JMIR Cancer

Mobile health (mHealth) apps offer new opportunities to support cancer survivors in managing their health, adopting healthier lifestyles, and increasing awareness of recurrence symptoms. However, despite their potential, little is known about survivors’ use of such tools in routine follow-up care, or which factors are associated with their engagement.

Accelerating the transition to value-based health care (VBHC) is essential to ensure sustainable care delivery. VBHC maximizes patient value by optimizing outcomes, controlling costs, and leveraging data to improve quality and patient-doctor communication. Integrating real-world data in health care is important to achieve informed decisions, especially in palliative care, where choices are complex.

Delayed cancer diagnosis leads to poorer outcomes. Unintended weight loss (UWL) is a nonspecific symptom associated with cancer and other serious conditions, which can make it complex to identify the underlying cause. Clinical decision support systems (CDSSs) can provide evidence-based recommendations to facilitate timely investigation and diagnosis.

Cardiovascular disease is a leading noncancer cause of morbidity and mortality among individuals diagnosed with cancer. Although modern cancer therapies have improved survival, many are associated with cardiotoxic effects that increase the risk of cardiovascular complications. Early identification of cardiovascular symptoms and signs may support timely clinical management. However, tracking cardiovascular health during cancer care can be complex for both patients and medical team participants, and important indicators, such as blood pressure changes, heart rate variability, fluid retention, chest pain, or new-onset fatigue, may be missed between clinic visits. Mobile health technologies offer potential tools to facilitate remote monitoring, yet behavioral factors influencing cardiovascular tracking in cardio-oncology remain insufficiently understood.

Emerging evidence indicates that inflammation plays a crucial role in cancer prognosis. Inflammatory response biomarkers are recognized as promising prognostic factors for mortality in patients with cancer.

Large language model (LLM)–based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer and informal caregivers remains limited.

Chemotherapy-induced alopecia is among the most psychologically distressing adverse effects of systemic cancer therapy. Although scalp cooling is increasingly used to mitigate hair loss, it is still largely perceived as a cosmetic intervention. Its broader psychological relevance and the biological basis of treatment success, particularly the preservation of follicular integrity under ongoing cytotoxic exposure, remain insufficiently explored.

Lung cancer remains a major contributor to cancer mortality in sub-Saharan Africa (SSA), where late diagnosis, driven by low awareness, sociocultural barriers, and health system constraints, limits effective treatment. Despite the growing burden, evidence on patients’ quality of life (QoL) and symptom experience in SSA is limited.

Tele-oncology addresses geographic barriers to cancer care, but implementation challenges persist in rural settings. AI-enhanced predictive analytics offer opportunities for optimizing deployment through personalized, data-driven strategies; however, evidence in rural tele-oncology contexts remains limited, and critical equity considerations remain underexamined.

The Internet of Things (IoT) is transforming various industries, including health care. IoT-based systems are increasingly prevalent in consumer health applications, while intelligent or smart devices equipped with sophisticated sensors are gaining recognition for their potential to improve clinical care practice and decision-making. Cancer care is a particularly promising area for IoT applications, enabling real-time and personalized interventions. However, empirical research on the effects of IoT in this field is limited due to the complexities inherent in cancer as a dynamic disease and the paucity of IoT-generated data available for research. This presents an opportunity to apply mathematical modeling to understand the effects of IoT under various scenarios. These analytical and “in silico” mathematical approaches are instrumental with limited data. Such models support the analysis of treatment uncertainty and patient response while balancing patient preferences, clinical outcomes, and system-level constraints. Grounded in mathematical oncology and health informatics, this paper proposes a conceptual framework that integrates real-time IoT data as dynamic inputs into adaptive mathematical models to simulate cancer dynamics. By exploring applications across multiple levels of analysis, the study demonstrates how IoT-enhanced mathematical models could inform implementation and optimize oncology services, addressing a critical gap in current research.

Our study describes the development and evaluation of a retrieval-augmented generation–based large language model to improve the quality of responses to provider questions about herbs and dietary supplements.

Electronic patient-reported outcomes (ePROs) have demonstrated greater sensitivity in detecting adverse events than clinician-reported outcomes. However, conventional care faces challenges related to recall bias, and evidence from real-world populations of older adults is limited. This study evaluates the feasibility of an ePRO system integrated with ecological momentary assessment and ecological momentary intervention (EMI) to facilitate a concordance model within the framework of digital transformation in health care.