AUTHOR

Ross Dixon
National Manufacturing Leader
Business Advisory
Albury

As the healthcare sector grapples with ongoing shortages of nurses, general practitioners and medical specialists, many are looking more closely at the possible role artificial intelligence (AI) could play in helping to close those gaps. 

In a recent poll by the Royal Australian College of General Practitioners, GP use of AI scribes has nearly doubled in one year alone. According to a 2026 study by Elsevier, 41% of nurses are also using AI for work – most commonly for professional education and upskilling, patient education and medical research. 

In medical specialties, we see AI increasingly being used to assist with image analysis, diagnostics and workflow management.  

But the big question is: does it really work?  

We asked John Wilson, Managing Director of Cadent Cardiology, to share his insights on how AI use is unfolding in cardiology. 

AI supporting diagnosis through data 

“Our practice specialises in preventative cardiology, so our goal is to reduce the average age that patients come to see us by 10 years,” says John. “If we can identify risk and support lifestyle changes earlier, we can improve patient outcomes.

 “By having an expanded demographic though, it means we need the capacity to see more patients. This capacity can only be gained by operating as efficiently and effectively as possible.”

John says this is where AI can add real value, especially when it comes to reviewing and interpreting large volumes of patient data.

“AI in cardiology is very much in the development phase, especially in terms of how well it can be relied upon for diagnosis. What we do see is increasing use of AI as an assistant and a second set of eyes. It is being used in cardiac diagnostic testing such as 24-hour Holter monitors where AI will read the rhythm and look for arrythmia or other irregularities. Although cardiologists review all the data themselves, an AI that can do it constantly and in real time is able to alert a cardiologist instantly who can then take immediate action.  

“We had a case recently where a patient was fitted with an Implantable Cardiac Monitor (ICM). Our cardiologist received an immediate alert from the patient’s device even though they were overseas, and was able to contact them and urge them to visit a hospital. That timely intervention did save their life that day.” 

Image removed.

AI helping cardiologists streamline admin work 

Another area where John says AI is proving very helpful is in streamlining some of the highly administrative aspects of the cardiologist’s role.  

For example, Cadent Cardiology has implemented a cardiology-specific AI scribe that he says has saved their cardiologists hours in notetaking, GP letters and other clinical documentation each day.  

Joel Freiberg, Founder of Medow Health – which developed the AI scribe Cadent Cardiology uses – says, “Across specialist healthcare, clinical documentation is becoming one of the clearest entry points for AI because it solves an immediate problem: highly trained clinicians spending too much time on repetitive administration.

“At Medow, we’ve learned that adoption takes much more than putting a general AI model in front of a specialist. The bar for quality, accuracy and reliability is high, which is why we build and tune our AI for individual specialties, their terminology, documentation patterns and workflows.

“But great technology alone doesn’t change behaviour. Deep integrations, hands-on onboarding and ongoing support help build clinician trust and drive the behaviour change required to embed AI into daily practice. That combination is what turns AI from a useful tool into infrastructure clinicians can rely on every day." 

Image removed.

What will the future of AI in cardiology look like? 

There are still many areas that John believes have a long way to go before AI can offer deeper value in the cardiology space.  

“We could be utilising AI more effectively to help with patient health predictions. However, the success of these tools relies on user confidence and adoption. As more people use smart watches and other wearable devices to track health metrics, AI can analyse that information alongside clinical data to identify patterns and support more proactive care.”

As Cadent Cardiology’s founder and lead cardiologist said recently when interviewed about what success looks like in the future: 

“To avoid anxiety, people will be educated on how to detect heart disease in themselves – including how to take all measures to prevent disease including diet, exercise and medication. There will be scans that can easily detect heart disease and AI based advice which can be individualised for each patient.” 
A/Prof Naylin Bissessor MD PhD

AI governance a high priority for the health sector 

John feels there does need to remain a certain level of scepticism about AI outputs, and adopting practices should be conscious of taking their patients along for the journey.  

“There are plenty of examples that prove AI is not foolproof, so specialist oversight will remain critical – especially in healthcare where patient lives are at stake.  

“As a field, we also need to be wary of adopting AI without including patients in that journey. You can’t just implement an AI receptionist without gauging a patient’s appetite for liaising with a robot instead of a human – and the AI needs to be advanced enough to be accurate and effective, specifically for your clinic and patients. These are steps it seems we are all taking together in a much longer and broader AI journey, and we need to be respectful that not everyone will take that journey at the same pace.”

Additionally, we need to acknowledge the very real risks that can come from implementing new AI tools in the healthcare sector where highly sensitive data is in play. While platforms such as Medow take extensive measures to protect patient data, the same cannot be said for all AI tools. Research published by Netskope found 71% of healthcare workers still use personal AI accounts for work purposes, and 81% of healthcare data policy violations involved regulated healthcare data – with staff uploading information to tools such as ChatGPT, Gemini and personal cloud storage accounts.

To help you determine a clear path forward with AI adoption and minimise the risks, we’ve put together a free AI adoption checklist for the healthcare sector (see below). 

Learn more about AI adoption, including how industries are using AI and steps to implement AI in your business, in our latest thinkBIG: Cutting through the AI hype. 

 AI in healthcare: adoption checklist 

  • Identify the clinical or administrative challenge you want to address
  • Determine whether AI is the right solution
  • Define what success looks like for patients, clinicians and the practice 
  • Assess the vendor's healthcare experience and track record
  • Understand how the tool works and where its recommendations come from
  • Identify what patient information the tool will access, store or share
  • Review whether the vendor uses customer data to train AI models 
  • Consider the privacy and cyber security risks
  • Consider the potential impact of errors, inaccuracies or bias
  • Identify where clinician review and oversight will be required
  • Confirm who remains accountable for decisions and outcomes 
  • Address unauthorised AI use and set clear rules  
  • Develop guidelines for staff use of new authorised tools
  • Train clinicians and staff on the tool's capabilities and limitations
  • Establish a process for reporting errors or unexpected outcomes
  • Communicate with patients where appropriate 
  • Start with a pilot or low-risk use case
  • Test the tool within existing workflows
  • Measure its impact on efficiency, patient experience and care outcomes
  • Review performance regularly and make improvements as needed 
  • Privacy and security checks completed
  • Policies developed and shared
  • Clinical oversight established
  • Staff training completed
  • Pilot testing completed
  • Monitoring and review processes in place 


 

Image removed.

 

 Ready to put AI to work in your business?

Whether you're exploring AI for the first time or looking to build on existing capabilities, RSM can help you identify opportunities, manage risks and develop a practical approach to AI adoption.

Please provide your company name.

Ideas and insights

Insights for the Medical sector