Due to the health information asymmetry, the upsurge of Patient Online Communities (POCs) and Patient Social Media groups has increased the importance of electronic word-of-mouth (eWOM) in health care, influencing individuals’ health decisions, as well as a medical organization’s image. This study investigates the association between the multidimensional perceived value of patients and their eWOM intentions in health care services, based on Art Weinstein’s adapted Perceived Value framework. According to this framework, perceived value comprises perceived quality, perceived service outcome, non-monetary costs, and organizational image. Data were collected from 210 Cardiology patients and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings of this study revealed that perceived value is positively associated with eWOM intentions within this sample, which highlights the practical importance of enhancing patient experience. As perceived value improves, it may be associated with increased patient-generated content in the form of eWOM. This study provides practical insights and contributes to the understanding of the patients’ perceived value in engaging in health-related eWOM.

Invasive fungal infections cause approximately 1.5 billion cases each year, with an estimated 1.6 million deaths. Classic diagnostic methods, such as culture, direct microscopy, and antigen testing, largely rely on professional experience and are often too slow. The field of fungal diagnostics is increasingly using deep learning (DL), a class of artificial intelligence based on multilayered neural architectures. Recent developments in DL applications to clinical mycology are examined in this review, including automated colony morphology analysis, MALDI-TOF mass spectrometry spectral classification, Raman spectroscopy, microscopy, molecular diagnostics, and antifungal resistance prediction. Aspergillus, Candida, Cryptococcus neoformans, Pneumocystis jirovecii, and dermatophytes are among the important pathogens evaluated for validated performance benchmarks. We also address the main barriers to clinical implementation, which include a lack of large annotated datasets, cross-site generalizability, interpretability requirements, and regulatory frameworks. We conclude that, depending on carefully selected open-access datasets and future clinical validation, DL has transformative potential across several mycological diagnostic platforms.