended improving clinic workflows, infrastructure, and training. Conclusions: Overall, clinicians, staff, and older patients with cancer all responded positively toward expanding telehealth use across multiple cancer and appointment types across the cancer control continuum. Older adults with cancer are generally interested in telehealth for cancer care, especially if strategies to address digital literacy gaps are incorporated. Clinician and staff members expressed specialized training and infrastructure needs to optimize telehealth uptake and service delivery.
Background: Patient education has moved from brochures to websites, apps, and social media, but the accuracy of digital content is uneven. Telehealth and the rapid uptake of generative AI now embed chatbots in care pathways, offering on-demand guidance yet risking bias, errors, and fabricated citations. Sexual health—especially for men who have sex with men (MSM) after prostate-cancer treatment—remains underserved, prompting patients to seek anonymous online advice. GenAI chatbots could fill this gap, but their contributions must be evaluated through caritative caring theory, technogenesis, and actor-network theory to understand how human–technology networks co-produce caring encounters. Objective: This study aimed to describe and compare how four generative AI (GenAI) chatbots respond to questions about sexual health following prostate cancer treatment, focusing on the needs of a gay man, and to theorize these responses using netnographic and actor-network theory perspectives. Methods: The first author engaged with ChatGPT (GPT-4o), Claude (3.5 Sonnet), Copilot (GPT-4 Turbo), and Gemini (2.0 Flash) using a standardized prompt. Responses were recorded and analyzed thematically, with attention to performativity, empathy, and cultural sensitivity. Results: All chatbots provided accurate, inclusive, and empathetic responses. Themes included clinical content quality, encouragement of dialogue, personalized self-care advice, discussion of same-sex practices, and varying degrees of cultural sensitivity. No hallucinated content was found. Chatbot behaviors were mapped along two continua—logical to empathetic and general to specific—resulting in four interaction types: structured overview, rational clarity, compassionate perspective, and compassionate precision. Conclusions: GenAI chatbots can support culturally sensitive, LGBTQI+-inclusive health communication. While they lack ethical consciousness, their performative responses resemble caring encounters and may complement nursing practice in sexual health.
Background: Postprostatectomy incontinence (PPI) is a common complication after robot-assisted radical prostatectomy and significantly impairs patients’ quality of life. Although behavioral interventions such as pelvic floor muscle training and bladder diaries are evidence-based, their effectiveness is often limited by poor adherence and lack of personalization.
Objective: This study aimed to develop and evaluate a reinforcement learning (RL)–driven clinical behavioral intervention-supporting system (CBISs) for adaptive, personalized rehabilitation in patients with PPI.
Methods: The study comprised 2 sequential stages. First, the CBISs was developed through (1) construction of a medical record database from a prospective cohort of PPI patients using standardized 3-day bladder diaries, (2) design of functional modules and user interfaces based on clinical rehabilitation needs, and (3) development of an RL model using XGBoost (extreme gradient boosting) and Bayesian optimization to generate individualized training plans. Second, a separate cohort of 16 patients participated in a single-arm, pre-post pilot study to evaluate feasibility and preliminary outcome trends over a 3-month intervention period, with assessments based on bladder diary parameters and system usage metrics.
