Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding.

BACKGROUND: Although advanced analytical techniques falling under the umbrella heading of artificial intelligence (AI) may improve health care, the use of AI in health raises safety and ethical concerns. There are currently no internationally recognized governance mechanisms (policies, ethical standards, evaluation, and regulation) for developing and using AI technologies in health care. A lack of international consensus creates technical and social barriers to the use of health AI while potentially hampering market competition. OBJECTIVE: The aim of this study is to review current health data and AI governance mechanisms being developed or used by Global Digital Health Partnership (GDHP) member countries that commissioned this research, identify commonalities and gaps in approaches, identify examples of best practices, and understand the rationale for policies. METHODS: Data were collected through a scoping review of academic literature and a thematic analysis of policy documents published by selected GDHP member countries. The findings from this data collection and the literature were used to inform semistructured interviews with key senior policy makers from GDHP member countries exploring their countries' experience of AI-driven technologies in health care and associated governance and inform a focus group with professionals working in international health and technology to discuss the themes and proposed policy recommendations. Policy recommendations were developed based on the aggregated research findings. RESULTS: As this is an empirical research paper, we primarily focused on reporting the results of the interviews and the focus group. Semistructured interviews (n=10) and a focus group (n=6) revealed 4 core areas for international collaborations: leadership and oversight, a whole systems approach covering the entire AI pipeline from data collection to model deployment and use, standards and regulatory processes, and engagement with stakeholders and the public. There was a broad range of maturity in health AI activity among the participants, with varying data infrastructure, application of standards across the AI life cycle, and strategic approaches to both development and deployment. A demand for further consistency at the international level and policies was identified to support a robust innovation pipeline. In total, 13 policy recommendations were developed to support GDHP member countries in overcoming core AI governance barriers and establishing common ground for international collaboration. CONCLUSIONS: AI-driven technology research and development for health care outpaces the creation of supporting AI governance globally. International collaboration and coordination on AI governance for health care is needed to ensure coherent solutions and allow countries to support and benefit from each other's work. International bodies and initiatives have a leading role to play in the international conversation, including the production of tools and sharing of practical approaches to the use of AI-driven technologies for health care.

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

The NICE Evidence Standards Framework for digital health and care technologies - Developing and maintaining an innovative evidence framework with global impact.

OBJECTIVE: In 2018, the UK National Institute for Health and Care Excellence (NICE), in partnership with Public Health England, NHS England, NHS Improvement and others, developed an evidence standards framework (ESF) for digital health and care technologies (DHTs). The ESF was designed to provide a standardised approach to guide developers and commissioners on the levels of evidence needed for the clinical and economic evaluation of DHTs by health and care systems. METHODS: The framework was developed using an agile and iterative methodology that included a literature review of existing initiatives and comparison of these against the requirements set by NHS England; iterative consultation with stakeholders through an expert working group and workshops; and questionnaire-based stakeholder input on a publicly available draft document. RESULTS: The evidence standards framework has been well-received and to date the ESF has been viewed online over 55,000 times and downloaded over 19,000 times. CONCLUSIONS: In April 2021 we published an update to the ESF. Here, we summarise the process through which the ESF was developed, reflect on its global impact to date, and describe NICE's ongoing work to maintain and improve the framework in the context for a fast moving, innovative field.

The ethics of AI in health care: A mapping review.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be 'ethically mindful? A series of screening stages were carried out-for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)-yielding a total of 156 papers that were included in the review. We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new 'AI winter' could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

Emergence of New Disease: How Can Artificial Intelligence Help?

Emergence of new disease remains a critical parameter in human health and society. Advances in artificial intelligence (AI) allow for rapid processing and analysis of massive and complex data. In this forum article, the recent applications across disease prediction and drug development in relation to the COVID-19 pandemic are reviewed.

Junior physician's use of Web 2.0 for information seeking and medical education: a qualitative study.

BACKGROUND: Web 2.0 internet tools and methods have attracted considerable attention as a means to improve health care delivery. Despite evidence demonstrating their use by medical professionals, there is no detailed research describing how Web 2.0 influences physicians' daily clinical practice. Hence this study examines Web 2.0 use by 35 junior physicians in clinical settings to further understand their impact on medical practice. METHOD: Diaries and interviews encompassing 177 days of internet use or 444 search incidents, analyzed via thematic analysis. RESULTS: Results indicate that 53% of internet visits employed user-generated or Web 2.0 content, with Google and Wikipedia used by 80% and 70% of physicians, respectively. Despite awareness of information credibility risks with Web 2.0 content, it has a role in information seeking for both clinical decisions and medical education. This is enabled by the ability to cross check information and the diverse needs for background and non-verified information. CONCLUSION: Web 2.0 use represents a profound departure from previous learning and decision processes which were normally controlled by senior medical staff or medical schools. There is widespread concern with the risk of poor quality information with Web 2.0 use, and the manner in which physicians are using it suggest effective use derives from the mitigating actions by the individual physician. Three alternative policy options are identified to manage this risk and improve efficiency in Web 2.0's use.

Health 2.0 and Medicine 2.0: tensions and controversies in the field.

BACKGROUND: The term Web 2.0 became popular following the O'Reilly Media Web 2.0 conference in 2004; however, there are difficulties in its application to health and medicine. Principally, the definition published by O'Reilly is criticized for being too amorphous, where other authors claim that Web 2.0 does not really exist. Despite this skepticism, the online community using Web 2.0 tools for health continues to grow, and the term Medicine 2.0 has entered popular nomenclature. OBJECTIVE: This paper aims to establish a clear definition for Medicine 2.0 and delineate literature that is specific to the field. In addition, we propose a framework for categorizing the existing Medicine 2.0 literature and identify key research themes, underdeveloped research areas, as well as the underlying tensions or controversies in Medicine 2.0's diverse interest groups. METHODS: In the first phase, we employ a thematic analysis of online definitions, that is, the most important linked papers, websites, or blogs in the Medicine 2.0 community itself. In a second phase, this definition is then applied across a series of academic papers to review Medicine 2.0's core literature base, delineating it from a wider concept of eHealth. RESULTS: The terms Medicine 2.0 and Health 2.0 were found to be very similar and subsume five major salient themes: (1) the participants involved (doctors, patients, etc); (2) its impact on both traditional and collaborative practices in medicine; (3) its ability to provide personalized health care; (4) its ability to promote ongoing medical education; and (5) its associated method- and tool-related issues, such as potential inaccuracy in enduser-generated content. In comparing definitions of Medicine 2.0 to eHealth, key distinctions are made by the collaborative nature of Medicine 2.0 and its emphasis on personalized health care. However, other elements such as health or medical education remain common for both categories. In addition, this emphasis on personalized health care is not a salient theme within the academic literature. Of 2405 papers originally identified as potentially relevant, we found 56 articles that were exclusively focused on Medicine 2.0 as opposed to wider eHealth discussions. Four major tensions or debates between stakeholders were found in this literature, including (1) the lack of clear Medicine 2.0 definitions, (2) tension due to the loss of control over information as perceived by doctors, (3) the safety issues of inaccurate information, and (4) ownership and privacy issues with the growing body of information created by Medicine 2.0. CONCLUSION: This paper is distinguished from previous reviews in that earlier studies mainly introduced specific Medicine 2.0 tools. In addressing the field's definition via empirical online data, it establishes a literature base and delineates key topics for future research into Medicine 2.0, distinct to that of eHealth.