AI in Telehealth in Australia

Artificial intelligence (AI) is showing up across healthcare, including telehealth. You might see AI used to summarise clinical notes, help route patients to the right type of appointment, surface safety prompts, translate information into plain language, or assist with administrative workflows like scheduling and document delivery. Some telehealth services also use chatbots or symptom checkers as a first step before a clinician reviews the case.

But healthcare is not a normal “tech product” environment. In Australia, patient safety, professional accountability, privacy, consent, and clinical governance still apply whether a consultation happens in person or online. AI can support care, but it should not replace clinical judgement, and it should never be used to mislead patients into thinking they are consulting a registered clinician when they are not.

This article explains how AI can be used in telehealth, what Australian rules and regulatory expectations commonly apply, what “good” AI use looks like in practice, and what patients should look for when choosing an online healthcare service. This content is general information only and not medical advice.

What “AI in telehealth” actually means

AI is a broad term. In telehealth, AI commonly appears in a few practical forms, ranging from low-risk admin tools to higher-risk clinical tools.

• AI scribes and documentation tools that draft consultation notes, letters, or summaries for clinician review and editing.
• Triage and routing tools that ask structured questions and help direct patients to the right appointment type or urgency level.
• Clinical decision support that provides prompts or suggestions (for example, reminding about allergy checks or red flags) while the clinician remains responsible for decisions.
• Patient-facing chatbots that provide general health information, appointment preparation guidance, or help navigate the platform.
• Risk detection and quality monitoring such as identifying unusual prescribing patterns, repeated certificate requests, or follow-up gaps.
• Imaging or diagnostic AI in some settings (usually higher risk and more heavily regulated when used for diagnosis or treatment decisions).

Not all AI uses have the same risk. A transcription assistant that a clinician edits is very different from an AI tool that claims to diagnose or recommend treatment without clinician oversight.

The safest principle: AI supports care, it does not replace clinicians

The safest model for AI in telehealth is “human-in-the-loop”: AI can assist with speed, structure, or recall, but a qualified clinician remains responsible for the assessment, the decision-making, and the communication of advice.

This is consistent with broader telehealth safety thinking: telehealth is a channel of care, not a shortcut around clinical standards. If you want background on this concept, read How Technology Supports — Not Replaces — Clinical Care.

Where AI can add real value in telehealth

Used responsibly, AI can improve patient experience and clinician capacity without compromising safety.

• Better documentation quality by helping clinicians capture structured notes, which can support continuity and follow-up.
• More time for patient conversation by reducing time spent typing, searching, or repeating admin steps.
• Safety prompts that remind clinicians about red flags, medication risks, or recommended follow-up steps (without overriding judgement).
• More consistent patient education such as plain-language explanations and after-care instructions that clinicians can tailor.
• More efficient workflows such as secure delivery of documents, reminders, and navigation support in patient portals.
• Equity improvements when tools support accessibility, language clarity, and flexible care models (with safeguards to avoid bias).

These benefits only hold if the platform is governed well, clinicians remain accountable, and patients are clearly informed about how AI is used.

Key risks of AI in telehealth

AI can introduce new safety and trust risks if it is used carelessly or marketed in a misleading way.

• Hallucinations and errors where AI produces confident but incorrect information, especially in free-text generation.
• Over-reliance where clinicians or patients trust AI output without adequate verification.
• Bias and inequity if AI performs worse for certain groups (language, disability, culture, age) or reflects training-data bias.
• Privacy and confidentiality risks if sensitive health data is handled insecurely or used for training without appropriate controls and transparency.
• Identity and accountability confusion if patients are not told when they are interacting with AI rather than a registered practitioner.
• Clinical appropriateness failures if AI-driven pathways encourage “one-size-fits-all” outcomes such as automatic certificates or inappropriate prescribing.

Strong clinical governance is what turns “interesting AI” into “safe healthcare”. If you haven't already, read The Role of Clinical Governance in Telehealth.

Australian rules and expectations that commonly apply to AI in telehealth

Australia does not treat AI as a free-for-all. Multiple regulatory and standards-based expectations can apply depending on how the AI is used, what it claims to do, and what data it handles.

1) Practitioner responsibilities still apply in telehealth

In Australia, clinicians providing telehealth are expected to meet professional standards that apply to all care, including taking adequate history, obtaining informed consent, maintaining appropriate records, and recognising when telehealth is not suitable and escalation is required.

Where AI is used to support the consultation (for example, an AI scribe or structured intake tool), a sensible safety expectation is that the clinician remains responsible for what is recorded and what decisions are made, and the patient is not misled about who (or what) they are interacting with.

For telehealth suitability, see When Telehealth Is Clinically Appropriate and When Telehealth Is Not Appropriate.

2) Informed consent and transparency about AI use

Trust in healthcare depends on transparency. If AI is used in a way that affects the consultation experience (for example, recording and transcribing, summarising, or providing an AI-driven intake), patients should be told in clear language and given a chance to ask questions.

Good practice includes explaining what the AI does, what it does not do, how information is handled, and whether there is a non-AI option available (for example, turning off an AI scribe or using a phone consult without automated intake).

