Precision health data: Requirements, challenges and existing techniques for data security and privacy

Review learning: Real world validation of privacy preserving continual learning across medical institutions

When a deep learning model is trained sequentially on different datasets, it often forgets the knowledge learned from previous data, a problem known as catastrophic forgetting. This damages the model's performance on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applications based on transfer learning (TL). To overcome this, we introduce "review learning" (RevL), a low cost continual learning algorithm for diagnosis prediction using electronic health records (EHR) within a PPDL framework. RevL generates data samples from the model which are used to review knowledge from previous datasets. Six simulated institutional experiments and one real-world experiment involving three medical institutions were conducted to validate RevL, using three binary classification EHR data. In the real-world experiment with data from 106,508 patients, the mean global area under the receiver operating curve was 0.710 for RevL and 0.655 for TL. These results demonstrate RevL's ability to retain previously learned knowledge and its effectiveness in real-world PPDL scenarios. Our work establishes a realistic pipeline for PPDL research based on model transfers across institutions and highlights the practicality of continual learning in real-world medical settings using private EHR data.

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

Recent Advancements in Rubber Composites for Physical Activity Monitoring Sensors: A Critical Review

This review provides the latest insight (2020 to 2025) for composite-based physical activity monitoring sensors. These composite materials are based on carbon-reinforced silicone rubber. These composites feature the use of composite materials, thereby allowing the creation of new generation non-invasive sensors for monitoring of sports activity. These physical sports activities include running, cycling, or swimming. The review describes a brief overview of carbon nanomaterials and silicone rubber-based composites. Then, the prospects of such sensors in terms of mechanical and electrical properties are described. Here, a special focus on electrical properties like resistance change, response time, and gauge factor are reported. Finally, the review reports a brief overview of the industrial uses of these sensors. Some aspects are sports activities like boxing or physical activities like walking, squatting, or running. Lastly, the main aspect of fracture toughness for obtaining high sensor durability is reviewed. Finally, the key challenges in material stability, scalability, and integration of multifunctional aspects of these composite sensors are addressed. Moreover, the future research prospects are described for these composite-based sensors, along with their advantages and limitations.

Lesionia: a digital data management system to enhance collaborative management of epidemiological and clinical data of cutaneous leishmaniases patients

Digital Systems for Data Management (DSDM) have become a critical cornerstone in collaborative biomedical research and clinical trials involving multiple investigators, institutions, and populations. DSDM provide unique features that ensure that data meet the standards of FAIR (Findability, Accessibility, Interoperability and Reusability). We herein present Lesionia, a DSDM designed to support the PEER518 consortium that aimed at developing new cutaneous leishmaniases (CL) diagnostics using samples and data collected from patients suspected of having CL in countries in the MENA region and West Africa. The consortium involved nine institutions across five countries: Tunisia, Morocco, Lebanon, Mali, and the USA, and informally Scientists from Algeria and Nigeria. The guidelines on the data to be collected by the clinicians and biologists during the project were used for the development of a Questionnaire that served as a basis for the implementation of a dedicated web-based DSDM.Lesionia was developed and validated for the management and the analysis of clinical and epidemiological data in the diagnosis of CL. It consists of a relational database, a web-based user interface (WUI) and a tool for experimental data handling and analysis of clinical and epidemiological data of CL cases. The platform was deployed and validated during the PEER518 project using data collected across the involved teams. Lesionia is expandable to include further collaborators, partners, and projects. It is designed for data handling from the consented patient interview and sample collection to the samples' storage and investigation. The WUI permits data entry, fetching, visualization and analysis. Rigorous controls on data entry were implemented to reduce discrepancies. It also offers a set of analysis tools that range from descriptive statistics to variable correlation analysis. Lesionia is accessible in a secure manner to all users of the consortium through a web browser.Lesionia will be a valuable tool for collaborative and integrative management of clinical and epidemiological data. It is an open-source software that can broadly serve the scientific community interested in studying, controlling, reporting, and diagnosing CL and similar cutaneous diseases.

Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring

Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real-time data. This review explores the current applications and future potential of multimodal AI across healthcare, with a particular focus on orthopaedic surgery. In presurgical planning, multimodal AI has demonstrated significant improvements in diagnostic accuracy and risk prediction, with studies reporting an Area under the receiving operator curve presenting good to excellent performance across various orthopaedic conditions. Intraoperative applications leverage advanced imaging and tracking technologies to enhance surgical precision, while postoperative care has been advanced through continuous patient monitoring and early detection of complications. Despite these advances, significant challenges remain in data integration, standardisation, and privacy protection. Technical solutions such as federated learning (allowing decentralisation of models) and edge computing (allowing data analysis to happen on site or closer to site instead of multipurpose datacenters) are being developed to address these concerns while maintaining compliance with regulatory frameworks. As this field continues to evolve, the integration of multimodal AI promises to advance personalised medicine, improve patient outcomes, and transform healthcare delivery through more comprehensive and nuanced analysis of patient data. Level of Evidence: Level V.

Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning

Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.

Learning Objectives Matrix in DIM.RUHR: A Didactic Concept for the Interprofessional Teaching of Data Literacy in Outpatient Health Care

(1) Background: Each year, significant volumes of healthcare data are generated through both research and care. Since fundamental digital processes cannot function effectively without essential data competencies, the challenge lies in enhancing the quality of data management by establishing data literacy among professionals in outpatient healthcare and research. (2) Methods: Within the DIM.RUHR project (Data Competence Center for Interprofessional Use of Health Data in the Ruhr Metropolis), a didactic concept for interprofessional data literacy education is developed, structured as a learning objectives matrix. Initially conceived through a literature review, this concept has been continually developed through collaboration with interprofessional project partners. The study was conducted between February 2023 and June 2024. (3) Results: The foundational structure and content of the didactic concept are based on various scientific studies related to general data literacy and the outcomes of an interactive workshop with project partners. Eight distinct subject areas have been developed to encompass the data literacy required in healthcare professions: (1) Fundamentals and general concepts, (2) ethical, legal, and social considerations, (3) establishing a data culture, (4) acquiring data, (5) managing data, (6) analyzing data, (7) interpreting data, and (8) deriving actions. Within these, learners' data literacy is assessed across the four competency areas: basic, intermediate, advanced, and highly specialized. (4) Conclusions: The learning objectives matrix is anticipated to serve as a solid foundation for the development of teaching and learning modules aimed at enhancing data literacy across healthcare professions, enabling them to effectively manage data processes while addressing the challenges associated with digital transformation.

Advances in Photonic Materials and Integrated Devices for Smart and Digital Healthcare: Bridging the Gap Between Materials and Systems

Recent advances in developing photonic technologies using various materials offer enhanced biosensing, therapeutic intervention, and non-invasive imaging in healthcare. Here, this article summarizes significant technological advancements in materials, photonic devices, and bio-interfaced systems, which demonstrate successful applications for impacting human healthcare via improved therapies, advanced diagnostics, and on-skin health monitoring. The details of required materials, necessary properties, and device configurations are described for next-generation healthcare systems, followed by an explanation of the working principles of light-based therapeutics and diagnostics. Next, this paper shares the recent examples of integrated photonic systems focusing on translation and immediate applications for clinical studies. In addition, the limitations of existing materials and devices and future directions for smart photonic systems are discussed. Collectively, this review article summarizes the recent focus and trends of technological advancements in developing new nanomaterials, light delivery methods, system designs, mechanical structures, material functionalization, and integrated photonic systems to advance human healthcare and digital healthcare.

Enhancing smart healthcare networks: Integrating attribute-based encryption for optimization and anti-corruption mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 ms, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74 %. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

Translational research serves as the bridge between basic research and practical applications in clinical settings. The journey from "bench to bedside" is fraught with challenges and complexities such as the often-observed disparity between how compounds behave in a laboratory setting versus in the complex systems of living organisms. The challenge is further compounded by the limited ability of in vitro models to mimic the specific biochemical environment of human tissues. This article explores and details the recent advancements and innovative approaches that are increasingly successful in bridging the gap between laboratory research and patient care. These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. The article aims to showcase how these technologies improve the predictability of drug performance in human bodies and significantly speed up the drug development process. Furthermore, it discusses the role of biomarker discovery in preparation of more targeted and personalized therapy approaches and covers the impact of regulatory changes designed to facilitate drug approvals. Additionally, by providing detailed case studies of successful applications, we illustrate the practical impacts of these innovations on drug discovery and patient care.

Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review

Introduction

Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics.

Methods

We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science.

Results

Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies (n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged.

Conclusion

The current evidence demonstrates consumers' understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.

