Research Appraisalother

Internet of Things-Enhanced Mathematical Oncology: Conceptual Framework for Adaptive Cancer Care Modeling

JMIR cancerOnasanya, Adeniyi, Kyabaggu, Ramona, Hepting, Daryl13 July 2026DOI

Clinical Snapshot

30CEBM

Evidence: Insufficientother

PICO Framework

P — Population	Patients with cancer (broadly defined); oncology health systems and clinical decision-making contexts
I — Intervention	A conceptual framework integrating real-time Internet of Things (IoT) data as dynamic inputs into adaptive mathematical models to simulate cancer dynamics
C — Comparator	No active comparator; the framework is proposed against a backdrop of current empirical research limitations and conventional oncology modelling approaches
O — Outcomes	Theoretical optimisation of oncology services, improved simulation of cancer dynamics, enhanced clinical decision-making under uncertainty, and personalised treatment adaptation — all assessed conceptually rather than empirically

Bottom Line

This paper proposes a conceptual framework for integrating IoT-generated real-time data into adaptive mathematical models for cancer care — an intellectually interesting idea that addresses a genuine gap in digital oncology. However, it is an entirely theoretical contribution with no empirical data, no implemented models, no simulation outputs, and no validation strategy. The framework remains at a high level of abstraction, with no specification of cancer type, IoT device class, or mathematical model family. Clinical translation is undefined, and important considerations including patient safety, data governance, regulatory compliance, and implementation costs are not substantively addressed. For senior clinicians, this paper is best read as a horizon-scanning piece identifying a potential future research direction rather than actionable evidence. It does not change current practice. Meaningful clinical relevance will require prospective development of the mathematical models described, empirical validation with real IoT datasets, and rigorous evaluation against patient outcomes. Researchers in digital oncology and health informatics may find the conceptual architecture a useful starting point for grant development or collaborative research design.

Evidence: Insufficient

Key Findings

Effect Size: Not applicable — no quantitative results presented
Primary Outcome: Proposal of a conceptual framework integrating real-time IoT data into adaptive mathematical models for cancer dynamics simulation — no empirical outcomes reported
Nnt Or Sensitivity: Not applicable — no clinical performance metrics, diagnostic accuracy, or treatment effect estimates are provided
Confidence Interval: Not applicable — no statistical analysis conducted

Clinical Application

Currently low for direct clinical implementation. The framework requires substantial further development including: mathematical formalisation of model components, selection and validation of specific IoT data streams, integration with existing electronic medical record systems, regulatory approval pathways, and prospective clinical evaluation. The authors acknowledge data paucity as a central limitation, which paradoxically also constrains framework validation. In the Australian context, this framework has limited immediate clinical applicability. The Therapeutic Goods Administration (TGA) regulates software as a medical device (SaMD) under the Medical Device Regulations 2002, and any IoT-integrated adaptive oncology decision-support tool would require conformity assessment before clinical deployment. Cancer Australia and the Australian Commission on Safety and Quality in Health Care have published digital health frameworks, but none currently endorse IoT-driven adaptive mathematical modelling for oncology. The PBS does not fund IoT devices for cancer monitoring outside specific programs. RACGP and COSA (Clinical Oncology Society of Australia) guidelines do not yet address this technology class. Australian oncology centres with existing digital health infrastructure (e.g., Peter MacCallum Cancer Centre, Chris O'Brien Lifehouse) may represent future pilot sites, but no Australian-specific data or context is referenced in the paper. Theoretically applicable to adult patients with any cancer type receiving care in settings with IoT infrastructure capacity; most relevant to oncology centres with digital health investment and data science capability

Abstract

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.

References

1.Onasanya, A., Kyabaggu, R., & Hepting, D. (2026). Internet of Things-Enhanced Mathematical Oncology: Conceptual Framework for Adaptive Cancer Care Modeling. JMIR Cancer. https://doi.org/10.2196/73997

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