Consent is also closely tied to confidentiality. For more, read Consent and Confidentiality in Telehealth.

3) Privacy law: health information is sensitive information

In Australia, health information is treated as sensitive personal information. Telehealth platforms and health service providers should handle it with strong privacy protections, clear collection and disclosure practices, and security safeguards that match the sensitivity of the data.

In practice, privacy-first AI in telehealth means data minimisation (collect only what is needed), careful access control (only authorised people can access data), secure storage and transmission, and clear patient information about how data is used.

If your telehealth journey includes documentation, certificates, or referrals, privacy matters end-to-end. Read Medical Certificates and Patient Privacy and How Telehealth Platforms Protect Patient Privacy.

4) Data breaches: expect preparedness and clear response processes

Cybersecurity incidents can occur in any digital service, including healthcare. A trustworthy telehealth platform should have a breach response plan, clear internal procedures, and practical steps to reduce risk, such as secure authentication, monitoring, and vulnerability management.

From a patient perspective, trust signals include clear privacy information, secure account practices, and transparency about how the service responds if something goes wrong.

5) My Health Record context: extra obligations can apply if you participate

If a service participates in the My Health Record system (or provides services connected to it), additional system-specific requirements and reporting expectations may apply. This is one reason telehealth providers must be very clear about their data flows, integrations, and security responsibilities.

Even if your service does not integrate with My Health Record, it is still handling sensitive health data and should act with healthcare-grade security and privacy practices.

6) TGA regulation: some AI software may be a medical device

In Australia, the Therapeutic Goods Administration (TGA) regulates medical devices, and software can be a medical device depending on its intended purpose. Some AI tools used for diagnosis, screening, prediction, monitoring, or treatment recommendations may fall under medical device rules, which can involve risk classification and inclusion requirements.

Not all software is regulated as a medical device. Many low-risk admin tools or general-purpose tools may fall outside TGA medical device requirements, but that does not remove the need for privacy, security, and clinical governance.

If a platform uses AI for clinical decision support, the details matter: what it does, what claims are made, and whether it meets any exemption criteria. This is a core area where responsible platforms obtain specialist regulatory advice and keep clear documentation of intended purpose and safety controls.

What “good” looks like: practical AI governance in telehealth

Governance is how you keep AI safe and trustworthy after launch, not just during development. Strong AI governance in telehealth usually includes the following elements.

1) Clear intended purpose and limits

Define what the AI is for, what it is not for, and what decisions it should never make. For example, an AI scribe may draft notes but must not approve prescriptions or certificates, and must not be used to simulate a clinician conversation without patient awareness.

2) Human oversight and accountability

Clinicians should review and approve clinical content that influences care. If AI drafts notes, letters, or patient instructions, the clinician should edit and confirm accuracy before it becomes part of the record or is sent to the patient.

3) Safety testing, monitoring, and continuous improvement

AI performance can drift over time due to changes in population, presentation patterns, or system updates. Responsible platforms monitor outcomes, track errors, review incidents, and update controls. Monitoring should include quality audits, clinician feedback loops, and escalation processes when something looks wrong.

4) Privacy-first design for AI features

Privacy-first AI design includes secure handling of recordings and transcripts, restricted access, clear retention periods, and transparent patient communication about what is collected and why. Where possible, minimise exposure of identifiable health information and avoid unnecessary sharing with third parties.

5) Bias and equity safeguards

AI can unintentionally disadvantage groups if it performs worse for certain accents, languages, disabilities, or cultural contexts. Equity-focused telehealth evaluates AI outputs across diverse groups, uses plain language, and provides alternative pathways where the AI experience is not suitable.

For more, read Equity and Access in Digital Healthcare and Accessibility Benefits of Telehealth.

6) Patient-centred communication

AI should reduce friction, not reduce dignity. Patients should feel listened to, not “processed”. Patient-centred telehealth communicates clearly, confirms understanding, and provides tailored safety-net advice rather than generic scripts.

For patient-centred practice, read The Importance of Patient-Centred Care in Telehealth.

What patients should ask about AI in telehealth

If a service uses AI, you can protect yourself by asking practical questions that reveal how seriously the service takes safety and privacy.

• Am I interacting with a clinician, an AI tool, or both?
• Is AI used to record, transcribe, or summarise my consult, and can I opt out?
• How is my information stored, who can access it, and for how long?
• Is my data used to train AI models, and if so, how is that communicated and controlled?
• What happens if the clinician thinks telehealth is not suitable for my symptoms?
• Are prescriptions, referrals, or certificates guaranteed, or are they based on clinical assessment?

Trustworthy providers answer these questions clearly and do not hide behind vague “AI magic” language. If you want a broader trust framework, read Building Trust in Online Healthcare.

How Dociva approaches AI in telehealth

Dociva is designed around clinician-led care, where technology supports safe workflows rather than replacing clinical judgement. Any use of automation or AI should be governed through safety-first design, privacy-first practices, and clear patient communication, with clinicians remaining responsible for assessment and decisions. If you want updates during pre-launch, use pre-launch sign-up.

Frequently Asked Questions (FAQs)