Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

An in-depth examination of requirements for disclosure risk assessment.

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

"Choice of law" in precision medicine research.

Large-scale precision medicine research requires massive amounts of data representing people from all walks of life; thus, in the US, it is often multistate research. Significant legal and ethical quandaries arise as a result of the patchwork of laws states have enacted that may apply to research, are not preempted by federal law, and may impose requirements or provide participant rights and protections that differ from other states. Determining which state's laws apply, and under what circumstances, is not solved by the transition to a single-IRB model and researchers cannot simply choose one state's laws to apply uniformly. At a minimum, the current process of meeting each state's requirements could be made more reliable and efficient. To fundamentally change this status quo, however, requires action at multiple levels. Federally, well-known gaps in the Genetic Information Nondiscrimination Act should be closed, and a coherent system of compensation for research injury-including non-physical injuries-should be developed. States should clarify which of their laws are intended to apply to research and work collaboratively to harmonize them. At the level of individual research projects, numerous policies and procedures could be standardized through authoritative guidelines. Examples include clarifying the scope of broad consent, understanding and upholding Certificates of Confidentiality, offering individual research results responsibly, and consistently disseminating aggregate results to participants and the public. Overall, development of a choice of law framework specific to the research context could significantly promote clarity and consistency.

Balancing data privacy and usability in the federal statistical system.

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.

Artificial Intelligence in the Provision of Health Care: An American College of Physicians Policy Position Paper.

Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.

Privacy preserving validation for multiomic prediction models.

Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.

Privacy-preserving federated genome-wide association studies via dynamic sampling.

MOTIVATION: Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS. RESULTS: This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We instantiate our approach using logistic regression, a commonly used statistical method for identifying associations between genetic markers and the phenotype of interest. We evaluate our proposed methods using two real genomic datasets and demonstrate their robustness in the presence of between-study heterogeneity and skewed phenotype distributions using a variety of experimental settings. The empirical results show the efficiency and applicability of the proposed method and the promise for its application for large-scale collaborative GWAS. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/amioamo/TDS.

Telemedicine with special focus on allergic diseases and asthma-Status 2022: An EAACI position paper.

Efficacious, effective and efficient communication between healthcare professionals (HCP) and patients is essential to achieve a successful therapeutic alliance. Telemedicine (TM) has been used for decades but during the COVID-19 pandemic its use has become widespread. This position paper aims to describe the terminology and most important forms of TM among HCP and patients and review the existing studies on the uses of TM for asthma and allergy. Besides, the advantages and risks of TM are discussed, concluding that TM application reduces costs and time for both, HCP and patients, but cannot completely replace face-to-face visits for physical examinations and certain tests that are critical in asthma and allergy. From an ethical point of view, it is important to identify those involved in the TM process, ensure confidentiality and use communication channels that fully guarantee the security of the information. Unmet needs and directions for the future regarding implementation, data protection, privacy regulations, methodology and efficacy are described.

The HI V O pen Call on I nformed C onsent and E thics in Research (VOICE) for Adolescents and Young Adults: A Digital Crowdsourcing Open Call in Low- and Middle-Income Countries.

BACKGROUND: Many adolescents and young adults (AYAs; 10-24 years old) are excluded from HIV research because of social, ethical, and legal challenges with informed consent, resulting in limited AYA-focused data. We use a participatory approach to identify strategies for improving AYA consent processes in HIV research in low- and middle-income countries (LMICs). METHODS: We conducted a digital crowdsourcing open call for ideas to improve AYA consent to HIV research in LMICs. Crowdsourcing involves engaging a group of people in problem-solving, then sharing emergent solutions. Submissions were evaluated by 3 independent judges using predefined criteria, with exceptional strategies receiving prizes. Demographic data were collected, and textual data were qualitatively analyzed for emergent themes in barriers and facilitators for improving AYA consent in HIV research, guided by a socioecological model. RESULTS: We received 110 strategies total; 65 were eligible for evaluation, 25 of which were identified as finalists. Fifty-eight participants from 10 LMICs submitted the 65 eligible submissions, of which 30 (52%) were 18 to 24 years old. Thematic analysis identified 10 barriers to AYA consent, including HIV stigma, limited education, and legal/regulatory barriers. Strategies for improving AYA consent processes revealed 7 potential facilitators: enhancing AYA engagement in research, involving parents/guardians, improving education/awareness, improving institutional practices/policy, making research participation more AYA-friendly, enhancing engagement of other key communities of interest, and empowering AYA. CONCLUSIONS: Diverse communities of interest in LMICs developed compelling strategies to enhance informed consent that may improve AYA inclusion in HIV research. These data will be used to develop practical guidance on improving AYA consent processes.