Results: The CBISs successfully implemented an adaptive, closed-loop behavioral rehabilitation framework that dynamically tailored training recommendations according to individual voiding patterns, fluid intake behaviors, and adherence signals. Feasibility outcomes were favorable, with high system engagement observed throughout the intervention (mean usage frequency 5.2, SD 1.1 times per day). In exploratory pre-post analyses (n=16), consistent directional improvements were observed across multiple outcomes. Mean daytime urinary frequency decreased from 5.74 (SD 1.21) episodes per day to 4.69 (SD 1.08) episodes per day, while median nighttime urinary frequency declined from 1.8 (IQR 1.6-2.2) episodes per night to 1.0 (IQR 1.0-1.6) episodes per night. Median incontinence episodes were reduced from 7.0 (IQR 6.0-11.0) episodes per day to 4.0 (IQR 2.0-6.0) episodes per day. Objective urine leakage measured by the 1-hour pad test decreased from a median of 8.5 (IQR 4.0-19.0) g to 3.5 (IQR 2.0-9.0) g. Patient-reported symptom burden, assessed using the International Consultation on Incontinence Questionnaire–Short Form (ICIQ-UI SF), showed a median reduction from 14.0 (IQR 12.0-20.0) points to 9.0 (IQR 6.0-16.0) points. Although several within-participant changes were statistically detectable, effect magnitudes varied across individuals. Given the single-arm design, small sample size, and lack of a control group, findings are presented as exploratory and hypothesis-generating rather than confirmatory of clinical efficacy.
Conclusions: The CBISs represents the first RL-powered digital therapeutic system for PPI, enabling adaptive, evidence-based behavioral optimization. By addressing limitations of static rehabilitation protocols and declining adherence, it offers a scalable approach for personalized PPI management. Future multicenter trials are needed to confirm its clinical effectiveness.
Background: Surveillance is noted to be an important part of survivorship to detect recurrence and/or second primary lung cancer (SPLC) at a curable stage. However, current surveillance guidelines remain controversial, and the factors providers consider in clinical decision-making are neither well-defined nor consistently applied. Objective: In order to inform the qualitative protocol for a larger national study, this pilot study aims to understand factors that influence lung cancer surveillance and how providers view risk stratification as a potential tool to inform surveillance practices. Methods: Semi-structured interviews were conducted between October 2023 and July 2024 with purposively sampled providers involved in treating and surveilling lung cancer patients from the U.S. based Palo Alto Veterans Affairs medical center and Stanford medicine and its affiliate clinics. Providers were recruited through both email outreach and in-person invitations. Interviews were transcribed by an external transcription service and analyzed through a qualitative inductive content analysis approach to identify themes. Results: Eleven physicians and two Advanced Practice Providers (N=13) participated in interviews. The majority were from Medical specialties (61.5%), and the average years of practice as a provider was 9 years. Three themes were identified that describe clinicians' sentiments about current surveillance practices and how a risk stratification tool could be utilized in screening for recurrence and/or SPLC. Clinicians consider a variety of clinical and non-clinical factors (Category 1: factors that influence clinical decision making), and highlighted limits of a risk stratification tool, including concerns about generalizability, accuracy, and validity (Category 2: sentiments toward a hypothetical risk stratification tool). Lastly, concerns were raised about how delivering risk stratification data might impact patient anxiety, misinterpretation, and adherence to surveillance plans (Category 3: delivery of risk stratification data to patients). Conclusions: This qualitative analysis highlights the complexity of lung cancer surveillance decision-making and provider concerns about tool accuracy and delivery. While risk stratification tools may support surveillance decisions, their further development must address data quality, accuracy across diverse clinical and non-clinical risk factors, and effective patient-level data delivery. Doing so will facilitate practical implementation of risk stratification tools to improve surveillance of SPLC and recurrence.