Understanding public trust in national electronic health record systems: A multi-national qualitative research study

Objective

Having public trust in national electronic health record systems (NEHRs) is crucial for the successful implementation and participation of NEHRs within a nations healthcare system. Yet, a lack of conceptual clarity precludes healthcare policymakers from incorporating trust to the fullest extent possible. In response, this study seeks to validate an existing framework for public trust in the healthcare system, which will help provide a clearer understanding of what constitutes public trust in NEHRs across members of the public in different countries, cultures, and contexts.

Methods

Twenty-four focus groups were conducted in Austria, Germany, France, Italy, the Netherlands, and Switzerland with residents of each respective country to discuss their viewpoints on our public trust in NEHRs framework in order to validate said framework.

Results

Frameworks describing the causes and effects of public trust in NEHRs were created for each country studied. Across all countries, the frameworks remained similar to our base framework, highlighting our frameworks' robustness. Data security, privacy, and autonomy were consistently described as the most important aspects of public trust in NEHRs. Concurrently, health system actors, such as doctors, were found to have significant influence on NEHR implementation. Their influence, however, can either be beneficial or detrimental to public trust in NEHRs, depending on their actions and how the public perceives those actions. Additional results detail contextual insights into country-specific viewpoints and the role of healthcare stakeholders in public trust in NEHRs. The results showcase the differences and similarities in which different populations across Europe view trust in NEHRs in the context of our framework.

Conclusions

These findings present public trust frameworks in the context of NEHRs for the study countries. These frameworks can assist stakeholders in obtaining a comprehensive understanding of the complexity of public trust in implementing and promoting their NEHRs, including measurements of public trust.

AI-Guided Cancer Therapy for Patients with Coexisting Migraines

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

Using Electronic Health Records to Facilitate Precision Psychiatry

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

Introduction

Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health.

Methods

Firstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health.

Results

After screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a "one-size-fits-all approach" with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation).

Discussion

Although there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction

Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction.

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case

BACKGROUND

Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

METHODS

Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two.

RESULTS

All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores.

CONCLUSIONS

As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

Efficient Privacy-preserving Logistic Model With Malicious Security

Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. The d -transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.

Achieving sustainable medical tourism: unpacking privacy concerns through a tripartite game theoretic lens

Introduction

Medical tourism has grown significantly, raising critical concerns about the privacy of medical tourists. This study investigates privacy issues in medical tourism from a game theoretic perspective, focusing on how stakeholders' strategies impact privacy protection.

Methods

We employed an evolutionary game model to explore the interactions between medical institutions, medical tourists, and government departments. The model identifies stable strategies that stakeholders may adopt to protect the privacy of medical tourists.

Results

Two primary stable strategies were identified, with E6(1,0,1) emerging as the optimal strategy. This strategy involves active protection measures by medical institutions, the decision by tourists to forgo accountability, and strict supervision by government departments. The evolution of the system's strategy is significantly influenced by the government's penalty intensity, subsidies, incentives, and the compensatory measures of medical institutions.

Discussion

The findings suggest that medical institutions are quick to make decisions favoring privacy protection, while medical tourists tend to follow learning and conformity. Government strategy remains consistent, with increased subsidies and penalties encouraging medical institutions towards proactive privacy protection strategies. We recommend policies to enhance privacy protection in medical tourism, contributing to the industry's sustainable growth.

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.

Secure data communication in WSHN using EXP-MD5 and DHSK-ECC

BACKGROUND

In the Healthcare (HC) sector, the usage of Wireless Sensor Healthcare Networks (WSHN) is attaining specific importance. The sensor device is implanted into the patient's body, and the sensed health information of patients is transformed via data aggregating devices like mobile devices, cameras, and so on, to the doctors. Thus, the early signs of diseases are identified, and remote monitoring of the patient's health is carried out by the physician on time. This aids in improving the health condition of the people and reduces the severity of disorders. But, the security gap in HC remains unresolved, despite various advantages.

OBJECTIVE

This work proposes secured data communication in WSHN using Exponential Message Digest5 (EXP-MD5) and Diffie Hellman Secret Key-based Elliptic Curve Cryptography (DHSK-ECC) techniques.

