Oct 5, 2025
6
min read
Medically Reviewed
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Understanding Software as a Medical Device (SaMD)
To understand the TGA’s stance, one must first understand the classification of Software as a Medical Device (SaMD). Under Australian regulations, software is considered a medical device if it is intended to be used for the diagnosis, prevention, monitoring, treatment, or alleviation of a disease. This definition is critical. An AI system that autonomously analyses an X-ray and declares "this is a fracture" is performing a diagnostic function and is strictly regulated as a high-risk medical device. It requires rigorous testing, clinical evidence, and inclusion in the Australian Register of Therapeutic Goods (ARTG).
However, the landscape is different for AI tools designed for documentation and workflow automation. If a software tool is intended merely to record, store, or display data—or to transcribe a conversation for the doctor to review—it typically does not fall under the strict definition of SaMD, or it falls into a very low-risk class. The TGA is less concerned with a tool that types for you and more concerned with a tool that thinks for you. The danger arises when clinics use standalone AI tools that blur this line. Some "scribes" on the market have begun to offer diagnostic suggestions without the appropriate regulatory clearance. This creates a compliance minefield. A unified platform like MediQo avoids this ambiguity by positioning its Clinical Assistant as a workflow aid, not a diagnostic device. It supports the clinician by structuring information and offering augmented analysis aligned with general guidelines, but it explicitly leaves the diagnostic conclusion to the doctor.
The "Human in the Loop" Principle
The cornerstone of the TGA’s guidance on the use of software in healthcare is the principle of the "Human in the Loop." The regulator acknowledges that software can fail, hallucinate, or misinterpret context. Therefore, it is a fundamental requirement that a qualified medical professional reviews and validates the output of any AI system before it becomes part of the patient’s permanent record. Automation should never equal abdication of responsibility.
This principle highlights a significant flaw in the use of fragmented, standalone AI apps. If a GP uses an app that runs in the background and automatically emails a summary to the reception desk or the patient without a mandatory review step, the "Human in the Loop" has been broken. This is a clinical governance failure. In contrast, a unified platform is architected to enforce this principle. MediQo’s Clinical Assistant generates a draft note in real-time within the clinical interface, but that note remains a draft until the clinician reviews and finalises it. The workflow is designed to compel validation. By integrating the drafting process directly into the consultation screen, the platform makes it easy for the doctor to verify the AI’s output against their own clinical judgement, satisfying the safety expectations of the regulator.
Expert Tips
"The TGA isn't trying to stop innovation; they are trying to stop negligence. The regulations essentially boil down to one question: 'Who is flying the plane?' If you are using a 'black box' AI that makes decisions for you, you are in dangerous territory. But if you are using a platform that organises the data, drafts the paperwork, and then hands it to you for final sign-off, you are enhancing safety. We built MediQo to be the co-pilot, never the pilot. The doctor is always the Human in the Loop, and that is what keeps your practice compliant." — Arash Zohuri, CEO, MediQo
The Risk of Hallucination and the Need for Context
One of the TGA’s primary concerns regarding Generative AI is the phenomenon of "hallucination"—where an AI model confidently invents facts that are not true. In a medical context, a hallucination could be disastrous; for example, an AI might record that a patient is taking a medication they never mentioned, simply because that medication is commonly associated with their condition. To mitigate this risk, AI needs context. A standalone scribe that listens to a consultation in a vacuum is statistically more likely to hallucinate because it lacks the grounding data of the patient’s history.
A unified platform mitigates this risk through deep contextual integration. MediQo’s "platform advantage" is that the AI does not listen in isolation. It is informed by the data captured by CALLA, the AI telephony module, which has already processed the patient’s intake and reason for visiting. It has access to the History-at-a-Glance timeline, which shows previous medications and conditions. When the Clinical Assistant generates a note, it cross-references the ambient audio against this known structured data. If the audio is ambiguous, the system relies on the established context rather than guessing. This layering of data significantly reduces the error rate and aligns with the TGA’s expectation that software should be reliable and accurate.
Key Takeaways
Recognise TGA regulations for high-risk medical software.
Ensure AI solutions have clinical safety evidence.
Maintain human oversight in documentation.
Keep systems updated to remain compliant.
The rapid integration of Artificial Intelligence (AI) into the Australian healthcare sector has moved faster than many anticipated. From large hospital networks to boutique medical centres, algorithms are now assisting with everything from image analysis to patient triage. However, for the General Practitioner (GP) on the front line, the most tangible impact of this revolution is the rise of AI-powered clinical documentation tools. These "AI scribes" promise to alleviate the crushing administrative burden that contributes to clinician burnout. Yet, as with any medical innovation, the excitement is tempered by the need for regulation and safety. The Therapeutic Goods Administration (TGA), Australia’s regulatory authority for therapeutic goods, plays a pivotal role in defining how software interacts with patient care.
For clinic owners and Practice Managers, navigating the TGA’s position on AI can be confusing. There is often uncertainty regarding where a digital tool ends and a "medical device" begins. Does an AI that summarises a consultation require TGA approval? What are the risks of using unregulated software? The TGA’s stance is evolving, but it remains rooted in a core principle: patient safety is paramount, and the human clinician must remain the ultimate decision-maker. This article explores the regulatory landscape of AI in clinical documentation and argues that the safest path to compliance lies not in disjointed, "black box" apps, but in a unified clinical automation platform that keeps the human firmly in the loop.
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