Physics-Informed Machine Learning in Biomedical Science and Engineering
Clinical Snapshot
PICO Framework
| P — Population | Biomedical research applications and complex biological systems |
| I — Intervention | Physics-informed machine learning frameworks (PINNs, NODEs, Neural Operators) |
| C — Comparator | Conventional black-box machine learning approaches |
| O — Outcomes | Model performance, physical interpretability, data efficiency, and applicability to biomedical problems |
Bottom Line
This narrative review examines physics-informed machine learning (PIML) as an emerging approach for biomedical modeling that combines physical laws with data-driven methods. The authors review three main frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators, highlighting applications in biomechanics, medical imaging, pharmacokinetics, and physiological modeling. While PIML shows promise for addressing data scarcity and improving model interpretability compared to conventional black-box approaches, the review lacks systematic methodology and quantitative assessment. The field appears promising for complex biomedical systems where physical constraints are important, but implementation requires substantial computational expertise. Clinical translation remains early-stage, with significant challenges in uncertainty quantification and generalization still to be addressed.
Key Findings
Effect Size: Not quantified - narrative review
Primary Outcome: PIML frameworks show promise for biomedical applications requiring physical interpretability
Nnt Or Sensitivity: Not applicable - methodological review
Clinical Application
Requires significant computational expertise and resources; limited by current software accessibility Relevant to Australian medical research institutions and biomedical engineering programs; potential applications in TGA regulatory science and health technology assessment Biomedical researchers, clinical engineers, and computational biologists working with complex physiological systems
Abstract
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging, among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
References
- 1.Ahmadi, N., Cao, Q., Humphrey, J. D., & Karniadakis, G. E. (2026). Physics-Informed Machine Learning in Biomedical Science and Engineering. Annual Review of Biomedical Engineering, 28. https://doi.org/10.1146/annurev-bioeng-110824-124907
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