Background: Artificial Intelligence (AI) is being rapidly integrated into oncologic care, yet little is known about how patients perceive these applications. Understanding patient perceptions is critical to ensuring AI applications align with their needs and preferences. Objective: This study aimed to evaluate oncology patients’ attitudes and beliefs on the use of AI across clinical touchpoints in cancer care. Methods: We conducted a cross-sectional survey study with adult oncology patients from September to December 2024. The survey assessed patients’ comfort with AI use across 8 clinical touchpoints of cancer care (e.g. screening, diagnosis, treatment) on a 5-point Likert scale (1=very uncomfortable to 5=very comfortable). Patients also rated their concerns about AI, including potential harms related to its use (e.g. medical errors, privacy breaches), on a 3-point Likert scale (1=not concerned to 3= very concerned). Results: Of 383 patients approached, 330 (86.2% response rate) participated; 184 (55.9%) were male, 162 (49.4%) age 65 or older, 35 (10.8%) Black, 40 (12.1%) Hispanic or Latino, and 233 (72.6%) were actively receiving cancer treatment. Patients were most comfortable with AI use in cancer screening (80.2%), and supportive care applications, including exercise (78.2%), diet (74.8%), and herbs/supplements (72.4%). Patients were least comfortable with AI use to assist with diagnosis (70.4%), symptom management (67.5%), treatment planning (64.8%), and prognosis (61.5%). Nonetheless, about half (49.7%) were at least somewhat concerned with the use of AI in cancer care, most commonly loss of human interaction and medical errors. Conclusions: While the majority of oncology patients had a favorable view of AI in cancer care, nearly half had concerns about potential harms. Incorporating patient perspectives into AI development is essential for patient-centered and high-quality cancer care.
Background: Ostomy creation for cancer treatment negatively impacts the quality of life for both patients and caregivers. Hispanic cancer patients and caregivers often face additional challenges, including having limited access to supportive care programs. Objective: This study aimed to examine the experiences and preferences of Hispanic cancer patients and caregivers living with ostomies to inform the cultural adaptation of an existing intervention program and the development of Programa de AutoCuidado de Estoma (PACE). Methods: In this two-stage study, we used a mixed-methods design, starting with an initial survey followed by qualitative interviews to explore the lived experiences, needs, and intervention preferences of Hispanic patients and caregivers managing ostomy care. Braun and Clarke’s six-phase approach to thematic analysis was used to analyze the data. Subsequently, we applied Affinity Diagraming and Persuasive Systems Design principles to guide the design and development of PACE. Results: We recruited 14 Hispanic cancer patients and their caregivers managing an ostomy All participants completed the survey and participated in interviews until data saturation was reached. Three major themes emerged regarding the experience of ostomy care: perceptions of living with an ostomy, seeking support, and post-surgery challenges. Additionally, two primary themes were identified regarding user preferences for ostomy care interventions: evaluation of existing content, delivery format, and language and recommendations for delivery methods, timing, and the inclusion of family and peer support. These themes informed the development of a web-based, user-friendly, bilingual PACE intervention that integrates content visualization, cultural adaptations, and persuasive technologies — strategies designed to encourage user engagement. Conclusions: Our findings emphasize the importance of understanding the ostomy care experiences of Hispanic patients and caregivers and the need for culturally tailored health interventions to address the needs of underserved populations. This theory-guided study represents the first to integrate the Persuasive Systems Design framework into ostomy care intervention, highlighting the potential of such approaches to enhance patient and caregiver engagement and support in managing their health conditions. Based on the findings from our previous pilot feasibility study and current research, the PACE web-based intervention is ready for evaluation in a sufficiently powered clinical trial to evaluate its effects.
