Although blockchain is acknowledged as one of the most important technologies to lead the fourth industrial revolution, major technical challenges regarding security breach and privacy issues remain. This issue is particularly sensitive in applied medical fields where personal health information is handled within the network. In addition, contemporary blockchain-converged solutions do not consider restricted medical data regulations that are still obstacles in many countries worldwide. This implies a crucial need for a system or solution that is suitable for the healthcare sector. Therefore, this article proposes the development of a dynamic consent medical blockchain system called DynamiChain, based on a ruleset management algorithm for handling health examination data. Moreover, medical blockchain-related studies were systematically reviewed to prove the novelty of DynamiChain. The proposed system was implemented in a scenario where the exercise management healthcare company provided health management services based on data obtained from the data provider’s hospital. The proposed research is envisioned to provide a widely compatible blockchain medical system that could be applied in future healthcare fields.

Detecting and analyzing Alzheimer's disease (AD) in its early stages is a crucial and significant challenge. Speech data from AD patients can aid in diagnosing AD since the speech features have common patterns independent of race and spoken language. However, previous models for diagnosing AD from speech data have often focused on the characteristics of a single language, with no guarantee of scalability to other languages. In this study, we used the same method to extract acoustic features from two language datasets to diagnose AD. Using the Korean and English speech datasets, we used ten models capable of real-time AD and healthy control classification, regardless of language type. Four machine learning models were based on hand-crafted features, while the remaining six deep learning models utilized non-explainable features. The highest accuracy achieved by the machine learning models was 0.73 and 0.69 for the Korean and English speech datasets, respectively. The deep learning models' maximum achievable accuracy reached 0.75 and 0.78, with their minimum classification time of 0.01s and 0.02s. These findings reveal the models' robustness regardless of Korean and English and real-time diagnosis of AD through a 30-s voice sample. Non-explainable deep learning models that directly acquire voice representations surpassed machine learning models utilizing hand-crafted features in AD diagnosis. In addition, these AI models could confirm the possibility of extending to a language-agnostic AD diagnosis.

Conventional personal health record (PHR) management systems are centralized, making them vulnerable to privacy breaches and single points of failure. Despite progress in standardizing healthcare data with the FHIR format, hospitals often lack efficient platforms for transferring PHRs, leading to redundant tests and delayed treatments. To address these challenges, we propose a decentralized PHR management system leveraging Personal Data Stores (PDS) and Decentralized Identifiers (DIDs) in line with the Web 3.0 model. Our system features secure interoperability and personal identification masking. Interoperability is achieved through DID digital certificates for verifying PDS addresses and a dynamic access key (AK) system to minimize credential exposure. Data de-identification, including anonymization and encryption, ensures privacy and prevents unauthorized access. We developed a prototype using the Solid open-source library and Hyperledger Aries protocol. Testing showed efficient performance, with DID validations and AK generation under one second, and data operations for 500 MB-sized PHRs completing in two seconds. De-identification processes were both effective and timely. The system demonstrated the ability to manage PHRs securely, empower users with control over their healthcare data, facilitate seamless and secure data transfer between patients and medical entities, and prevent exposure of sensitive information. This approach advances decentralized PHR management, supporting improved healthcare outcomes and patient experiences in the digital era.

The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms.

Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and load it according to the CDM model, considering the computing resources of each hospital. Accordingly, this study attempts to share experiences and recommend computing resource-allocation designs. Here, two types of servers were defined: combined and separated servers. In addition, three database (DB) setting types were selected: desktop application (DA), online transaction processing (OLTP), and data warehouse (DW). Scale, TPS, average latency, 90th percentile latency, and maximum latency were compared across various settings. Virtual memory (vmstat) and disk input/output (disk) statuses were also described. Transactions per second (TPS) decreased as the scale increased in all DB types; however, the average, 90th percentile and maximum latencies exhibited no tendency according to scale. When compared with the maximum number of clients (DA client = 5, OLTP clients = 20, DW clients = 10), the TPS, average latency, 90th percentile latency, and maximum latency values were highest in the order of OLTP, DW, and DA. In vmstat, the amount of memory used for the page cache field and free memory currently available for DA, OLTP, and DW were large compared to other fields. In the disk, DA, OLTP, and DW all recorded the largest value in the average size of write requests, followed by the largest number of write requests per second. In summary, this study presents recommendations for configuring CDM settings. The configuration must be tuned carefully, considering the hospital’s resources and environment, and the size of the database must consider concurrent client connections, architecture, and connections.

Glaucoma is one of the major diseases that cause blindness, which is incurable and irreversible, and it is essential to detect glaucoma vision deficits in treatment and check the progression of vision disorders in advance. In order to minimize the risk of glaucoma, it is necessary not only to diagnose and observe glaucoma but also to predict prognosis via indicators from Visual Field (VF) tests. However, information from the VF test cannot be directly used in clinical studies because most medical institutions store VF test sheets in Portable Document Format (PDF) or image files in different standards. We developed AI-based real-time VF big data digitizing systems that digitalize VF test images in real-time in two ways; Semi-AI and Full-AI digitizer. The Semi-AI digitizer detects the VF text area with actual coordinates derived from mouse handler system. Full-AI digitizer detects the VF text area with Faster Region Based Convolutional Neural Networks (RCNN). After detecting the text area, both systems extract texts with Recurrent Neural Network based Optical Character Recognition. Semi-AI and Full-AI digitizer post-processes the extracted text results with in-system algorithm and out-of-system algorithm, respectively. Both systems used 325,310 VF test sheets from a tertiary hospital and extracted a total of 5,530,270 texts. From the 100 randomly selected VF sheets, 3,400 texts were used for the validation. Semi-AI and Full-AI digitizer showed 0.993 and 0.983 of accuracy, respectively. This study demonstrates the effectiveness of AI applications in detecting text areas and the different implementation methodologies of the post-processing process. In detecting text area, Semi-AI may be better than Full-AI digitizer in terms of system speed and human labor labeling if the number of types to be classified is small. However, Full-AI digitizer is recommended because it allows detecting text area regardless of resolution and size of the VF sheets, as the types of real-world VF test sheets cannot be predicted, and the types become more unpredictable when extended to multi-hospital studies. For Post-preprocessing, Semi-AI methodology is recommended because Semi-AI produced higher results with less effort and considered the convenience of researchers by implementing them as in-system.