Informed consent is fundamental to ethical clinical practice. Patients should understand proposed treatments, their risks and benefits, and alternatives before agreeing to proceed.

In practice, consent processes often fall short. Time pressures, complex information, variable communication skills, and patient factors all affect consent quality. AI is being explored as a tool to improve consent—but the applications vary significantly across specialties.

The Informed Consent Challenge

Good informed consent requires:

• Information about proposed treatment/procedure
• Explanation of risks and potential complications
• Discussion of alternatives including non-treatment
• Assessment of patient understanding
• Documentation of the consent conversation

Challenges include:

• Time pressure limiting discussion depth
• Complex medical information that’s hard to communicate
• Patient anxiety or cognitive factors affecting comprehension
• Variability in clinician communication skills
• Documentation that may not reflect actual conversation content

Research consistently shows that patient recall and understanding after consent discussions is often poor—even when clinicians believe they’ve communicated effectively.

AI Applications in Consent

Several AI applications aim to improve consent:

Personalised Risk Communication

AI that calculates individualised risk estimates based on patient characteristics:

• Surgical complication risks adjusted for patient factors
• Treatment outcome probabilities personalised to the individual
• Comparative effectiveness data for treatment alternatives

Rather than generic population-level statistics, patients receive risk information relevant to them.

Interactive Consent Tools

AI-powered tools that:

• Present consent information in adaptive ways based on patient responses
• Check understanding through interactive questioning
• Adjust complexity and detail to patient needs
• Provide information in preferred languages and formats

These go beyond static consent forms to dynamic, responsive consent experiences.

Natural Language Explanation

AI that translates medical terminology into plain language:

• Procedure descriptions in accessible terms
• Risk explanations without jargon
• Answers to common patient questions

Large language models are increasingly capable of this translation work.

Consent Documentation

AI that:

• Generates consent documentation from clinical conversations
• Ensures all required elements are covered
• Creates records of what was discussed
• Highlights areas where additional discussion may be needed

Documentation that reflects actual consent conversations rather than generic forms.

Shared Decision-Making Support

AI tools that facilitate shared decision-making by:

• Presenting balanced information about options
• Eliciting patient values and preferences
• Matching treatment options to individual priorities
• Supporting structured decision processes

Moving beyond consent-as-signature to consent-as-genuine-decision.

Specialty Applications

Different specialties face different consent challenges. AI applications reflect this variation.

Surgery

Surgical consent involves specific procedure risks, often complex and numerous. AI applications include:

• Procedure-specific risk calculators incorporating patient factors
• Visual representations of surgical procedures
• Complication probability estimates
• Recovery timeline predictions

Surgical specialties have been early adopters of personalised risk calculation, particularly for elective procedures where time permits detailed discussion.

Oncology

Cancer treatment consent involves:

• Multiple treatment options (often combinable)
• Complex risk-benefit trade-offs
• Uncertain outcomes
• Significant impact on quality of life

AI tools in oncology focus on comparative effectiveness, treatment response prediction, and shared decision-making for complex choices. The stakes and complexity justify substantial consent investment.

Mental Health

Mental health consent has unique considerations:

• Capacity may be affected by the condition being treated
• Treatment effects may take time to manifest
• Side effects may be significant and persistent
• Patient preferences and values are particularly important

AI applications focus on communication adaptation, comprehension checking, and documentation of capacity assessments.

Paediatrics

Paediatric consent involves:

• Parents/guardians consenting on behalf of children
• Developmental considerations in communicating with children
• Assent processes for older children

AI tools adapt communication for different family members and developmental stages, and support documentation of who was involved in consent discussions.

Emergency Care

Emergency consent faces time pressure:

• Urgent decisions with limited discussion time
• Patients who may have impaired capacity
• Situations where full consent isn’t possible

AI applications focus on streamlined communication, essential information prioritisation, and documentation of consent limitations.

What’s Actually Working

In Australian practice, I observe:

Surgical risk calculators are increasingly used, particularly in cardiac, orthopaedic, and colorectal surgery. Several validated tools exist with good evidence base.

Interactive consent platforms are emerging in elective surgery and cancer care. Patients can engage with information before clinic visits, improving discussion efficiency.

Decision aids (some AI-enabled) are used in preference-sensitive decisions where patient values are central. Prostate cancer screening, breast cancer treatment options, joint replacement timing.

Consent documentation AI is less developed. Most consent documentation remains form-based rather than AI-generated.

Implementation Considerations

For organisations considering consent AI:

Clinical Governance Oversight

Consent AI affects the doctor-patient relationship. Clinical governance should:

• Review AI tools before deployment
• Monitor consent quality outcomes
• Address complaints or concerns about AI-assisted consent
• Ensure AI doesn’t replace necessary human conversation

Liability Clarity

If AI-provided risk information is inaccurate or incomplete, who is responsible?

• The clinician who used the AI?
• The organisation that deployed it?
• The vendor who built it?

Legal and risk management perspectives should inform implementation.

Equity Considerations

AI consent tools should be accessible to:

• Patients with limited digital literacy
• Patients with disabilities
• Patients who speak languages other than English
• Patients in regional and remote areas

Tools that work only for well-educated, English-speaking, digitally-capable patients worsen rather than improve consent equity.

Evidence Requirements

Before deployment, understand:

• What evidence supports this AI tool?
• Has it been validated in relevant populations?
• Does it actually improve patient understanding?
• What are the known limitations?

Marketing claims aren’t evidence. Demand rigorous validation.

The Human Element

AI should support, not replace, the human element of consent:

• Clinician judgment about what matters for each patient
• The therapeutic relationship that builds trust
• Responding to patient questions and concerns
• Assessing genuine understanding, not just form completion

The best consent AI augments human communication; it doesn’t substitute for it. Team400, working with several Australian health services on consent AI implementations, reports that projects succeed when clinicians remain central to the consent conversation.

For organisations developing consent AI strategies, working with partners who understand both clinical and technical aspects helps. AI consultants Sydney can advise on technology options, though clinical and ethical aspects require clinical leadership.

Regulatory Considerations

Consent AI may have regulatory implications:

• TGA considerations if consent tools make claims about clinical outcomes
• Privacy requirements for patient information processed by AI
• Professional standards about consent documentation
• State/territory-specific consent requirements

Regulatory navigation should be part of implementation planning.

Looking Forward

I expect consent AI to develop significantly:

Near term: Surgical risk calculators become standard practice. Interactive consent platforms grow in elective care settings.

Medium term: AI-assisted documentation of consent discussions becomes more common. Shared decision-making AI matures.

Longer term: Consent becomes a continuous, AI-supported process rather than a point-in-time event. AI helps track patient understanding and preferences over time.

The goal remains unchanged: ensuring patients genuinely understand and agree to their care. AI is a tool toward that goal, not a replacement for the fundamental ethical obligation.

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Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.