\u201cI can look at my patient instead of my keyboard.\u201d\u00a0<\/p>\n
This is just one example of how AI has supported Dr Janice Tan, Deputy Chair of RACGP Digital Health Special Interest Group<\/a>, who uses AI mostly for paperwork and administrative work, including an AI scribe in most consultations.<\/p>\n Artificial intelligence (AI) has become a common support tool in many medical settings, with even more growth and innovation to be seen because of its rapidly expanding technological capabilities. \u00a0<\/p>\n The Australian Commission on Safety and Quality in Health Care (the Commission) recognises that while artificial intelligence (AI) has the potential to support healthcare delivery, it also presents key ethical challenges for clinicians.\u00a0<\/p>\n Macquarie University authors of a scoping review<\/a>, report that, \u201cAI is being used to make influential clinical and operational decisions through tasks such as predictive analysis, diagnostic assistance, or optimisation of administrative processes.<\/p>\n \u201cThese decisions may change resource allocation decisions or diagnosis and treatment pathways, raising concerns over liability in instances of harm as lines of responsibility remain unclear in legal guidelines.<\/p>\n \u201cNew risks for patient safety have also emerged from AI systems, with the potential for errors arising from data drift, hidden biases, and model \u2018hallucination, not adequately addressed by existing governance. In a high-risk industry like healthcare, such errors could have serious widespread or fatal consequences.\u201d<\/p>\n \u00a0Associate Professor Ash Hopkins<\/a>, an NHMRC Investigator and leader of Flinders\u2019 Clinical Cancer Epidemiology Lab, says, \u201cAI holds enormous promise but must be applied responsibly.\u201d\u00a0<\/p>\n Understanding the ethics of using AI in healthcare should assist clinicians.<\/p>\n AI ethics principles in healthcare\u00a0<\/p>\n The Australian Government\u2019s AI Ethics Principles<\/a> are a helpful guide for understanding the ethics of AI for all Australians. Meanwhile, Macquarie University\u2019s scoping review<\/a> examined the frameworks governing AI across the lifecycle within healthcare organisations.<\/p>\n It found the most common ethics principles, such as transparency, fairness and equity, privacy and security, accountability, and beneficence, aligned closely with established AI ethics frameworks. However, subthemes specifically relevant to healthcare, such as consent, confidentiality, and medicolegal liability, were not commonly included. Noticeably, some critical ethics principles, such as contestability, sustainability and cultural safety, were absent from more than three-quarters of the frameworks reviewed.<\/p>\n Accountability<\/p>\n The review found that the oversight mechanism (such as an AI-specific governance committee) was the least common ethics component.\u00a0<\/p>\n According to the Commission,\u00a0a key challenge is maintaining accountability for clinical decisions. While AI can support clinical practice, responsibility for patient care remains with the treating clinician. As outlined by the Commission\u2019s Pragmatic AI Guidance for Clinicians<\/a> (the Guidance), this means applying professional judgement and critically evaluating recommendations or outputs generated by AI tools.<\/p>\n Dr Tan says she never lets AI make the decision. \u201cIt doesn\u2019t diagnose, doesn\u2019t prescribe, and nothing it writes leaves my practice until I\u2019ve read it. For evidence lookups, I go to the source. If a tool can\u2019t show me where something came from or if I am uncertain that the reference is valid, I don\u2019t use it.\u201d<\/p>\n Patient harm from AI-assisted clinical decisions\u00a0<\/p>\n This oversight is important, as a situation may arise in which an AI-assisted decision causes harm. In this situation, Professor of Law Jeannie Marie Paterson<\/a>, and Co- director of Melbourne University\u2019s Centre for Artificial Intelligence and Digital Ethics (CAIDE), says there are three possible parties who may be responsible: the clinician, the clinic, and the manufacturer\/developer. \u201cIn all cases, liability doesn\u2019t attach merely because there is a poor outcome.<\/p>\n \u201cThe liability of the clinician (and the clinic) depends on negligence (a tort) and a breach of s 60 of the Australian Consumer Law. The clinician must have failed to exercise reasonable care and skill in using the tool. There are as yet no cases. It is likely that the clinician would have to exercise independent judgment about whether the tool is suitable for use and more broadly understand the limitations of the tool and the propensity for error.\u00a0<\/p>\n \u201cFor example, AI clinical assisted decision tools may provide accuracy and confidence ratings – the clinical should understand what these mean and exercise skill in deciding how to use the recommendations.\u201d<\/p>\n Jeannie explains that the liability of the developer will commonly be based in product liability under the Australian Consumer Law, which can apply to software. \u201cThe question would be whether the tool has a safety defect and is not as safe as the community reasonably would expect.\u201d<\/p>\n Safety<\/p>\n