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13. Health data and new technologies

Introduction

The increase of digital data collection and computational speed has facilitated the rapid development of analytical tools and techniques to gain additional insight from data. Statistical analysis, machine learning, and artificial intelligence are terms used to describe a spectrum of analytic techniques that range from traditional statistical analysis through to evolving approaches such as deep learning.

However, for the purposes of this document ‘artificial intelligence’ (AI) will be used to encompass approaches that are algorithmically-driven. This definition is intentionally broad as AI is often used as an umbrella term to refer to a number of techniques, encompassing everything from machine learning and natural language processing to expert systems and vision. In these Standards, the term ‘AI’ covers all these techniques.

AI is a rapidly evolving science, and the application of AI in healthcare has the potential to significantly transform healthcare delivery at all steps of the patient journey. Potential or realised applications would cover prediction of illness in the presently well individual through diagnosis to death, and will touch on all aspects of population health, system planning, service delivery, and individual medical specialities.

While offering great opportunity, these emerging technologies present defined and presently undefined risks, and the evolving science of AI presents difficulty in outlining explicit ethical standards. Therefore, this section will first frame the general principles guiding the ethics of biomedicine as they apply to AI, then frame standards applying these principles to specific circumstances. All researchers employing health data in AI systems throughout the AI life cycle as outlined in Figure 13.1 should refer to the ethical principles described below in the absence of a standard that directly applies to their case. The standards in this chapter are also likely to be updated periodically. Researchers are encouraged to check for updates prior to submitting applications which involve the use of AI for ethical review.

Figure 13.1 – The AI life cycle

The AI life cycle has 4 stages: data-preprocessing, model development, model valudation and model implementation. Data pre-processing includes evaluate for data bias, representativeness, missingness, exclusion and measurement. Model development includes what the model is optimising for - and what are the alternatives, what are the impacts of included and excluded features, is the model transparent, does the method allow explanation and is the output interpretable. Model validation includes validate in populations representative of the implementation population, use of best-practices validation prior to any implementation and consider prospective validation based on risk. Model implementation includes consider an output audit plan, does the model behave in a way you expect, does the model impact different populations differently and how will the post-implementation model impact data informing the model.

An AI project can be broken down into series of interdependent steps:

  • data pre-processing is the curation and refining of raw data into a data set that can be utilized in subsequent model development. This step can be automated or human determined
  • model development is the selection of specific methods to analyse the data, decisions made to train the underlying statistical model, and selection of model parameters and hyperparameters
  • model validation is the process of determining the model performance in a data set different from the data set in which the model was trained. This can be done retrospectively or in a prospective population
  • model implementation is the application of the validated model into a live environment, where the output of the model impacts something outside the model itself. This is the highest risk step in the AI life cycle.

Not all AI projects will touch on all elements of this life cycle.

Ethical principles in the context of AI

Given the breadth of situations to which AI is being applied, the emerging use of AI systems in healthcare raises a number of potential ethical challenges. Other jurisdictions have highlighted that AI interfaces with the themes of consent, autonomy, privacy, fairness, bias, justice, transparency, reliability, accountability and liability (Nuffield Council on Bioethics 2018; Fenech et al 2018). As healthcare systems and healthcare delivery increasingly become supported by, integrated with, or delegated to AI systems it is critical that they are in line with the fundamental societal values that shape healthcare delivery and research ethics as well as individual rights. Ethical reflection about AI should be grounded on the ethical principles and concepts applicable to health data generally.

Certain aspects of AI development create unique difficulties in foreseeing the impact of the technology on some of these principles. The concepts around algorithmic transparency, interpretability, and explainability are presently evolving – leading to so-called ‘black-box’ problems, where the impact of any specific data structure or element on the final algorithmic output is obscured by the methodology itself. The above principles can still be applied in this circumstance, but may not be applicable by design, only by transparency of the impact of AI implementation in the broader biomedical and societal context.

13.1 While the state of AI does not guarantee built-in explainability, researchers must ensure that the processes

Use of data

Data is used to develop the algorithms supporting AI, and in self-learning approaches is used in implementation. This data may be from traditional healthcare domains, such as clinical activities in healthcare environments, data generated explicitly or as a by-product of screening, diagnosis, and treatment. This may include demographics, medical notes, electronic recordings from medical devices, data from physical examinations and clinical laboratory data, imaging and genetic testing (Jiang et al. 2017). AI systems may merge these health data sources with non-health data, such as social media, locational data, and socio-economic data.

13.2 While individual datasets may be non-identifiable, as data sets are merged, AI researchers should be conscious that methodologies presently allow identification of even non-identified data contributors if the dataset is sufficiently linked with a high degree of accuracy.

13.3 Researchers of AI need to carefully consider the nature of the data, as well as the people who are accessing and using the data.

Risk considerations of AI outputs

13.4 Researchers must carefully consider the risk of harm the use of AI may cause to participants.

A useful framework for risk categorisation has been developed by the International Medical Device Regulators Forum (IMDRF) for Software used as a Medical Device (SaMD). According to the SaMD risk framework, consideration should be given to the following two major factors which help to describe the intended use of the AI which, in turn, helps to inform the risk its outputs may have on participants:

  1. The significance of the information provided by the AI and whether it is to:
    • treat or to diagnose;
    • drive clinical management; or
    • inform clinical management.
  2. The state of the situation or condition for which the AI is intended to be used, namely, is it intended to be used in a:
    • critical situation or condition;
    • serious situation or condition; or
    • non-serious situation or condition[1]
State of healthcare situation or condition Significance of information provided by SaMD to healthcare decision
Treat or diagnose Drive clinical management Inform clinical management
Table 13.1 – Risk matrix
Critical IV III II
Serious III II I
Non-serious II I I

After determining the significance of (1) the information provided by the AI to the healthcare decision, and (2) the state of the healthcare situation or condition for which the AI is intended to be used, Table 13.1 SaMD risk matrix provides guidance on the levels of impact the AI may have on participants.