Background: Growing interest surrounds how internet search behaviours might provide digital signals of disease prior to diagnosis, for example when people search symptoms or potential remedies online. Internet browsing data offers novel opportunities for understanding response to symptoms, public health surveillance and early intervention in conditions such as cancer. However, the acceptability of using such sensitive data in medical research remains unclear, particularly among individuals at higher risk of health and digital exclusion, such as older adults and those from minority ethnic groups or with a lower socio-economic status. Objective: To explore the feasibility and acceptability of using internet browsing history data for health research. Methods: Participants were purposively sampled to ensure representation from groups at risk of digital and health inequalities via community organisations and charities. We conducted semi-structured and think-aloud interviews allowing participants to reflect on hypothetical research involving sharing their internet browsing data. The Adapted Theoretical Framework of Acceptability guided the interview structure and coding. Interviews were transcribed, coded in NVivo and thematically analysed. Patient and public involvement informed the study approach, participant-facing documents and interpretation of the findings. Results: Twenty participants (ten with a history of cancer and ten without) were included in the study representing a range of age, gender, ethnic and socioeconomic groups. Key themes focused on factors necessary for acceptability, including trust, transparency and control; and on the perceived feasibility and individual willingness to share internet browsing history for health research. Trust and transparency were fundamental to participants’ willingness to share data. Trust in researchers was key and would have to be earned through clear communication, ethical data handling and familiarity with a named research team. Privacy concerns were prominent, with participants wanting control over what was shared, particularly regarding non-health-related information (such as details related to banking and finance) or activity related to others (such as children, friends and family). Potential (mis)use of data beyond the original research purpose caused more concern than the nature of the shared data itself. Digital literacy varied; many expressed concerns over the technical aspects of sharing data. Participants also doubted that their individual internet browsing history would have sufficient value, for example as they chose not to search for health information due to the prevalence of misinformation. However, they described wider benefits arising from internet browsing history research, such as potential advancements in early detection and opportunities to promote credible online sources. Conclusions: Participant recommendations balanced privacy concerns against the potential of internet history data for early diagnosis and health research. The study highlights ethical and inclusive approaches to health research using internet browsing history. Future researchers should consider defining the scope of health specific data filters; providing user-friendly information and guidance for study participants; and ensuring that participants are able to contact research team members to build trust and facilitate data sharing.
Background: Patients with pediatric cancer often experience reduced physical activity (PA) due to treatment-related fatigue, functional limitations, and lack of structured exercise programs. Digital health solutions, including wearable sensors and augmented reality (AR)-based interventions, may offer new possibilities for monitoring and improving PA in this population.
Objective: This scoping review aims to address existing research gaps by identifying the instruments—both conventional and digital—used to monitor PA in patients with pediatric cancer during treatment. In addition, this study examines PA monitoring methods, identifies the variables collected, and explores the applicability of digital health solutions in facilitating PA engagement among patients with pediatric cancer.
Methods: In accordance with the Joanna Briggs Institute methodology, a systematic search was conducted across 8 scientific databases—ProQuest, Web of Science, EBSCO Complete, Google Scholar, ScienceDirect, Scopus, MEDLINE (PubMed), and Cochrane—on April 18 and 19, 2024. Studies were screened using the Rayyan AI-assisted review tool based on predefined inclusion criteria targeting children aged 7-19 years who were undergoing cancer treatment or were within 2 years posttreatment. Eligible studies included clinical trials and observational studies that examined objective (eg, wearable sensors) and subjective (eg, questionnaires and self-reports) approaches to PA monitoring. Keywords and controlled vocabulary (eg, MeSH [Medical Subject Headings] terms) were identified through a review of relevant literature. Data were extracted systematically to capture study characteristics, intervention types, and outcome measures. Extracted data were charted and synthesized narratively to identify patterns, technological applications, and research gaps in PA monitoring among patients with pediatric cancer.
Results: Twelve studies met the inclusion criteria and employed a range of PA monitoring tools. Digital health solutions, including Actical and Garmin VivoFit 3 devices, were used in 5 studies to assess step counts, gait cycles, and movement intensity. Self-reported measures were identified in 11 studies, most commonly the Activities Scale for Kids and the Pediatric Quality of Life Inventory-Multidimensional Fatigue Scale, which provided insights into mobility and fatigue. Despite their feasibility, subjective assessments were limited by recall bias and motivational factors. Although digital health solutions—such as wearable sensors, gamification, and mobile applications—showed potential to improve PA adherence, their application remains underutilized, and evidence regarding their integration in pediatric oncology is limited.
Conclusions: Existing objective and subjective methods for monitoring PA provide valuable insights; however, gaps remain in the use of interactive digital health solutions, such as AR-based interventions, for PA monitoring and engagement. Future research should focus on integrating digital tools that not only track PA but also actively engage patients, enhance motivation, and support rehabilitation across both clinical and home settings.
