ISO/TC 215 Task Force 5 has published a new white paper, “Healthcare-Specific Considerations for AI”, examining how healthcare environments shape the risks, responsibilities and evaluation of artificial intelligence systems and related standards.
Published in October 2026, the report argues that healthcare cannot be treated simply as another deployment environment for general-purpose AI principles. Clinical consequences, professional responsibilities, healthcare workflows, sensitive data, regulatory structures and patient trust can materially change how AI governance concepts need to be interpreted and applied.
Why Healthcare Requires Specific AI Considerations
The report notes that healthcare systems face increasing demand, constrained capacity, ageing populations and a growing burden of chronic disease.
AI may help extend the reach, efficiency and precision of care, but its use requires an appropriate balance between:
benefit;
safety;
accountability;
equity;
long-term value.
At the same time, AI is being deployed rapidly across healthcare institutions, clinical practice, research, public health and patient-facing environments.
General-purpose AI tools are also increasingly available directly to patients and the public, often outside traditional institutional or professional oversight.
Healthcare Is a Distinctive Deployment Domain
ISO/TC 215 highlights several characteristics that make healthcare particularly challenging for AI governance.
These include:
clinical uncertainty;
direct consequences for patient safety;
professional judgment;
interconnected clinical workflows;
fragmented information infrastructures;
sensitive health data;
regulatory oversight;
professional accountability;
patient vulnerability;
population-level equity considerations.
The report argues that these characteristics can fundamentally alter what common AI principles mean in practice.
AI Principles Need Healthcare-Specific Interpretation
The white paper focuses on several principles commonly found in international AI governance frameworks:
human-centred values and human oversight;
fairness and non-discrimination;
transparency and explainability;
safety, robustness and security;
accountability and responsibility;
privacy and data governance.
The report’s central argument is that sector-neutral AI principles are necessary, but may not be sufficient on their own when AI is deployed in healthcare.
Human Oversight Must Be Meaningful
ISO/TC 215 stresses that simply having a human involved in a decision does not automatically constitute meaningful oversight.
Clinicians may face:
automation bias;
cognitive load;
time pressure;
interface limitations;
incomplete information about AI uncertainty or performance.
Meaningful human oversight depends on whether clinicians can critically evaluate AI-generated recommendations and whether patients can meaningfully understand and participate in decisions affecting their care.
The report also notes that oversight may need to take place throughout an AI-mediated workflow, not simply at the final decision point.
Fairness Is Not the Same as Equal Treatment
The white paper makes an important distinction between fairness and statistical equality.
In healthcare, different treatment can be clinically appropriate because patients have different:
risks;
conditions;
needs;
clinical circumstances.
The key question is whether differences in AI outputs reflect legitimate clinical factors or whether they arise from biased datasets, proxy variables or historical inequalities.
ISO/TC 215 also warns that aggregate performance metrics can hide poor performance in underrepresented or complex patient populations.
Transparency Must Support Clinical Judgment
Transparency in healthcare AI is not simply about explaining how an algorithm works.
Clinicians may need information about:
uncertainty;
known limitations;
validation populations;
situations where performance varies;
factors influencing individual outputs.
An explanation can therefore be technically accurate but still inadequate if it does not provide the information needed for clinical decision-making.
The report also stresses that explanatory information must be available at the point of decision rather than being confined to separate technical documentation.
Transparency Extends Across the AI Lifecycle
AI systems can be updated, retrained or replaced over time.
For healthcare organisations, this creates a need to maintain information about:
which version was used;
what recommendation was generated;
what evidence supported the system;
what limitations were known at the time.
This makes lifecycle traceability and provenance important components of transparency and accountability.
Technical Performance Is Not the Same as Clinical Validity
A particularly important distinction in the report is between technical performance and clinical validity.
A system may reliably produce its intended output while still being inappropriate for a particular:
patient population;
clinical purpose;
healthcare setting;
point in the care pathway.
ISO/TC 215 therefore argues that AI safety cannot be assessed solely under controlled validation conditions.
Post-Deployment Monitoring Is Essential
AI performance may change after deployment because of:
distribution shift;
changes in patient populations;
different documentation practices;
integration issues;
data quality differences;
changes in clinical workflows.
The report highlights that an AI system validated successfully in one institution may behave differently when deployed in another environment.
This reinforces the importance of continuous monitoring and lifecycle evaluation.
Security Risks Can Affect Clinical Safety
Cybersecurity is also treated as a patient-safety issue.
AI systems may be affected by:
malicious access;
compromised source data;
third-party software vulnerabilities;
manipulated records;
model poisoning;
adversarial inputs.
Unlike some conventional failures, a compromised AI system may continue operating while generating incorrect outputs.
This can make problems difficult to identify and can affect both information confidentiality and continuity of care.
Accountability Is Distributed
Responsibility for AI-enabled healthcare systems may be shared among:
developers;
technology vendors;
healthcare organisations;
clinicians;
regulators;
other service providers.
This creates the potential for accountability gaps when responsibilities for design, validation, deployment, monitoring, modification and clinical use are not clearly defined.
The report stresses that AI-specific responsibility frameworks do not replace existing professional and institutional responsibilities.
Regulatory Classification Is Only Part of the Picture
Some AI systems may qualify as medical devices or regulated health software.