The four categories (I, II, III, IV) are in relative significance to each other; Category IV has the highest level of impact while Category I has the lowest impact.[2]

General considerations

These standards apply to data used for pre-processing, model development and validation and implementation of AI.

13.5 Researchers must ensure that the intended use of AI is fair and is intended to benefit New Zealanders (Stats NZ and Privacy Commissioner 2018). They must identify the risks and benefits of the AI system, paying particular attention to ensuring it does not contribute to inequalities, for example by negatively discriminating against classes of individuals or groups.

13.6 Researchers must be mindful of the need to consult with Māori as partners, and of the need to consult with all relevant researchers to ensure they manage data use involving AI systems in a trustworthy, inclusive and protected way (Stats NZ and Privacy Commissioner 2018).

13.6.a Consultation is especially important if the research is a partnership between private and public organisations. In this case, researchers must clearly identify the aims and goals of each contributing partner, together with information about who will have access to what data and for what purposes, and who is accountable.

13.7 Intended use of AI should be explained in language that is clear, simple and easy to understand to those not directly involved in the AI lifecycle.

13.8 The source of the data, particularly with regards to quality, completeness, representativeness, and risk of bias, should be evaluated.

13.8.a Design and the implementation of measures to correct and mitigate risks arising from data bias should be considered.

13.9 The data should be evaluated with regards to identifiability and risk of re-identification.

13.10 Data used in an AI life cycle must be safeguarded with both data security and integration of appropriate levels of security into data storage and each element of the AI life cycle, including against adversarial attacks.

13.11 Data used in AI should have a plan for storage, reuse, destruction or retention, and should have effective and robust data management plans in place.

13.12 Project-specific data governance policies and procedures should adhere to local, organisational, regional, and national data governance requirements.

Transparency, explainability, and interpretability

13.13 Researchers must be transparent about methodologies used in the AI life cycle. There should be justification for a specific method, an accounting for the specific risks and limitations of the methodology, and the consideration of alternative approaches.

13.14 Model development and choices of methodology should be clear in their optimisation parameters and explainability of input and output to model.

13.15 AI implemented in live environments are strongly encouraged to have a monitoring and audit plan in place to assess issues of safety, accuracy, bias and fairness.

13.15.a Insight into the drivers of AI output is at times limited by the methodology itself – concepts such as black-box algorithms, explainability, and transparency are set against issues such as fairness and bias. However, such approaches do not remove ethical accountability from the researchers. Current approaches to accessing and evaluating risk in this setting recommend ongoing audit of input and output.

13.15.b Measures to mitigate risks arising in safety, accuracy, bias and fairness resulting from the application of AI should be in place prior to implementation.

13.15.c Algorithms should be transparent about the involvement of automated input-output loops versus human-in-the-loop designs. Provision for human input and oversight should be integrated into design or implementation governance structures. If there is no such provision, this absence must be justified.

Human oversight and accountability

13.16 Researchers must clearly identify who is accountable for each step of the AI life cycle, and how they are accountable.

13.16.a Accountability in this context references both accountability for algorithmic behaviour/output, and accountability for subsequent actions based on the algorithm. Clear lines of accountability for addressing safety, accuracy, bias and fairness involving the AI should be in place prior to any deployment/implementation.

  • Organisations involved in an AI lifecycle should have clear lines of responsibility for each step of the AI life cycle.
  • AI in a public-private partnership should have clear delineation of responsibility between the partners.

Standards for an AI life cycle

13.17 Prior to data pre-processing, model development and validation, researchers using AI should account for the following:

  • the source of the data to be used by the AI system (e.g. clinical notes, imaging, genetics or laboratory results)
  • attention should be paid to issues of bias (having particular regard to the quality, completeness and representativeness of the data)
  • a clear description of methodology/methodologies to be used in the project. A justification for the method/methods, limitations, risks, and optimisation parameters should be presented
  • an explanation of how the data will be pre-processed and why, and an identification of any protected attributes
  • the identifiability of the data (i.e. identifiable, re-identifiable or non-identifiable) and, if the data is re-identifiable, a description of the risks of re-identification, and of the measures used to mitigate those risks
  • measures the researchers will take to mitigate risks identified, especially in terms of correcting bias
  • an explanation of how the researchers will determine accountability for safety, accuracy, bias and fairness issues
  • a description of how the AI system will be validated.

13.18 Prior to implementation, researchers using AI should account for the following:

  • an explanation of how the proposed use will be monitored for safety, accuracy, bias and fairness, including measures used to assess and why those measures have been selected, paying particular regard to whether certain groups would be advantaged or disadvantaged by the method(s)
  • a plan for ongoing audit in implementation. This is particularly relevant for settings where algorithmic updating is dependent upon data created by the implementation
  • there should be a list of the people and organisations involved in the AI life cycle, including their qualifications
  • an audit and monitoring plan for the AI to assess issues like safety, accuracy, bias and fairness; how often the auditing and monitoring will take place; and who will be responsible for it
  • an explanation of how the researchers will determine accountability for safety, accuracy, bias and fairness issues
  • a statement as to whether there will be human oversight of the AI system, and, if so, what the oversight comprises, the stages at which it will occur and how the oversight will be implemented.