Ethical considerations for artificial intelligence in dermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

The use of artificial intelligence (AI) in dermatology is rapidly increasing, with applications in dermatopathology, medical dermatology, cutaneous surgery, microscopy/spectroscopy and the identification of prognostic biomarkers (characteristics that provide information on likely patient health outcomes). However, with the rise of AI in dermatology, ethical concerns have emerged. We reviewed the existing literature to identify applications of AI in the field of dermatology and understand the ethical implications. Our search initially identified 202 papers, and after we went through them (screening), 68 were included in our review. We found that ethical concerns are related to the use of AI in the areas of clinical image analysis, teledermatology, natural language processing models, privacy, skin of colour representation, and patient and provider attitudes toward AI. We identified nine ethical principles to facilitate the safe use of AI in dermatology. These ethical principles include fairness, inclusivity, transparency, accountability, security, privacy, reliability, informed consent and conflict of interest. Although there are many benefits of integrating AI into clinical practice, our findings highlight how safeguards must be put in place to reduce rising ethical concerns.

Direct-to-consumer genetic testing and the changing landscape of gamete donor conception: key issues for practitioners and stakeholders.

RESEARCH QUESTION: What effect does direct-to-consumer genetic testing (DTCGT) have on information finding and sharing in relation to gamete donor conception? DESIGN: This study used in-depth qualitative interviews with parents through donor conception, donors, the relatives of donors and donor-conceived people who have used, or considered using, DTCGT. Interviews were conducted between September 2021 and February 2023. Sixty people defined themselves as having been affected by donor conception and DTCGT. Fifty-seven of these were resident in the UK at the time of interview. The final sample included 19 (spermatozoa, egg or embryo) donors, 25 donor-conceived people, 20 parents through donor conception and two relatives of donors. Five participants occupied more than one of these roles. RESULTS: The rise of DTCGT is affecting how information about donor conception is managed: it shifts patterns of knowledge about donor conception; increases flexibility regarding the age of access to information about donor relatives; can lead to a growing role for non-professionals, including wider family members, in gatekeeping information about donor conception; accentuates the effect of donor conception for donors' and the relatives of donor-conceived people; and shapes, and is shaped, by the formal regulatory donor information management systems. CONCLUSION: Fertility professionals should inform people using, or considering, donor conception, or (potential) donors, about the different ways DTCGT can affect sharing information about donor conception. Support is needed for those affected by these changes.

Prehospital Clinical Decision-Making for Medication Administration for Behavioral Emergencies.

Introduction. Prehospital behavioral emergency protocols provide guidance on when a medication may be necessary; however, the final decision of which medication to administer to a patient is made independently by paramedics. This study sought to describe the clinical decision-making process of prehospital behavioral emergencies when paramedics consider chemical restraints, and determine the factors associated with choosing specific medications to administer. Methods. Paramedics from a Midwest County in the United States were surveyed in November of 2019. The survey consisted of two open-ended questions, measuring paramedics' clinical decision-making process and factors that they considered when selecting a medication. An immersion-crystallization approach was used to analyze the interviews. Results. There was a 53% (79/149) response rate. Six themes emerged regarding the paramedics' decisions to use medication for cases involving patients with behavioral emergencies: safety of the patients and paramedics; inability to use calming techniques; severity of the behavioral emergency; inability to assess the patient due to presentation; etiology of the behavioral episode; and other factors, such as age, size, and weight of the patient. Six themes emerged regarding factors paramedics considered when choosing which medication to use in behavioral emergencies: etiology of the behavioral emergency, patient presentation, the patient's history, the patient's age, desired effect and intended outcome of the medication, and other factors. Conclusion. This study suggests there are several factors, such as safety and the etiology of the behavioral emergency, that contributed to paramedics' decision-making. The results of this study could help Emergency Medical Services administrators in revising behavioral emergency protocols.

Ethical Dilemmas of Using Artificial Intelligence in Medicine.

BACKGROUND: Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. AREAS OF UNCERTAINTY: The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. DATA SOURCES: A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. ETHICS AND THERAPEUTIC ADVANCES: The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. CONCLUSIONS: AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

Determining the state of guidance on pediatric biobanking for researchers, HRECS, and families: Regulatory mapping of international guidance.

