AI-Assisted Medical Imaging Reconstruction: Physics-Informed Networks, Trustworthiness, and Clinical Translation

Abstract

The objective of this review is to synthesize current advances in physics-informed neural network architectures for medical imaging reconstruction, evaluate trustworthiness and interpretability considerations, and examine pathways toward clinical translation. A qualitative literature review was conducted on 16 selected high-impact articles focused on AI-assisted image reconstruction, physics-informed neural networks, and trustworthiness in medical imaging. Articles were sourced from peer-reviewed journals, screened for relevance, and analyzed using NVivo 14 software. Thematic coding was applied to identify key concepts, subthemes, and overarching categories until theoretical saturation was reached, enabling a systematic synthesis of architectures, trust metrics, and translational considerations. Five major themes emerged: (1) physics-informed neural networks enhance reconstruction fidelity and generalizability by integrating physical priors and forward models; (2) hybrid deep learning architectures combining physics and data-driven components demonstrate superior performance in undersampled or noisy imaging; (3) trustworthiness features—interpretability, uncertainty quantification, robustness, fairness, and human-in-the-loop mechanisms—are critical for clinical adoption; (4) translation to clinical practice remains limited, with few studies addressing multicenter validation, workflow integration, regulatory compliance, and safety; and (5) future research directions include federated and privacy-preserving learning, physics–AI co-design, standardized benchmarking, and improved human–AI interaction. The synthesis indicates that while technical innovations are promising, systemic challenges in trust, usability, and regulatory readiness persist. Physics-informed neural networks represent a significant advancement in AI-assisted medical imaging reconstruction, offering improved fidelity and interpretability. However, adoption in clinical settings requires concerted efforts to embed trustworthiness, validate across diverse datasets, align with regulatory standards, and integrate with clinical workflows. The review provides a roadmap for researchers, clinicians, and regulators to navigate the integration of physics-informed AI reconstruction into practice safely and effectively.