Children aren’t small adults. This medical truism applies to AI just as it does to drug dosing and disease presentation. AI systems designed for adult populations often don’t work well—or safely—for children.

Paediatric AI requires specific attention. Here’s what healthcare organisations and clinicians working with children should understand.

Why Paediatric AI Is Different

Several factors make paediatric AI distinct:

Physiological Variation

Children’s physiology changes dramatically with age:

• Vital sign normal ranges vary by age
• Drug metabolism differs from adults
• Disease presentations vary with development
• Growth affects all measurements

AI trained on adult data may not understand these variations. An AI might flag a normal infant heart rate as tachycardia, or miss concerning values in adolescents.

Developmental Considerations

Children’s cognitive and emotional development affects AI interaction:

• Communication capability varies with age
• Consent involves parents/guardians
• Fear and anxiety affect clinical encounters
• Developmental context matters for diagnosis

AI that interacts directly with patients (symptom checkers, chatbots) needs to work appropriately for different developmental stages.

Smaller Populations

Paediatric populations are smaller than adult populations:

• Less training data available for AI development
• Rare conditions are even rarer
• Statistical power for validation studies is limited
• Market size for paediatric AI products is smaller

These factors mean less AI development for paediatrics and greater validation challenges.

Different Disease Spectrum

Children’s diseases differ from adults:

• Congenital conditions and genetic syndromes
• Infectious diseases with different patterns
• Malignancies with different types
• Injuries with different mechanisms

AI trained on adult disease patterns may miss paediatric-specific conditions.

Family-Centred Care

Paediatric care is inherently family-centred:

• Parents/guardians are central to care decisions
• Family dynamics affect health and healthcare
• Communication must include family members
• AI must work for families, not just patients or clinicians

Applications in Paediatric Settings

Paediatric AI applications include:

Age-Adjusted Early Warning

Paediatric early warning scores (PEWS) use age-adjusted vital sign thresholds. AI versions:

• Use age-appropriate reference ranges
• Model paediatric deterioration patterns
• Integrate paediatric-specific factors
• Account for different disease trajectories

Adult deterioration AI doesn’t translate directly to children.

Paediatric Imaging

Imaging AI for children needs to account for:

• Size differences in anatomical structures
• Normal developmental variants
• Paediatric-specific pathology (bone age, growth abnormalities)
• Lower radiation dose images with different characteristics

Some adult imaging AI has paediatric validation; much doesn’t.

Neonatal Applications

Neonatal intensive care has specific AI applications:

• Retinopathy of prematurity screening
• Brain injury prediction
• Sepsis detection in premature infants
• Feeding and growth optimisation

Neonatal physiology is distinct enough that adult or even older paediatric AI doesn’t apply.

Developmental Assessment

AI for developmental screening and assessment:

• Motor milestone tracking
• Language development assessment
• Autism screening support
• Learning difficulty identification

These are paediatric-specific applications without adult equivalents.

Paediatric Mental Health

Children’s mental health AI:

• Anxiety and depression screening adapted for children
• Developmental disorders assessment
• Behavioural analysis
• School-related mental health support

Adult mental health AI doesn’t translate to children whose presentations and treatments differ.

Implementation Challenges

Paediatric AI implementation faces particular challenges:

Validation Requirements

Before deploying AI in paediatric settings:

• Is the AI validated in paediatric populations?
• Were children of relevant ages included?
• Were relevant paediatric conditions represented?
• Is the validation sample size adequate?

Adult validation doesn’t substitute for paediatric validation.

Consent Complexity

AI use in paediatric care involves:

• Parental/guardian consent for younger children
• Assent from older children where appropriate
• Communication with families about AI involvement
• Consideration of mature minor provisions

Consent frameworks need adaptation for paediatric contexts.

Clinician Expertise

Paediatric clinicians should be involved in:

• Evaluating AI for paediatric use
• Overseeing paediatric AI implementation
• Monitoring AI performance in children
• Making governance decisions about paediatric AI

General AI governance should include paediatric expertise. AI consultants Melbourne working with children’s hospitals emphasise this point—paediatric representation on AI governance committees is essential.

Family Experience

AI should enhance, not complicate, family experience:

• Parent-facing AI should be appropriate and reassuring
• AI should support family communication, not replace it
• Cultural and linguistic diversity in families matters
• Anxiety and fear should be considered in AI design

Australian Context

Paediatric healthcare in Australia has specific considerations:

Specialist children’s hospitals (Royal Children’s, Sydney Children’s, Queensland Children’s, etc.) have concentration of paediatric expertise and may be better positioned for paediatric AI implementation.

General hospitals with paediatric services face challenges having paediatric-specific AI when volumes are lower and general AI systems are deployed.

Primary care sees most paediatric patients but has least AI development for paediatric populations.

Aboriginal and Torres Strait Islander children have different disease patterns and need culturally appropriate AI applications.

Regional paediatrics faces access challenges that AI might help address through telehealth and decision support, but also infrastructure limitations.

Recommendations

For organisations using AI in paediatric care:

Verify Paediatric Validation

Before deploying any AI:

• Confirm paediatric validation exists
• Assess whether validation population matches your patients
• Understand age ranges and conditions covered
• Know the limitations for paediatric use

Don’t assume adult-validated AI works for children.

Involve Paediatric Expertise

Include paediatric clinicians in:

• AI evaluation and selection
• Implementation planning
• Governance oversight
• Ongoing monitoring

Paediatric perspective should be represented, not assumed.

Communicate With Families

Ensure families understand:

• When AI is used in their child’s care
• What AI does and doesn’t do
• How AI-informed decisions are made
• Their right to ask questions

Family communication is part of paediatric AI implementation.

Monitor Paediatric Outcomes

Track AI performance specifically in paediatric populations:

• Are outcomes as expected?
• Are there concerning patterns by age group?
• Do families have concerns?
• Is paediatric care improving?

Aggregate performance may mask paediatric-specific issues.

Partner Appropriately

For organisations developing paediatric AI, partnering with specialists helps. AI consultants Sydney and health informatics advisors can assist with implementation, though paediatric clinical expertise must guide decisions.

Looking Forward

Paediatric AI will develop, but likely slower than adult applications:

• Smaller markets attract less commercial investment
• Smaller populations mean less training data
• Higher validation standards create barriers
• Risk aversion in paediatrics slows adoption

Advocacy for paediatric AI development matters. Children deserve AI that works for them, not adapted adult solutions.

For now, the key message is caution: verify paediatric applicability, involve paediatric experts, and don’t assume AI works for children just because it works for adults.

Children deserve AI designed with them in mind—not as an afterthought.

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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.