Artificial Intelligence (AI) is transforming smart healthcare by enhancing diagnostic precision, automating clinical workflows, and enabling personalized treatment strategies. This review explores the current landscape of AI in healthcare from two key perspectives: capability types (e.g., Narrow AI and AGI) and functional architectures (e.g., Limited Memory and Theory of Mind). Based on capabilities, most AI systems today are categorized as Narrow AI, performing specific tasks such as medical image analysis and risk prediction with high accuracy. More advanced forms like General Artificial Intelligence (AGI) and Superintelligent AI remain theoretical but hold transformative potential. From a functional standpoint, Limited Memory AI dominates clinical applications by learning from historical patient data to inform decision-making. Reactive systems are used in rule-based alerts, while Theory of Mind (ToM) and Self-Aware AI remain conceptual stages for future development. This dual perspective provides a comprehensive framework to assess the maturity, impact, and future direction of AI in healthcare. It also highlights the need for ethical design, transparency, and regulation as AI systems grow more complex and autonomous, by incorporating cross-domain AI insights. Moreover, we evaluate the viability of developing AGI in regionally specific legal and regulatory frameworks, using South Korea as a case study to emphasize the limitations imposed by infrastructural preparedness and medical data governance regulations.

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations.

Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions.

Background: The global livestock industry faces pressure to reduce antimicrobial usage while maintaining animal health and productivity. Non-pharmaceutical interventions (NPIs) including probiotics, prebiotics, phytogenics, essential oils, organic acids, and enzymes have emerged as alternatives to antibiotic growth promoters. Commercial success depends on consumer acceptance and willingness to pay (WTP) for products from animals raised using these approaches. Objective: This systematic review synthesized peer-reviewed literature examining consumer knowledge, attitudes, perceptions, and WTP toward animal products produced using NPIs or marketed as antibiotic-free (ABF) to identify a critical gap in existing research. Methods: Following PRISMA 2020 guidelines, four databases (PubMed, Web of Science, Scopus, and Google Scholar) were searched for peer-reviewed studies published from January 2020 to December 2024. Inclusion criteria encompassed original research examining consumer perspectives toward NPIs or antibiotic-free (ABF) animal products. Narrative synthesis was employed due to study heterogeneity. Results: From 847 records, 15 studies met inclusion criteria. A critical finding was that virtually no peer-reviewed research directly examines consumer perceptions of specific NPIs such as probiotics, prebiotics, phytogenics, organic acids, or enzymes as feed additives. The included studies predominantly examined ABF production generally (60%) without specifying alternatives employed. Europe accounted for 80% of studies, while Asia accounted for 20%. Consumer awareness of agricultural antibiotic use was consistently low across contexts. Attitudes toward ABF products were favorable with one study reporting WTP premiums of 18–20%. Health consciousness was the strongest predictor of acceptance. Conclusions: The review highlights that while substantial literature exists on ABF products, no studies examine consumer perceptions of specific non-pharmaceutical interventions. Future research should investigate consumer responses to intervention specific labeling and communication strategies.