METHODS

Primarily, the patient registers their details in the Hospital Cloud Server (HCS). With hospital ID and patient ID, public and private keys are generated during registration. Afterward, by utilizing the Navie Shuffling (NS) technique, nCr combinations are created and shuffled. After shuffling, any of the randomly selected combinations are encoded utilizing the American Standard Code for Information Interchange (ASCII) code. For patient authentication, the ASCII code is further converted into a Quick Response(QR) code. Upon successful registration, the patient logs in to HCS. The patient can book for doctor's appointment if the login details are verified with those of the registered details. On consulting the doctor at the pre-informed time, the digital signature is created utilizing the Universal Unique Salt-based Digital Signature Algorithm (UUS-DSA) for authenticating the patient details. Further, for providing accessibility to all the authorized patients, the registered patients on HCS are considered as nodes. Then, an authorized path is created using the EXP-MD5 technique to protect each individual patient's details. The patient's IoT data is sensed, followed by authorized path creation. The data is encrypted via the DHSK-ECC algorithm for secure data transmission. Lastly, all the information is stored in HCS, so that the patient's health condition is regularly monitored by the doctor and the needy advice is suggested to the patients in the future. Also, hash matching is carried out when the doctor needs to access data.

RESULTS

The proposed technique's efficacy is validated by the performance analysis in comparison with other conventional techniques.

CONCLUSION

In this proposed research, the authentication is performed in multiple scenarios to enhance data security and user privacy. The patient details are authenticated during registration and verification to access the online consultation only by the authorized person. Further, the patient health information is encrypted in the proposed work after consultation so that the intrusion of medical records by malicious users and data tampering is prevented. Also, the sensed data gathered from patients are transferred to the HCS by creating the authorized path, which further enhances the security of patient data. Thus, the data communication of the WSHN is well-secured in this work through multi-level authentication and improved cryptography techniques.

Big data in breast cancer: Towards precision treatment

Breast cancer is the most prevalent and deadliest cancer among women globally, representing a major threat to public health. In response, the World Health Organization has established the Global Breast Cancer Initiative framework to reduce breast cancer mortality through global collaboration. The integration of big data analytics (BDA) and precision medicine has transformed our understanding of breast cancer's biological traits and treatment responses. By harnessing large-scale datasets - encompassing genetic, clinical, and environmental data - BDA has enhanced strategies for breast cancer prevention, diagnosis, and treatment, driving the advancement of precision oncology and personalised care. Despite the increasing importance of big data in breast cancer research, comprehensive studies remain sparse, underscoring the need for more systematic investigation. This review evaluates the contributions of big data to breast cancer precision medicine while addressing the associated opportunities and challenges. Through the application of big data, we aim to deepen insights into breast cancer pathogenesis, optimise therapeutic approaches, improve patient outcomes, and ultimately contribute to better survival rates and quality of life. This review seeks to provide a foundation for future research in breast cancer prevention, treatment, and management.

Developing technology-based interventions for infectious diseases: ethical considerations for young sexual and gender minority people

Compared to their heterosexual and cisgender peers, young sexual and gender minority (YSGM) people are more likely to contract sexually transmitted infections (STIs; e.g., HIV) and to face adverse consequences of emerging infections, such as COVID-19 and mpox. To reduce these sexual health disparities, technology-based interventions (TBIs) for STIs and emerging infections among YSGM adolescents and young adults have been developed. In this Perspective, we discuss ethical issues, ethical principles, and recommendations in the development and implementation of TBIs to address STIs and emerging infections among YSGM. Our discussion covers: (1) confidentiality, privacy, and data security (e.g., if TBI use is revealed, YSGM are at increased risk of discrimination and family rejection); (2) empowerment and autonomy (e.g., designing TBIs that can still function if YSGM users opt-out of multiple features and data collection requests); (3) evidence-based and quality controlled (e.g., going above and beyond minimum FDA effectiveness standards to protect vulnerable YSGM people); (4) cultural sensitivity and tailoring (e.g., using YSGM-specific models of prevention and intervention); (5) balancing inclusivity vs. group specificity (e.g., honoring YSGM heterogeneity); (6) duty to care (e.g., providing avenues to contact affirming healthcare professionals); (7) equitable access (e.g., prioritizing YSGM people living in low-resource, high-stigma areas); and (8) digital temperance (e.g., being careful with gamification because YSGM experience substantial screen time compared to their peers). We conclude that a community-engaged, YSGM-centered approach to TBI development and implementation is paramount to ethically preventing and treating STIs and emerging infections with innovative technology.