Others may fall outside product-specific medical device regulation.
However, ISO/TC 215 notes that systems outside a product-specific regulatory framework may still be subject to:
professional accountability;
institutional clinical governance;
procurement controls;
data protection;
quality and safety processes.
This means regulatory classification alone does not determine the full governance framework applicable to healthcare AI.
Privacy and Data Governance
The report also highlights the complexity of health data governance.
Healthcare data can be used across:
clinical care;
research;
quality improvement;
public health surveillance;
long-term follow-up;
AI model development.
The legal or ethical basis that permits one use may not automatically authorise another.
The report also warns that even de-identified health data may sometimes be re-identified through linkage or inference.
Data Governance Involves Trade-Offs
ISO/TC 215 highlights a significant tension between privacy protection and representativeness.
Restricting data use too heavily can reduce the availability of diverse datasets needed to evaluate AI systems across different populations.
At the same time, insufficient safeguards can create privacy and trust risks.
The report therefore argues that healthcare AI governance must consider both the risks of inappropriate use and the risks of unnecessarily restricting appropriate data use.
Trust as a Foundation of Healthcare AI
Trust is a recurring theme throughout the white paper.
Patients disclose sensitive information, accept recommendations and consent to treatment because they expect healthcare to operate competently, accountably and in their interests.
ISO/TC 215 states that fairness, transparency, safety, accountability, privacy and human oversight help protect that trust.
The report also stresses that trade-offs between these principles cannot always be resolved through technical solutions alone.
AI Risk Runs in Both Directions
The white paper makes an important observation: risk can arise both from using AI and from not using it.
Potential harm can result from:
deploying an unsafe AI system;
delaying or withholding a beneficial system;
inappropriate data sharing;
excessive restriction of legitimate data use.
The report therefore warns against treating restriction or delay as automatically neutral or risk-free.
Healthcare Is Moving Beyond Traditional Clinical Settings
The report also considers changes in how healthcare is delivered.
AI increasingly operates in:
remote consultations;
home monitoring;
asynchronous care;
consumer health technologies;
patient-facing digital services;
integrated smart healthcare environments.
These environments may lack some of the safeguards naturally present in hospitals, such as direct professional observation and immediate intervention.
Growing Use of General-Purpose AI
ISO/TC 215 specifically notes that general-purpose AI is increasingly being used by patients and consumers for health-related purposes without institutional or professional mediation.
This challenges traditional governance models that assume a licensed healthcare professional is present to interpret outputs and exercise judgment.
Implications for AI Standardization
The white paper argues that healthcare-specific and sector-neutral AI standards need to evolve together.
Relevant standards already include areas such as:
AI management systems;
AI risk management;
medical device risk management;
health software;
interoperability;
health informatics;
cybersecurity.
The report warns that AI increasingly crosses boundaries between these previously separate areas.
Coordination Across ISO and IEC
The document is intended to support coordination among standards bodies including:
ISO/TC 215 — Health informatics;
ISO/IEC JTC 1/SC 42 — Artificial intelligence;
ISO/TC 210 — quality management for health-related products and medical devices;
IEC TC 62 — medical equipment, software and systems;
ISO/TC 304 — healthcare organisation management.
The report argues that neither horizontal AI standards nor healthcare-specific standards can be developed effectively in isolation.
Foundational Questions for Healthcare AI
Annex A provides a practical framework for evaluating healthcare AI.
Key questions include:
What clinical or health consequences could be affected?
How does the AI interact with clinical workflows and medical devices?
What healthcare data does the system use or generate?
Which professional and institutional responsibilities apply?
Which regulatory or formal oversight mechanisms apply?
What equity, patient rights or population-level considerations are relevant?
The framework also asks organisations to consider the AI system’s:
intended purpose;
lifecycle;
adaptivity;
autonomy;
integration;
scale of impact;
error detectability;
error reversibility.
Impact on Healthcare AI Developers and Medical Device Manufacturers
For developers of AI-enabled medical devices, clinical software and digital health technologies, the white paper reinforces several important expectations.
Organisations should consider AI not only as a technical model but as part of a broader clinical and sociotechnical system.
This means regulatory and quality strategies should increasingly account for:
clinical validity;
intended use and deployment context;
human oversight;
patient participation;
subgroup performance;
transparency and explainability;
lifecycle monitoring;
change management;
cybersecurity;
data provenance;
interoperability;
accountability across organisations.
For regulated AI medical devices, these considerations are particularly relevant to risk management, clinical evaluation, post-market surveillance and lifecycle change control.
Impact on Healthcare Organisations
Healthcare institutions deploying AI also have responsibilities that extend beyond procurement.
The report indicates that organisations need sufficient:
governance capability;
clinical expertise;
technical infrastructure;
workforce resources;
monitoring processes;
incident-response mechanisms;
change-management systems.
Without this capacity, even technically strong AI systems may not be deployed safely.
A Lifecycle Approach to Healthcare AI
One of the strongest messages in the white paper is that AI safety and governance cannot stop at initial validation.
Performance, risk and accountability can change as:
models are updated;
populations change;
data sources evolve;
workflows are redesigned;
systems become more integrated.
Healthcare AI therefore requires a continuous lifecycle approach combining technical evaluation with clinical, organisational and regulatory oversight.
