AI reshaping the healthcare landscape

By Craig Wong-Pan
Published 0, 0000 | 2 min read

Artificial intelligence (AI) is ushering in a new age of healthcare, offering the medical and health sectors greater capacity and more precision in everything from diagnosing cancers to dispensing drugs.

AI reshaping the healthcare landscape

Artificial intelligence (AI) is ushering in a new age of healthcare, offering the medical and health sectors greater capacity and more precision in everything from diagnosing cancers to dispensing drugs.

In doing so, AI is not only saving lives but also delivering efficiencies that will translate into tangible monetary savings for hospitals, healthcare systems and governments, according to a recent panel at the RBC Capital Markets Australian Technology Conference.


Tackling one of the leading causes of death

Max Mito CEO of co-founded the company three years ago to prevent adverse drug events and therefore improving patient outcomes and medical workflow, “This problem costs the Australian health system between A$1.4 billion and A$1.7 billion a year,” he explained. “It is also the second-leading cause of death in the States after cancer.” offers two ‘generations’ of products, the first digitizes and unifies patient prescription records for community pharmacists, aged care centers and hospitals, and also provides support with e-prescriptions, drug administration and compliance management.

The second generation leverages this data, using a suite of AI tools to improve patient outcomes while also providing additional and supplementary services. For example, opt-in facial recognition software in hospitals and pharmacies can identify chronic patients, automatically read records and then administer the required medication.

“We deployed this in hospitals where some medical practitioners need to approve hundreds of prescriptions a day. This speeds up the process, improves security and reduces wait time,” Mito explained.


Increasing the world’s healthcare capacity’s mission is to ‘scale up global healthcare capacity using AI’. According to CEO Dr Aengus Tran, a qualified medical doctor and machine learning engineer, this is achieved by providing skilled specialists with greater precision in their diagnoses while simultaneously freeing them up for higher-value work.

“It takes about 16 years to qualify as a radiologist or pathologist” he explained “These are critical workers in many countries, especially when it comes to the diagnosis and prognostication of cancer and other conditions. We’re seeing that the number of radiologists has simply not grown in line with the demand for scans around the world.”

To overcome this challenge,’s products have the capacity to analyze a large volume of scans and automatically identify potential issues. Its first product can read chest scans and accurately identify 124 different conditions. “It’s a sort of spell checker for radiologists,” Dr Tran explained. “It looks through a lot of cases very quickly and offers a safety net that prevents radiologists from making critical mistakes.”

Dr Tran recalled how he was recently contacted by a radiologist with more than 20 years of experience who was using the product and found it had correctly identified four cases of cancer he had missed over a two week period.

The technology, which is already used by 30% of Australia’s radiologists, has the added effect of allowing medical specialists to essentially triage their patients by pre-reading and flagging potentially high-risk cases so that they move to the top of the list.


Expanding horizons

Despite these advances, Dr Tran says is still only in the early stages of where it ultimately wants to be and that there is opportunity for both vertical and horizontal growth. “We can expand with doctors currently using our chest x-rays by looking at other body parts such as breasts and ribcages,” he explained. “There is also an opportunity to expand through different verticals.”

Their first movement in this regard has been into pathology, where it is currently working to integrate AI into daily clinical practice within histopathology. Other verticals to later expand into include breast, skin and gastroenterological tract.

Mito’s is also expanding both internationally - into markets including the UK and the United States - and into new sectors. “Our customers are primarily aged care, hospitals and pharmacies. However, we’re also seeing correctional facilities come on board,” he explains.

Mito said that this growth will enable the company to begin to offer additional ‘second generation’ services such as patient referrals to clinical trials and pharmacy manufacturing programs. “This should help with the cost of medications, a growing issue in Australia and around the globe,” he said.


Data the key challenge has grown at the impressive rate of 800% this year, Mito acknowledged that its take-up had been partly fueled by the Royal Commission into Aged Care, which emphasized the need for greater digitization of patient records. However, he explained, there are still challenges that need to be overcome, especially around data.

“There is still a process to digitize data,” Mito told the audience. “When we first came into [primary care] we wanted to use a suite of AI tools, but we found it wasn’t quite ready given the prevalence of paper and SQL servers. We’ve had to focus on making sure there was a data-enabled infrastructure in place.”

Dr Tran echoed this point, saying’s technology relied on a “large, clean, diverse and anonymized data set”.

“Your dataset is a key asset,” he concluded. “Creating and labelling it is an expensive and lengthy process.”


Working with existing players

Looking ahead, Dr Tran stressed the importance of remaining ‘big picture’ and trying to offer holistic solutions that solved many problems rather than focusing on just one thing.

“If you look at most AI companies, they can only offer one solution; it’s like offering a spell checker, but only for the letter ‘A’.”

He believes there are three potential inflexion points for growth for AI in healthcare. The first was gaining early adopters; the second was proving that it could save medical practitioners time; the third and ultimate one was proving that it could save money for the entire system, including hospitals, insurers and the public purse.

“The final frontier for us will be when we can do something - say a blood test - that never has to touch a human hand. With enough evidence, safety data and a certain clinical setting, a radiologist won’t even have to read it.”

“Then we can say the AI suite of products is infinitely scalable around the world.” 

Craig Wong-Pan

Craig Wong-Pan