Privacy and Health Practices in the Digital Age

Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review

Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the "empirical turn" in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased use of empirical methodologies from social sciences, the proliferation of health-related subtypes of technology ethics might have a comparable impact on current bioethical research. This systematic journal review analyses the reporting of ethical frameworks and non-empirical methods in argument-based research articles on digital technologies in medicine, health care and public health that have been published in high-impact bioethics journals. We focus on articles reporting non-empirical research in original contributions. Our aim is to describe currently used methods for the ethical analysis of ethical issues regarding the application of digital technologies in medicine, health care and public health. We confine our analysis to non-empirical methods because empirical methods have been well-researched elsewhere. Finally, we discuss our findings against the background of established methods for health technology assessment, the lack of a typology for non-empirical methods as well as conceptual and methodical change in bioethics. Our descriptive results may serve as a starting point for reflecting on whether current ethical frameworks and non-empirical methods are appropriate to research ethical issues deriving from the application of digital technologies in medicine, health care and public health.

Australian Attitudes Towards Waivers of Consent Within the Context of Genomic Data Sharing

This research identifies the circumstances in which Human Research Ethics Committees (HRECs) are trusted by Australians to approve the use of genomic data - without express consent - and considers the impact of genomic data sharing settings, and respondent attributes, on public trust. Survey results (N = 3013) show some circumstances are more conducive to public trust than others, with waivers endorsed when future research is beneficial and when privacy is protected, but receiving less support in other instances. Still, results imply attitudes are influenced by more than these specific circumstances, with different data sharing settings, and participant attributes, affecting views. Ultimately, this research raises questions and concerns in relation to the criteria HRECs use when authorising waivers of consent in Australia.

Advancing AI in healthcare: A comprehensive review of best practices

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the healthcare sector. This review considers twelve key aspects of AI in clinical practice: 1) Ethical AI; 2) Explainable AI; 3) Health Equity and Bias in AI; 4) Sponsorship Bias; 5) Data Privacy; 6) Genomics and Privacy; 7) Insufficient Sample Size and Self-Serving Bias; 8) Bridging the Gap Between Training Datasets and Real-World Scenarios; 9) Open Source and Collaborative Development; 10) Dataset Bias and Synthetic Data; 11) Measurement Bias; 12) Reproducibility in AI Research. These categories represent both the challenges and opportunities of AI implementation in healthcare. While AI holds significant potential for improving patient care, it also presents risks and challenges, such as ensuring privacy, combating bias, and maintaining transparency and ethics. The review underscores the necessity of developing comprehensive best practices for healthcare organizations and fostering a diverse dialogue involving data scientists, clinicians, patient advocates, ethicists, economists, and policymakers. We are at the precipice of significant transformation in healthcare powered by AI. By continuing to reassess and refine our approach, we can ensure that AI is implemented responsibly and ethically, maximizing its benefit to patient care and public health.

Asking questions that matter – Question prompt lists as tools for improving the consent process for neurotechnology clinical trials

Implantable neurotechnology devices such as Brain Computer Interfaces (BCIs) and Deep Brain Stimulators (DBS) are an increasing part of treating or exploring potential treatments for neurological and psychiatric disorders. While only a few devices are approved, many promising prospects for future devices are under investigation. The decision to participate in a clinical trial can be challenging, given a variety of risks to be taken into consideration. During the consent process, prospective participants might lack the language to consider those risks, feel unprepared, or simply not know what questions to ask. One tool to help empower participants to play a more active role during the consent process is a Question Prompt List (QPL). QPLs are communication tools that can prompt participants and patients to articulate potential concerns. They offer a structured list of disease, treatment, or research intervention-specific questions that research participants can use as support for question asking. While QPLs have been studied as tools for improving the consent process during cancer treatment, in this paper, we suggest they would be helpful in neurotechnology research, and offer an example of a QPL as a template for an informed consent tool in neurotechnology device trials.