Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019). The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. (2019) in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper.

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AIassisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data.

The Food and Drug Administration (FDA) has sought an injunction to prevent a US-based company from offering an autologous adult stem cell treatment for musculoskeletal and spinal injuries. Given the alarming number of clinics promoting stem-cell-based interventions, the outcome of this case could have wide-ranging implications.

BackgroundGenomic profiling of malignant tumours has assisted clinicians in providing targeted therapies for many serious cancer-related illnesses. Although the characterisation of somatic mutations is the primary aim of tumour profiling for treatment, germline mutations may also be detected given the heterogenous origin of mutations observed in tumours. Guidance documents address the return of germline findings that have health implications for patients and their genetic relations. However, the implications of discovering a potential but unconfirmed germline finding from tumour profiling are yet to be fully explored. Moreover, as tumour profiling is increasingly applied in oncology, robust ethical frameworks are required to encourage large-scale data sharing and data aggregation linking molecular data to clinical outcomes, to further understand the role of genetics in oncogenesis and to develop improved cancer therapies.ResultsThis paper reports on the results of empirical research that is broadly aimed at developing an ethical framework for obtaining informed consent to return results from tumour profiling tests and to share the biomolecular data sourced from tumour tissues of cancer patients. Specifically, qualitative data were gathered from 36 semi-structured interviews with cancer patients and oncology clinicians at a cancer treatment centre in Singapore. The interview data indicated that patients had a limited comprehension of cancer genetics and implications of tumour testing. Furthermore, oncology clinicians stated that they lacked the time to provide in depth explanations of the tumour profile tests. However, it was accepted from both patients and oncologist that the return potential germline variants and the sharing of de-identified tumour profiling data nationally and internationally should be discussed and provided as an option during the consent process.ConclusionsFindings provide support for the return of tumour profiling results provided that they are accompanied with an adequate explanation from qualified personnel. They also support the use of broad consent regiments within an ethical framework that promotes trust and benefit sharing with stakeholders and provides accountability and transparency in the storage and sharing of biomolecular data for research.Electronic supplementary materialThe online version of this article (10.1186/s40246-017-0127-1) contains supplementary material, which is available to authorized users.