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Matulionyte, Rita --- "A Study on Explainable AI in Healthcare: A Brief Report" [2022] ANZCompuLawJl 9; (2022) 94 Computers & Law, Article 9





Despite its exponential growth, artificial intelligence (AI) in healthcare faces various challenges. One of the problems is a lack of transparency and explainability around healthcare AI. This arguably leads to insufficient trust in AI technologies, quality, and accountability and liability issues. In our pilot study we examined whether, why, and to what extent AI explainability is needed with relation to AI-enabled medical devices and their outputs. Relying on a critical analysis of interdisciplinary literature on this topic and a pilot empirical study, we conclude that the role of technical explainability in the medical AI context is a limited one. Technical explainability is capable to addresses only a limited range of challenges associated with AI and is likely to reach fewer goals than sometimes expected. The study shows that, instead of technical explainability of medical AI devices, most stakeholders need more transparency around its development and quality assurance process.



AI technologies, such as machine learning (ML), are gaining importance in healthcare. AI-enabled medical applications have been developed that promise to: improve diagnosis; assist in the treatment and prediction of diseases; and improve clinical workflow. AI-enabled medical devices are expected to comply with a number of ethical principles and policy recommendations, such as benevolence, privacy and protection of data, safety, fairness, accountability and responsibility, avoidance of bias, governance, and others. A sought-after principle is that of transparency and/or explainability, which is found in most ethical AI guidelines.[1] Generally speaking it mandates that certain information about AI in healthcare should be made available and that outcomes of AI tools should be explainable and interpretable.

In response to this, computer scientists have been working to develop AI explainability techniques, with some of them focusing specifically on explainable AI (XAI) in the healthcare sector. In order to ensure explainability of complex and thus intrinsically inexplainable algorithms (such as those based on deep learning and artificial neural networks) and their outcomes, numerous so-called post-hoc XAI approaches and techniques have been developed and discussed in the literature. [2] At the same time, the literature has shown signs of increasing disagreement as to whether explainability should be a required feature of AI devices, including those intended for the healthcare sector. While some commentators argue that the black box nature of AI-enabled medical devices has led to a lack of trust and quality, and, consequently, a slow adoption of these technologies in practice, others are increasingly suggesting that AI explainability is not a necessary or adequate measure in ensuring the quality of AI or, indeed, the trust in AI.[3]

The aim of this study was to examine whether, why, and to what extent AI explainability should be demanded with relation to AI-enabled medical devices and their outputs. To achieve this aim, we posed the following questions: First, what exactly an AI explainability principle means and how it could be delineated from other terms, such as transparency and interpretability; second, what goals AI explainability can be expected to achieve and which stakeholders will likely benefit from AI explainability; and finally, is AI explainability capable of achieving the identified goals or does it merely create a ‘false hope’, as suggested by some commentators?

In the study, we adopted a dual methodology. First, we have reviewed, synthesised, and critically analysed medical and computer science literature exploring the question of explainability of AI-enabled medical devices. Secondly, we adopted the Focus Group method to supplement our analysis with first-hand empirical data. We organized two pilot focus group discussions (5-6 participants each) to collect views from clinicians, AI developers and policy makers on the need of explainability for AI-enabled medical devices.

This study was conducted by an interdisciplinary team: Dr Rita Matulionyte (Macquarie Law School, Macquarie University), Paul Nolan (Macquarie Law School, Macquarie University), Prof Farah Magrabi (Australian Institute for Health Innovation) and Prof Amin Beheshti (School of Computing, Macquarie University).


Since ‘AI explainability’ does not have an agreed definition and various meanings of it are provided in different contexts, we first developed the definition to be used in this study. We noted that both in literature and in policy documents, AI explainability is sometimes used as a synonym to AI transparency, while in other instances it is delineated from the latter. In our study we distinguish between AI explainability and AI transparency principles. We refer to ‘AI explainability’ in a narrow sense, as an explanation of how an AI system generates outputs, which in most cases will require using specific explainable AI (XAI) approaches or techniques. This is similar to ‘technical explainability’ as defined by the EU Principles on Trustworthy AI.[4] In contrast, we understand ‘transparency’ as a requirement to provide information about the model. It may require disclosing very general information such as ‘when AI is being used (in a prediction, recommendation or decision, or that the user is interacting directly with an AI-powered agent, such as a chatbot)’[5] or more specific information about the AI use, its technical configuration, limitations, etc.

After clarifying the concept of explainability, we identified the main reasons, as proposed in the literature, why and by whom technical explainability of AI medical devices could be required. We identified 4 main rationales for explainable AI, as discussed in legal, healthcare and computer science literature: trust in technology; patient autonomy and clinician-patient relationship; quality of AI and improved clinical decision making; and accountability and liability.

First, the ‘black box’ nature of AI arguably fails to elicit trust, both among clinicians and their patients. If clinicians cannot interpret and understand the decision made by AI, such as a diagnosis or a treatment recommendation, or if they cannot understand the criteria taken into account when making the decision, trust and reliance issues will arise.[6] Secondly, a lack of explainability, arguably, is incompatible with patient-centered medicine, as it adversely affects both a patient’s ability to make informed decisions and the clinician-patient relationship. [7] Thirdly, the lack of explanation may arguably lead to technical errors or bias in AI that, due to the opaque nature of AI, cannot be readily identified by technical or medical specialists.[8] Such errors or bias, if AI is applied to numerous cases, could lead to harm to multiple patients. Finally, many experts cite explainable AI as the answer to ensuring professional accountability and determining legal liability for wrong decisions generated by AI.[9] Arguably, the opaque nature of AI arguably leads to problems in defining moral accountability and legal liability as it makes it unclear as to who would be held accountable for harm caused by a black box algorithm – the clinician, the AI developer, both, or none of them. Explainable AI would arguably help more clearly and appropriately allocate accountability for incorrect AI decisions.

