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Artificial intelligence is no longer a future concept in healthcare; it is already reshaping clinical practice, decision-making, and patient experience across the world. A recent conference on AI healthcare risk responsibility examined the significant potential of this technology to improve patient outcomes, enhance efficiency, and support healthcare providers and clinicians in increasingly complex environments, alongside the associated risks, regulatory considerations, and legal implications from a global perspective.
How This Technology Is Impacting Patients and Clinicians
Studies of tools with “humans in the loop” are demonstrating that this collaboration is leading to better patient outcomes. However, when clinicians do not agree with a machine-generated decision, they need to be clear as to their reasoning, and there should be procedures in place, such as peer reviews, to oversee clinical decision-making in these scenarios. Where there is a difference of opinion which impacts treatment decisions, patients should be informed.
Why Clinicians Must Still “Know Their Patients”
Clinicians should continue to “know their patients” and use their clinical expertise and intuition when using these tools. Clinicians should be trained to understand the output in order to counsel patients as to clinical decisions made with algorithmic assistance, and they should ideally know what is in the “black box.” Clinicians are advised to only use tools that they have been trained to use, and which have been pre-approved by the healthcare organisation or provider.
A Targeted Approach to Implementation Within AI Healthcare Risk Responsibility
Implementation in healthcare needs a targeted approach. To drive value and positively impact patient outcomes, healthcare providers should identify the patient need or problem before introducing the solution. The clinical impact of these tools should be measured in real time by healthcare providers to monitor effectiveness before scaled implementation and to reduce patient risk.
Preserving Learning Opportunities Amid Automation
Automation of routine tasks through the use of this technology can help maximise opportunities for targeted patient care. Clinicians should receive supplemental training to ensure that the learning opportunities those tasks provided are not lost, for example, in the production of discharge summaries.
AI Scribes and Consent Considerations Within This Framework
Use of AI scribes within healthcare settings should be limited to those that have been approved for use by the healthcare provider and used with caution once training has been given. The clinician remains responsible for the notes created and for checking the note to ensure accuracy. Patients should be made aware of these tools being used in their treatment or diagnosis and be asked to consent in writing.
The Role of the National Commission Into AI Regulation
The National Commission into the Regulation of AI in Healthcare has been established by the Medicines and Healthcare products Regulatory Authority to advise upon the development of a new regulatory framework, however this process will take time, and regulation will need to catch up with the fast pace of technological change.
Medical Device Compliance and AI Healthcare Risk Responsibility for Providers
Regulatory requirements are in place for medical devices. To reduce the risk of harm and liability risk, healthcare providers should ensure medical devices in use in their settings have the highest classification approval and that they undertake adequate due diligence of the suppliers, with devices only being used for the purpose for which they are designed. It is essential to ensure that clinicians and staff are trained in how to use these tools, including medical devices, before they are integrated into the healthcare system.
Why Risk Mapping Matters at the Board Level
Healthcare providers should undertake risk mapping in respect of all medical devices and technology in use in their settings. This mapping process, which ideally should be undertaken from board level down, is necessary to ensure there are policies and codes of practice in place governing their use which are adequate for each speciality, that they are fully protected with regard to contractual indemnities with suppliers, and that they have indemnity cover.
Governance Structures Supporting AI Healthcare Risk Responsibility
Clear governance and risk mapping throughout the tool chain within each healthcare setting by speciality should help to ensure that if any incidents occur, they can be identified as quickly as possible in real time, traced to source and interrogated, so that harm can be mitigated and liability apportionment fast-tracked.
Why Sensitive Patient Data Access Presents a Real Challenge
For these tools in healthcare to reach their full potential, access to sensitive patient data for machine learning is key. Access rights around data use therefore present a very real challenge for public healthcare organisations and other healthcare providers. Patient consent must be obtained specifically for each element of the use of their data, and clinicians need to be informed as to how data is being used in order to communicate this to patients.
Cross-Border Data Storage Challenges Within AI Healthcare Risk Responsibility
Public healthcare organisations and other healthcare providers should also be aware of the challenges to be faced as to the storage of patient data and the use of patient data across borders. There remain restrictions in the UK on processing and using health data. Healthcare providers should ensure that their operational systems are compliant with data protection laws and that patient data is kept securely on the premises or in a private cloud where possible; public clouds present a greater security risk.
Contractual Protections When Sharing Data With Third Parties
If patient data is being disclosed to a third party, healthcare providers should have contractual restrictions in place which govern disclosure and use of data, alongside practical measures being adopted such as considering interface security, having guardrails which limit questions that these tools will answer, and ensuring as much as possible that they are protected against hacking and unauthorised access.
What This Means for Healthcare Organisations and Insurers
Taken together, these takeaways on AI healthcare risk responsibility point to a consistent theme: realising the benefits of this technology in healthcare depends on disciplined governance, not just technical capability. Public healthcare organisations, private providers, and insurers alike will need to balance the drive toward efficiency with rigorous attention to clinician training, medical device compliance, data security, and clear lines of accountability when incidents occur.
What to Watch Going Forward
As the National Commission into the Regulation of AI in Healthcare continues its work toward a formal regulatory framework, healthcare providers should not wait for that process to conclude before implementing their own risk mapping, governance structures, and consent protocols. Given how quickly these tools are being adopted across clinical settings, organisations that establish strong internal governance now may be better positioned to manage liability and patient safety risks while regulation continues to catch up with the pace of technological change.
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