Biobanking-the storage of human biological samples, including tissue, blood, urine, and genetic data-raises many ethical, legal, and social issues, including confidentiality and privacy. Pediatric biobanking is more complicated, with difficulties arising because children lack capacity to consent and acquire this capacity upon maturity when the research is still ongoing. Yet given the limited availability of pediatric samples, the translational nature of biobanking presents a unique opportunity to share samples and produce clinically necessary information about pediatric development and diseases. Guidance on navigating these legal and ethical difficulties is needed for those involved in pediatric biobanking-including researchers, participants, and families, and those involved in biobank governance. This paper seeks to map the current regulatory framework governing pediatric biobanking to determine what guidance is currently offered. Regulatory mapping of current international and national guidelines on pediatric biobanking addressing the ethical, legal, and social nuances of pediatric biobanking was undertaken. This paper finds that international guidelines around biobanking are mostly for adults, and even when pediatric-specific, documents are non-binding, inconsistent, or only limited guidance is offered on a range of important issues specific to pediatric biobanks.   Conclusion: This paper shows a need for consistent, comprehensive, and clear regulation on pediatric biobanking so that research can more quickly, efficiently, and ethically be translated to useful information and treatment in pediatric care. What is Known: • Pediatric biobanking presents new opportunities to conduct valuable translational research to benefit pediatric populations. However, the storage of pediatric biological samples raises many ethical, legal and social issues-in part because child participants may be considered to lack capacity to consent but can acquire this capacity upon maturity when the research is still ongoing. Pediatric biobanks must grapple with issues of consent, confidentiality and privacy, and long-term participation regarding child participants. What is New: • Regulatory guidance on these ethical, legal, and social issues is needed for researchers, participants, and families and those involved in biobank governance. This paper identifies nationally specific and international guidance on biobanking and summarizes the guidance provided in relation to these pediatric specific issues. It finds that most guidance is non-binding and inconsistent between guidance documents and may offer only limited guidance to stakeholders. A need for consistent, comprehensive, and clear regulation on pediatric biobanking is needed at an international level to enable research.

Open AI meets open notes: surveillance capitalism, patient privacy and online record access.

Patient online record access (ORA) is spreading worldwide, and in some countries, including Sweden, and the USA, access is advanced with patients obtaining rapid access to their full records. In the UK context, from 31 October 2023 as part of the new NHS England general practitioner (GP) contract it will be mandatory for GPs to offer ORA to patients aged 16 and older. Patients report many benefits from reading their clinical records including feeling more empowered, better understanding and remembering their treatment plan, and greater awareness about medications including possible adverse effects. However, a variety of indirect evidence suggests these benefits are unlikely to accrue without supplementation from internet-based resources. Using such routes to augment interpretation of the data and notes housed in electronic health records, however, comes with trade-offs in terms of exposing sensitive patient information to internet corporations. Furthermore, increased work burdens on clinicians, including the unique demands of ORA, combined with the easy availability and capability of a new generation of large language model (LLM)-powered chatbots, create a perfect collision course for exposing sensitive patient information to private tech companies. This paper surveys how ORA intersects with internet associated privacy risks and offers a variety of multilevel suggestions for how these risks might be better mitigated.

Exploring the Ethical, Legal, and Social Implications of ChatGPT in Urology.

PURPOSE OF THE REVIEW: ChatGPT is programmed to generate responses based on pattern recognition. With this vast popularity and exponential growth, the question arises of moral issues, security and legitimacy. In this review article, we aim to analyze the ethical and legal implications of using ChatGPT in Urology and explore potential solutions addressing these concerns. RECENT FINDINGS: There are many potential applications of ChatGPT in urology, and the extent to which it might improve healthcare may cause a profound shift in the way we deliver our services to patients and the overall healthcare system. This encompasses diagnosis and treatment planning, clinical workflow, patient education, augmenting consultations, and urological research. The ethical and legal considerations include patient autonomy and informed consent, privacy and confidentiality, bias and fairness, human oversight and accountability, trust and transparency, liability and malpractice, intellectual property rights, and regulatory framework. The application of ChatGPT in urology has shown great potential to improve patient care and assist urologists in various aspects of clinical practice, research, and education. Complying with data security and privacy regulations, and ensuring human oversight and accountability are some potential solutions to these legal and ethical concerns. Overall, the benefits and risks of using ChatGPT in urology must be weighed carefully, and a cautious approach must be taken to ensure that its use aligns with human values and advances patient care ethically and responsibly.

Videos for Psychiatric Education: Could Deidentification Technology Make it Possible to Use Video Recordings of Patients for Psychiatric Education?

ABSTRACT: The field of psychiatry has been limited in its use of patient videos for educational purposes because essential facial information must be obscured to protect patient privacy, confidentiality, and dignity. This article calls attention to emerging technologies for deidentification of patients in video recordings while still preserving facial expression. Fully anonymized videos could be used to augment the education of psychiatric residents and for continuing education of the psychiatric workforce. This article suggests projects that deidentification technology could make possible; it also outlines some complex problems that would need to be addressed before the field could use this potentially transformative technology.

The European Genome-phenome Archive in 2021.

The European Genome-phenome Archive (EGA - https://ega-archive.org/) is a resource for long term secure archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research in line with the FAIR principles. Launched in 2008, the EGA has grown quickly, currently archiving over 4,500 studies from nearly one thousand institutions. The EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA, therefore, the submitter keeps control on who has access to the data and under which conditions. Given the size and value of data hosted, the EGA is constantly improving its value chain, that is, how the EGA can contribute to enhancing the value of human health data by facilitating its submission, discovery, access, and distribution, as well as leading the design and implementation of standards and methods necessary to deliver the value chain. The EGA has become a key GA4GH Driver Project, leading multiple development efforts and implementing new standards and tools, and has been appointed as an ELIXIR Core Data Resource.