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    TGA
    Australia · Therapeutic Goods Administration

    Risk-based software rules, with AI evidence rules layered on.

    Australia's TGA reformed its software-as-a-medical-device rules in 2021 and has since been bolting AI-specific evidence expectations onto that framework rather than writing a stand-alone AI regulation. The 2024 evidence guidance for AI-enabled software, plus the Feb 2026 refresh of the AI and medical-device software page, are now the operational reference points for any AI SaMD entering the ARTG.

    Posture · Developing · AI-specific evidence guidance live since 2024Last reviewed · May 2026
    Key facts
    Reformed software rules in force
    Feb 2021
    Evidence guidance for AI software
    2024 · updated 2026
    AI regulation page last updated
    Feb 2026
    Change posture
    Significant change → new inclusion
    §01

    How TGA regulates AI-enabled software

    AI-enabled software that meets the definition of a medical device is regulated under the Therapeutic Goods (Medical Devices) Regulations 2002, as amended in 2021. Devices are classified using risk-based rules (Class I → Class III / AIMD) with software-specific classification rules (Rules 4.1–4.4 / Schedule 2) introduced in the reform. AI does not change classification logic · it raises the evidence bar, particularly around data, validation, transparency and post-market monitoring.

    • Most diagnostic AI lands Class IIa or IIb under the software classification rules.
    • Sponsors must hold a conformity assessment (TGA or comparable overseas regulator) before ARTG inclusion.
    • Evidence package: intended purpose, dataset, validation, human factors, post-market plan.
    • Australian Unique Device Identifier (AusUDI) work is rolling out and applies to software devices.
    §02

    Evidence requirements for AI software

    The TGA evidence guidance for AI software sets out what a sponsor must demonstrate over and above the standard software evidence: clarity of intended purpose, representativeness and quality of training and test data, transparency to users about model behaviour and limitations, and a post-market plan that detects drift and adverse events. It is explicitly aligned with the IMDRF GMLP guiding principles.

    • Document data provenance, inclusion/exclusion criteria, and subgroup performance.
    • Demonstrate validation on data that is representative of the Australian deployment context.
    • Provide transparency information · what the model does, what it does not do, its known failure modes.
    • Operate a post-market monitoring plan with thresholds, escalation, and reporting.
    §03

    Change control: significant change triggers re-inclusion

    TGA does not yet have a formal PCCP-equivalent. The default rule is that a significant change to an AI medical device · including retraining that materially alters performance or intended use · requires a new ARTG inclusion or, at minimum, a change notification to TGA. Sponsors are expected to define, in their QMS, what constitutes a significant change for their specific device. AI-specific change-control guidance is on the agency's known workplan.

    §04

    Practical posture

    Australia is a high-leverage market: the rules are pragmatic, the comparable-overseas-regulator pathway accelerates entry for devices already cleared by FDA / EU / HC, and the evidence expectations are well-telegraphed. The risk is treating it as a copy-paste market. Validation evidence, intended purpose statements, and post-market plans need to be reworked for the Australian context, and change control needs to be planned defensively until formal PCCP guidance lands.

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