As a next step, we critically analysed these rationales for explainable AI in healthcare and, through focus group discussions, examined whether stakeholders (clinicians, patients, policy makers) agree with these propositions. We made four main conclusions.

First, AI explainability is not the only (or the best) way to ensure trust in AI among clinicians. We argue that a causal explanation is not always necessary in clinical decision-making as clinicians have traditionally used or relied on technologies that they do not fully understand. For instance, physicians and others rely on laboratory test results in their decision making, even if they do not precisely know how the pathology laboratory testing works. Similarly, clinicians routinely prescribe pharmaceutical interventions without knowing their specific mechanisms of action. Further, XAI techniques are still facing a number of technical challenges and are yet to attain sufficient certainty, and therefore the explanations that they produce cannot be themselves trusted. [10] In additional, we suggest that there are more optimal alternatives to ensure trust in AI systems. Empirical research suggests that trust of the AI system in healthcare could be ensured by, e.g., building relationships with stakeholders from the beginning of the project to the final implementation stage; by respecting professional discretion and elevating the expertise of stakeholders rather than replacing them with technology; and by creating ongoing information feedback loops with stakeholders.[11]

Second, we argue that a lack of explainability does not inhibit patient autonomy, nor their relationship with the clinician or trust in the medical system generally. While patients may need certain information about AI technology, like any other technologies applied in the healthcare sector, the information they would require would fall under the ‘transparency’ concept defined above, rather than a technical explainability concept on which we focused in this study.

Third, we contend that XAI techniques may be helpful in ensuring the quality of AI during the development process, but the utility of XAI techniques in eliminating AI errors in a clinical setting is questionable. We agree that XAI may be useful, or perhaps even necessary, for AI developers in ensuring the quality, accuracy, and absence of bias when developing AI modules. However, it is questionable whether XAI techniques could help clinicians to improve clinical decision making. In most if not all instances, XAI techniques and their outputs cannot be understood and interpreted by those lacking AI expertise, such as clinicians.[12] Also, empirical evidence suggests that additional explainability features do not necessarily improve clinical decision making. [13] In addition, explanations may lead to an over-trust and over-reliance on the technology, thereby introducing a risk of missing obvious mistakes.

Finally, we also question the need of explainability functions to clearly allocate accountability and liability among different stake holders (clinician, AI developer and healthcare provider institution). We submit that it is yet not clear how explainability functions in an AI-enabled medical device will ultimately affect the determination of liability. Explainability of an AI system is something that, from a legal perspective, potentially cuts both ways: it could both decrease the potential for errors, negative patient outcomes and associated liability for clinicians, or it could increase the standard of care demanded of clinicians, leading to the potential to breach their duty.


These findings suggest that the role of an AI explainability principle in the medical AI context is a limited one. Technical explainability, as we define it here, can address only a limited range of challenges associated with AI and is likely to reach fewer goals than sometimes expected. This should be considered by policy makers when making demands for AI-enabled medical devices to be explainable, and by companies and data scientists when deciding whether to integrate an explainability function in an AI-enabled medical device.

The full reference to the report is: R Matulionyte, P Nolan, F Magrabi, A Beheshti, ‘Should AI Medical Devices be Explainable?’, 30(2) International Journal of Law and Information Technology, 151-180 (2022). A pre-print copy could be accessed via: <>.

[*] Senior Lecturer, Macquarie Law School, Macquarie University.

[1] Eg Australia’s Artificial Intelligence Ethics Framework (2022), <> (‘There should be transparency and responsible disclosure so people can understand when they are being significantly impacted by AI and can find out when an AI system is engaging with them.).

[2] Eg J Amann et al.Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective’ (2020) 20 BMC Med Inform Decision Making 310.

[3] Eg A J London, ‘Artificial Intelligence and Black‐Box Medical Decisions: Accuracy Versus Explainability’, (2019) 49(1) Hastings Center Report 15-21.

[4] Eg European Commission, ‘Ethics Guidelines for Trustworthy AI’ (2019), <>.

[5] OECD, Recommendation of the Council on AI (2022), para 1.3, <>.

[6] See eg K Rasheed et al, ‘Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey’, (2021) Comput Biol Med. 2.

[7] J C Bjerring, J Busch, ‘Artificial Intelligence and Patient-Centered Decision-Making’, (2021) 34(2) Philosophy & Technology 349-371.

[8] H Maslen, ‘Responsible Use of Machine Learning Classifiers in Clinical Practice’, (2019) 27(1) Journal of Law and Medicine 37-49.

[9] Eg M Sendak et al, ‘The human Body is a Black Box: Supporting Clinical Decision-Making with Deep Learning’, (Conference paper, Fairness, Accountability, and Transparency, 2020, 99-109, 101).

[10] J J Wadden, ‘Defining the Undefinable: The Black Box Problem in Artificial Healthcare’, (2021) J Med Ethics 2.

[11] Sendak et al (n 9) 100.

[12] See e.g. interpretability analysis by E Zihni et al, ‘Opening the Black Box of Artificial Intelligence for Clinical Decision Support: A study Predicting Stroke Outcome’, (2020) 15(4) Plos one e0231166.

[13] Eg H J Weerts et al, ‘A Human-Grounded Evaluation of SHAP for Alert Processing’, (2019) arXiv preprint arXiv:1907.03324.

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