Background: Patients receiving systemic anti-cancer therapy can deteriorate rapidly between appointments, yet acute oncology services often rely on reactive helplines with limited symptom visibility....
Background: Patients receiving systemic anti-cancer therapy can deteriorate rapidly between appointments, yet acute oncology services often rely on reactive helplines with limited symptom visibility. Objective: To evaluate the feasibility, safety, and workflow integration of OncsCare, a digital symptom triage platform mapping patient-reported symptoms to UK Oncology Nursing Society (UKONS) acuity tiers with episode-based clinician review. Methods: This 10-week service evaluation (July–September 2025) implemented OncsCare within a UK tertiary acute oncology service. Patients completed daily symptom check-ins mapped to UKONS-informed green/amber/red tiers. Alerts were grouped into episode-level triage events using prespecified rules (48-hour window, symptom-domain continuity) to represent operational workload. Outcomes included engagement, alert distribution, escalation pathways, review timeliness, and safety signals via structured case-finding. Results: Thirty-two patients participated (none withdrew). Daily check-in completion rate was 91.7% (1444/1574 expected patient-days). From 362 amber/red alerts, 62 episodes were generated; 38.7% (24/62) were clinically actionable, resulting in telephone management (50%), acute care assessment (37.5%), emergency referral (8.3%), or admission (4.2%). Median review time for in-hours red alerts was 47 minutes. Predefined safety case-finding identified no intervention-attributable safety signals. Patients reported increased home reassurance (85%) and clinicians reported improved situational awareness without increased workload. Conclusions: UKONS-informed digital triage with episode-based review demonstrated feasibility and safety in routine acute oncology care. This operational model addresses alert fragmentation and supports multicentre evaluation. Clinical Trial: Not applicable
Background: Artificial intelligence (AI) has reached expert-level performance across many areas of medical imaging, yet this progress has not translated proportionally into improvements in patient out...
Background: Artificial intelligence (AI) has reached expert-level performance across many areas of medical imaging, yet this progress has not translated proportionally into improvements in patient outcomes. While deep learning models excel at pixel-level pattern recognition, their impact on clinical decision-making, workflow efficiency, and patient-centered care remains poorly characterized Objective: This structured narrative review synthesizes evidence from high-quality studies (2018–2025) to evaluate whether imaging AI systems meaningfully improve patient outcomes beyond diagnostic accuracy. The review critically examines clinical integration, workflow implications, ethical considerations, and the persistent gap between algorithmic performance and patient-centered benefit. Methods: A structured search of PubMed, Scopus, IEEE Xplore, and Web of Science (2018–October 2025) identified empirical studies applying AI to human medical imaging and reporting both diagnostic metrics and real-world clinical, workflow, or patient-centered outcomes. Studies were screened independently by two reviewers, and data were extracted using predefined categories : model type, dataset characteristics, validation strategy, performance metrics, workflow impact, patient outcomes, and ethical considerations. Results: Ten high-quality studies met the inclusion criteria. Across domains (ophthalmology, mammography, echocardiography, CT, PET/CT, and chest radiography), AI models achieved strong diagnostic performance (pooled mean AUC = 0.91 ± 0.03). However, only 30% of studies reported measurable patient impact and 20% reported workflow improvements. External validation often revealed 5–10% performance degradation, and only four systems were deployed in routine care. Ethical analyses showed emerging concerns regarding bias, explainability, and trustworthiness particularly related to racial inference from imaging data. Conclusions: Medical imaging AI has matured algorithmically but remains clinically immature. Achieving true patient-centered benefit requires shifting from model-centric development to systems-level innovation: multimodal integration, explainable AI, human-in-the-loop designs, equity-aware training, and prospective clinical evaluation. AI will advance from “seeing the organ” to “understanding the patient” only when technical performance aligns with clinical workflows, ethical oversight, and human experience.
