Modern photovoltaic technologies are increasingly evaluated not only in terms of power conversion efficiency and cost, but also with respect to resource origin, toxicity, recyclability, and overall life-cycle impacts. Within this broader sustainability framework, bio-based and bio-inspired materials derived from biomass or mimicking biological structures have emerged as promising candidates for a wide range of photovoltaic components, including active layers, interfacial modifiers, substrates, encapsulants, and natural dyes. This review provides a layer-by-layer overview of such materials implemented or proposed in dye-sensitized, organic, perovskite, biohybrid, and silicon solar cells, linking their molecular structures and optoelectronic properties to representative device performances and key degradation pathways. Cross-cutting challenges related to moisture and thermal stability, barrier performance, feedstock variability, and the risk of “greenwashing” are highlighted, emphasizing that sustainability claims must be supported by quantitative metrics such as life-cycle assessment, circularity indicators, and durability studies. Finally, we outline promising research directions in molecular engineering, hybrid biosynthetic architectures, and advanced encapsulation concepts that could enable bio-based materials to make a meaningful contribution to low-impact photovoltaic technologies.

Purpose: Artificial intelligence is increasingly developed and deployed to support radiological image interpretation, triage, segmentation and workflow prioritisation. However, high performance reported in retrospective studies does not necessarily translate into safe and useful deployment in routine practice. This narrative review examines why this translational gap persists and what radiology departments, researchers and developers should address before clinical implementation. Methods: We synthesised methodological, empirical and regulatory literature on the development, validation, deployment and governance of radiology AI. Radiology was used as the primary setting, with adjacent imaging fields discussed only where they illustrate broader mechanisms relevant to medical image analysis. Results: Recurrent barriers include limited data quality and representativeness, scanner- and protocol-related domain shift, shortcut learning, hidden stratification, insufficient external and prospective validation, poor calibration, over-reliance on global metrics and weak workflow integration. Model performance can be shaped by scanner protocols, reconstruction methods, local reporting practices, patient selection and institutional workflows beyond the underlying pathology. In Europe, GDPR-based data governance, the Medical Device Regulation, the AI Act and the European Health Data Space further shape clinical deployment. Conclusions: Emerging approaches, including foundation models, generative AI, multimodal systems, federated learning and local adaptation, may address selected technical constraints, but they introduce new uncertainties and do not replace independent validation in the intended clinical setting. Radiology AI should be evaluated not only as an algorithmic model, but as a clinical decision-support component embedded in radiology practice, requiring external and preferably prospective validation, subgroup analysis, calibration assessment, human oversight, workflow integration and post-deployment monitoring.