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All Journal Journal of Advanced Health Informatics Research
Elsa Wulandari, Annastasya Nabila
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Computer Science & IT Control & Systems Engineering Engineering Medicine & Pharmacology Public Health

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Smart Contracts for Data Sharing in Drug Development a Systematic Review of Security and Transparent Measurement Elsa Wulandari, Annastasya Nabila; Purwono
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.288

Abstract

This systematic literature review explores the role of smart contracts in improving data sharing for drug development, with an emphasis on security and transparency. Using blockchain technology, smart contracts offer a decentralized tracking mechanism for pharmaceutical supply chains, addressing challenges related to drug authentication and supply chain optimization. The review examined 52 studies using the PRISMA methodology, highlighting the automation of data exchange, reduced reliance on external parties, and acceleration of operational processes. Advanced encryption and strict access controls in smart contracts strengthen data security, ensuring patient confidentiality and compliance with medical data regulations. Despite technical and regulatory barriers, smart contracts promise significant improvements in operational efficiency, transparency, and collaboration among stakeholders in drug development. This study emphasizes the need for standardized protocols, further empirical research, and strategic implementation to fully leverage the potential of smart contracts in the pharmaceutical industry. Integration of these technologies can accelerate clinical trials and improve data reliability, thereby enhancing the safety and effectiveness of the drug development process.
Comparison of Transfer Learning Performance in Lung and Colon Classification with Knowledge Distillation Elsa Wulandari, Annastasya Nabila; Yudhistira , Aimar; Purwono; Sharkawy , Abdel-Nasser
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.289

Abstract

This research aims to apply the knowledge distillation method to medical image classification, specifically in the case of lung and colon image classification using various transfer learning models. Knowledge distillation allows the transfer of knowledge from a larger model (teacher) to a smaller model (student), which enables more efficient model building without sacrificing accuracy. In this research, the DenseNet169 model is used as the teacher model. The student model uses several alternative transfer learning architectures such as DenseNet121, MobileNet, ResNet50, InceptionV3, and Xception. The data used consists of 25,000 histopathology images that have been processed and divided into training, validation, and test data. Data augmentation was performed to enlarge the dataset from 750 to 25,000 images, which helped improve the performance of the model. Model performance evaluation was performed by measuring the accuracy and loss value of each student model compared to the teacher model. The results showed that the student models generated through the knowledge distillation process performed close to or even exceeded the teacher model in some cases, with the Xception model showing the highest accuracy of 96.95%. In conclusion, knowledge distillation is effective in reducing model complexity without compromising performance, which is particularly beneficial for implementation on resource-constrained devices.
Co-Authors Purwono, Purwono Sharkawy , Abdel-Nasser Yudhistira , Aimar