Background: Although effective, current CAR-T production methods — centralized, manual, and complex — are cost-intensive, time-consuming, and prone to variability. AIDPATH proposes a decentralized...
Background: Although effective, current CAR-T production methods — centralized, manual, and complex — are cost-intensive, time-consuming, and prone to variability. AIDPATH proposes a decentralized, automated alternative that integrates patient-specific data, optimizes resource use, and potentially improves cell viability, manufacturing efficiency, and patient outcomes. Objective: The aim of this study was to compare AIDPATH-produced CAR-T therapy to both Cilta-Cel and standard of care (SoC) for triple-class refractory multiple myeloma (MM) patients, over a 40-year time horizon in Germany from the hospital perspective. Methods: A partitioned survival model reflecting 3 health states (progression-free disease, progressed disease, and death) was used. The analysis used clinical trial data for Cilta-Cel, real-world data for SoC, and estimated parameters for AIDPATH, due to the developmental status of the platform. The primary outcome was the incremental cost effectiveness ratio, secondary outcomes included sensitivity and scenario analyses. Results: AIDPATH was dominant compared to both Cilta-Cel and SoC. Most costs for CAR-T therapies were driven by acquisition and adverse events. Sensitivity analyses showed the results were most influenced by discount rates and assumptions about progression-free survival. Scenario analyses, including reduced adverse events and shorter vein-to-vein time for AIDPATH, further supported its cost-effectiveness. Conclusions: This is the first study to assess the cost-effectiveness of CAR-T product generated with AI support in Germany from the hospital perspective. AIDPATH was found to be a cost-effective alternative to both Cilta-Cel and SoC, making it a promising option for future implementation. While further data are needed, this study provides valuable guidance for health care stakeholders, reimbursement discussions, and future research.
Background: Progression-free survival (PFS) is a critical endpoint in oncology, yet real-world applications of individualised, explainable machine-learning (ML) predictions remain limited. Objective:...
Background: Progression-free survival (PFS) is a critical endpoint in oncology, yet real-world applications of individualised, explainable machine-learning (ML) predictions remain limited. Objective: This study aims to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate cancer cohort in Brunei Darussalam. Methods: We analysed a retrospective cohort of 212 patients (478 longitudinal observations) treated at the Brunei Cancer Centre (January 2018 to December 2024). Clinical, laboratory, and treatment data were harmonised, with missing values imputed via Extremely Randomised Trees. Longitudinal patterns were captured using a recurrent autoencoder to generate latent representations. We compared four modelling approaches: Cox Proportional Hazards (CPH), Random Survival Forests (RSF), Gradient Boosting Survival (GBS), and Deep Neural Network Survival models. Performance was evaluated using time-dependent AUC, Harrell’s C-index, and Integrated Brier Score (IBS), with SHAP (Shapley Additive exPlanations) used for interpretability. Results: RSF demonstrated improved discriminative performance and balanced calibration, achieving a C-index of 0.906 and AUCs of 0.941 at both 4 and 5 years (IBS = 0.0698). In contrast, the traditional CPH model performed poorly (C-index 0.531; AUC 0.706 at 4 years). Deep survival (AUCs of 0.941 at 4 years and 0.941 at 5 years, C-index 0.719, IBS=0.0590) and GBS (AUCs of 0.765 at 4 years and 0.833 at 5 years, C-index 0.844, IBS=0.0887) models showed moderate performance. SHAP analysis identified sodium (Na), alanine aminotransferase (ALT), MCH, platelet count, and specific treatment categories as key drivers of increased progression risk. Conclusions: Tree-based ensemble approaches, particularly RSF integrated with SHAP, offer high accuracy for personalised risk stratification in prostate cancer. These findings highlight the potential of explainable ML to enhance clinical decision-making. However, external validation in larger multi-institutional, multi-omics dataset is required before routine clinical implementation.
