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At least 19 recordsLinked to original sources
PubMed1986

Computer security: a necessary element of integrated information systems.

The Matheson Report sees the medical library as playing a key role in a network of interlocking information bases that will extend from central repositories of medical information to each physician's personal records. It appears, however, that the role of security in this vision has not been fully delineated. This paper discusses problems in maintaining the security of confidential medical information, the state of the applicable law, and techniques for security (with special emphasis on the UNIX operating system). It is argued that the absence of security threatens any plan to build an information network, as there will be resistance to any system that may give intruders access to confidential data.

Computers↗
PubMed2025

A Sociotechnical Approach to Genomic Data Privacy: A Comparative Analysis.

The sharing of genomic data across international borders presents significant privacy law challenges.Secured computed environments on smartphones allow the storing and processing of sensitive data without the underlying data being shared with processors.A novel technology, described here, to process genomic data within a secured computing environment seems to comport with EU and US privacy laws, despite their differing aims and rules.This technology suggests there may be technological solutions to privacy law fragmentation across jurisdictions, so long as data subjects socially trust the technology and have control over their data.

genome↗
PubMedDate not supplied

Medical informatics in the intensive care unit: state of the art 1991.

Intensive care medicine requires timely, accurate, and integrated patient records to provide the highest quality patient care. Computerized patient records offer the best method to achieve these needs. The expectations of society for medical progress through increased use of computers is growing. For optimal use of computers in the ICU there must be a harmonious collaboration between medical informaticists, physicians, nurses, therapists, and administrators. The future use of computers in ICU care will be evolutionary rather than revolutionary. We are on the frontier of some exciting times in the next decade as computers become commonplace in the clinical care process rather than an unusual event. This paper discusses the progress and challenges of computers in the ICU.

Clinical Protocols↗
PubMed2026

Pithos - a scalable and secure data container for FAIR-compliant research data management in life sciences.

Modern research techniques have led to exponential growth in the volume and complexity of scientific data. Consequently, managing these volumes securely and efficiently has become a major challenge. While all research domains face these challenges, life science research is particularly affected because current approaches often rely on a large set of different file formats, with metadata stored in separated databases or spreadsheets. This leads to fragmented datasets, orphaned data, and compromised research reproducibility. Traditional solutions also force researchers to choose between security and accessibility, with encrypted files preventing selective access and indexed formats lacking adequate security for sensitive data. These limitations are particularly problematic in large-scale genomic studies where researchers must decompress multi-gigabyte files to access specific regions, creating computational bottlenecks and inefficient network usage when working with cloud-stored datasets. We introduce Pithos, a next-generation file format specifically designed for scientific data management in distributed cloud environments. The format uses content-defined chunking to enable efficient deduplication across distributed storage systems, thereby reducing storage costs and bandwidth requirements. The append-only structure ensures data immutability and allows for incremental updates without compromising content. Benchmark results show that Pithos outperforms existing solutions in read and write performance, with comparable or improved storage efficiency.

Biological Science Disciplines↗
PubMed2025

Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.

MOTIVATION: The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. RESULTS: This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. AVAILABILITY AND IMPLEMENTATION: All associated code and documentation are available at https://github.com/farahpoor/smkhe.

Computer Security↗
PubMedDate not supplied

Data security in medical information systems: technical aspects of a proposed legislation.

This paper analyses the results of a recent survey performed among medical establishment personnel in Greece, evaluates information security legislation existing in other countries and incorporates guidelines of international societies to propose principles governing a future legal framework. Furthermore, it presents a design methodology for designing secure information systems and provides an example of the use of this methodology in designing a database oriented secure medical information system with access rights incorporated.

Computer Security↗
PubMed2026

Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.

BACKGROUND: Biomedical artificial intelligence (AI) requires the integration of privacy-enhancing technologies (PETs) to safeguard sensitive clinical, imaging, and genomic data while preserving analytical utility. OBJECTIVES: This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks. METHODS: We systematically searched PubMed, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2015 and 2025. Eligible studies addressed differential privacy, federated learning, secure multiparty computation, homomorphic encryption, or hybrid approaches in biomedical AI. Data were charted on PET type, modality, lifecycle stage, utility metrics, privacy parameters, and deployment considerations. A critical appraisal rubric assessed threat-model adequacy, methodological clarity, reproducibility, privacy-utility transparency, and deployment realism. Additionally, we hand-searched major venues (USENIX Security, NeurIPS, AAAI) and screened Google Scholar for grey literature, applying de-duplication across sources. RESULTS: We identified 87 studies spanning clinical decision support, genomics, and medical imaging. From 25,761 initial records, 3,754 underwent title/abstract screening and 1,968 underwent full-text assessment. PETs demonstrated distinct strengths and limitations: differential privacy provided provable guarantees but reduced performance on imbalanced data; federated learning improved data access but remained vulnerable to gradient leakage; and cryptographic methods ensured confidentiality at high computational cost. Synthetic data generation supported privacy-conscious data sharing and benchmarking but remained sensitive to disclosure risk, fidelity loss, and subgroup representation. Hybrid and emerging approaches, including trusted execution environments, zero-knowledge proofs, and privacy-preserving transformer architectures, mitigated composability gaps yet lacked full end-to-end assurance. Case studies at hospital and biobank scale illustrated practical feasibility and infrastructure demands. CONCLUSIONS: Situating PETs within technical and operational contexts clarifies their capabilities, limitations, and deployment challenges. Residual risks persist, including fairness concerns, inference-time leakage, and overreliance on PETs as compliance proxies. Sustained technical innovation and institutional governance remain essential for the trustworthy integration of PETs in biomedical AI.

biomedical AI↗
PubMed1992

A computerized intensive care unit order-writing protocol.

OBJECTIVE: To present a computerized intensive care unit order-writing protocol. DESIGN: Descriptive report. SETTING: Eight-bed surgical intensive care unit, Department of Surgery, Department of Veterans Affairs Medical Center, Bronx, NY. METHODS: IBM-based, computer network program that provides user-friendly, logical, and comprehensive organ-system order sequences for patient management. RESULTS: Since July 1988, an order program that stresses (1) improved and more efficient patient care, (2) the use of program-integrated automatic safety features, (3) the substitution of computer entry for handwriting, and (4) the assurance that physicians deliver obligatory care in a logical organ-system-based progression has been implemented. CONCLUSIONS: The order protocol system presented is simple to introduce and operate, has minimal training and technical requirements, and is demonstrably reliable.

Computer Security↗
PubMed1992

Automated ambulatory medical records systems. An orphan technology.

Automated ambulatory medical records systems (AAMRSs) have been operational for over 20 years but have not been adopted by more than a small fraction of their potential users. This paper presents a detailed analysis of the uses and benefits of the COSTAR-based AAMRS at the Harvard Community Health Plan and of the factors which have inhibited the dissemination of COSTAR. We conclude that AAMRSs have been an orphan technology and cite trends in health care that favor the future development of AAMRSs.

Ambulatory Care Information Systems↗
PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