Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as “leader collusion”, in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

Introduction: Adverse drug events (ADEs) remain a major cause of preventable healthcare complications due to incorrect pill identification, dosage errors, and confusion between visually identical pills, particularly among older adults, visually impaired individuals, and people with limited health literacy. Recent advances in artificial intelligence and computer vision have enabled automated pill recognition systems. However, many existing methods address detection, classification, and imprint recognition as isolated tasks without providing a lightweight unified framework suitable for real-time healthcare deployment. Methods: This study presents a lightweight hybrid framework for multi-pill and multi-attribute recognition to support pharmacovigilance and artificial intelligence-driven clinical decision support. The proposed framework integrates pill detection using YOLOv26n, MobileNetV4-ConvSmall-based classification, EasyOCR-based imprint recognition, and metadata retrieval into a unified prediction pipeline. A customized benchmark dataset containing 400 pill classes, each with 50 images, for a total of 20,000 images, was used for classification. Each image has two pills, comprising 40,000 manually labeled pill instances used for detection. Comparative experiments, functional comparisons with existing pill recognition systems, and cross-device qualitative evaluations were conducted to assess framework performance, robustness, and deployment feasibility. Results: YOLOv26n achieved a precision of 0.963, a recall of 0.982, mAP@50 of 0.989, and mAP@50-95 of 0.981, while MobileNetV4-ConvSmall achieved a class accuracy of 99.60% with higher performance in pill shape and color accuracy. Comparative optical character recognition (OCR) analysis shows that EasyOCR achieved more reliable imprint recognition performance than TesseractOCR and TrOCR. Comparative framework evaluation demonstrated that the YOLOv26n with MobileNetV4-ConvSmall combination achieved the high overall recognition performance, obtaining a Top-1 accuracy of 89.83% and a Top-5 accuracy of 99.49%. Cross-device qualitative evaluation using smartphone cameras and direct laptop uploads demonstrated robust performance under varying image noise conditions. Discussion: The proposed framework demonstrates the potential of lightweight deep learning architectures for scalable medication identification and AI-assisted pharmacovigilance applications. By integrating detection, classification, imprint recognition, and metadata retrieval into a unified framework, the proposed system has the potential to support medication identification and improve assistive healthcare workflows. The final clinical judgment and medication verification should remain under the supervision of a healthcare professional.

The clinical translation of safe and effective therapeutic methods for brain cancer treatment is a major challenge that persists in modern medicine. The insufficient drug delivery into the regime of the affected brain tissue due to blood–brain barrier (BBB) restriction leads to a poor prognosis of the disease. However, an alternative strategy using biomaterials like exosomes can offer advancements in the treatment of brain cancer. Exosomes are a type of extracellular microvesicle with a diameter of 30–100 nm, principally functioning as intercellular mediators in the cell signaling process. Due to their biological origin, exosomes demonstrate a significant ability to cross the BBB and possess enhanced biocompatibility, high drug-loading capacity, and low immunogenicity. With the innate property of biomolecule delivery, exosomes also offer enhanced cellular uptake, rendering them exceptional in drug delivery systems. Herein, we focus on the anticancer and diagnostic applications of exosomes for brain cancer therapeutics. The enhancement of the physico-chemical properties of various cell-derived exosomes can be effectively used as a prime drug delivery agent in most treatment strategies. The biphasic and fast drug release in acidic pH of the tumor microenvironment by exosome-mediated drug delivery system contributes to passive targeting, which is often considered advantageous over other drug delivery platforms. These characteristic features are likely to enhance the therapeutic potential and efficacy of the treatment. The exosome loaded with the drug acts as an efficient biomaterial to surpass the BBB, followed by efficient cellular uptake, leading to cytotoxicity in glioblastoma cells. In this review, we summarize the recent updates in theranostic and prognostic strategies using exosomes as a mediator and their prevalence in biomedical applications, with a focus on brain cancer diseases.

Healthcare industry is a stage which is presented with tremendous innovative headways consistently. Parkinson disease (PD) has become a critical overall ge