Large Language Models in Neurology Research and Future Practice

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.

Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics

Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.

The Role of E-Commerce Adoption in Enhancing Regulatory Compliance in Information Systems of Foreign Investment Management in Malaysia - A Moderating Effect of Innovation Management

Over the years, the rise of e-commerce has brought about significant changes in the way businesses operate globally which also includes how foreign investment is managed. As more companies move on-line and engage in move-border transactions, foreign investment management has turned out to be greater complicated and requires a distinctive set of techniques. This study aims to examine the mediating role of IT capabilities and information security measures, as well as the moderating role of innovation management in this relationship. This study uses a cross-sectional research design. Data were collected from 230 Malaysian foreign investment management firms using a structured questionnaire. The measurement scales used were validated and adopted from previous studies. SPSS was used to analyze the data and test the hypothesized relationships. The findings of the study showed that e-commerce adoption has a significant and positive impact on regulatory compliance. Furthermore, this relationship is significantly mediated by IT capabilities and information security measures and moderated by innovation management. This study provides valuable insights into the effects of e-commerce adoption on regulatory compliance in the context of foreign investment management in Malaysia. The findings underscore the importance of developing strong IT capabilities and implementing strong information security measures to enhance regulatory compliance. Additionally, the study emphasizes the need for innovative management practices to effectively leverage e-commerce adoption for regulatory compliance.

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Machine learning and deep learning approaches in IoT

The internet is a booming sector for exchanging information because of all the gadgets in today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual's life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT.

Information security and privacy in hospitals: a literature mapping and review of research gaps

Information security and privacy are matters of concern in every industry. The healthcare sector has lagged in terms of implementing cybersecurity measures. Therefore, hospitals are more exposed to cyber events due to the criticality of patient data. Currently, little is known about state-of-the-art research on information security and privacy in hospitals. The purpose of this study is to report the outcome of a systematic literature review on research about the application of information security and privacy in hospitals. A systematic literature review following the PRISMA methodology was conducted. To reference our sample according to cybersecurity domains, we benchmarked each article against two cybersecurity frameworks: ISO 27001 Annex A and the NIST framework core. Limited articles in our papers referred to the policies and compliance sections of ISO 27001. In addition, most of our sample is classified by the NIST function "Protect," meaning activities related to identity management, access control and data security. Furthermore, we have identified key domains where research in security and privacy are critical, such as big data, IOT, cloud computing, standards and regulations. The results indicate that although cybersecurity is a growing concern in hospitals, research is still weak in some areas. Considering the recrudescence of cyber-attacks in the healthcare sector, we call for more research in hospitals in managerial and non-technical domains of information security and privacy that are uncovered by our analysis.

Research on medical data security sharing scheme based on homomorphic encryption

With the deep integration of "AI + medicine", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance.

An anonymization-based privacy-preserving data collection protocol for digital health data

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.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Multimodal machine learning in precision health: A scoping review

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

The present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.

WSNEAP: An Efficient Authentication Protocol for IIoT-Oriented Wireless Sensor Networks

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge-response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol.

Assessment of human milk in the era of precision health

PURPOSE OF REVIEW

Precision health provides an unprecedented opportunity to improve the assessment of infant nutrition and health outcomes. Breastfeeding is positively associated with infant health outcomes, yet only 58.3% of children born in 2017 were still breastfeeding at 6 months. There is an urgent need to examine the application of precision health tools that support the development of public health interventions focused on improving breastfeeding outcomes.

RECENT FINDINGS

In this review, we discussed the novel and highly sensitive techniques that can provide a vast amount of omics data and clinical information just by evaluating small volumes of milk samples, such as RNA sequencing, cytometry by time-of-flight, and human milk analyzer for clinical implementation. These advanced techniques can run multiple samples in a short period of time making them ideal for the routine clinical evaluation of milk samples.

SUMMARY

Precision health tools are increasingly used in clinical research studies focused on infant nutrition. The integration of routinely collected multiomics human milk data within the electronic health records has the potential to identify molecular biomarkers associated with infant health outcomes.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed solution for confidentiality management of electronic medical records on the cloud, i.e., which can ensure the confidentiality of electronic medical records on the untrusted cloud, without compromising the availability of an existing medical information management system.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

Federated Learning for Electronic Health Records

In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.