TORONTO – The increase in technologies such as ChatGPT has thrust artificial intelligence into the spotlight throughout 2023 and health care is no exception.
With the increasing availability of health-care data and the rapid progress in analytic techniques, whether logic-based, machine learning, or statistical, AI tools could change the health sector, the World Health Organization said when it launched a set of regulatory recommendations in October.
As we move into 2024, there are some key AI developments and cautions which will be on mind for Canadian experts in the new year and beyond.
PERSONALIZED PATIENT CARE
One of the most exciting potential developments in health-care AI is connecting the ability of a computer model to process and interpret multi-modal data about a patient, said Roxana Sultan, vice-president of health at the Toronto-based Vector Institute.
At the moment, AI models can make a diagnosis based on one or two pieces of information, such as an X-ray, Sultan said. That’s achieved by training the model on tons and tons of X-ray images, which helps to recognize certain diagnoses.
She also added that, in the near future, machine learning will develop so that AI can take a much more comprehensive look at patient health.
In addition to a patient’s X-ray, for example, AI would be able to process other data, including doctor’s notes, lab samples, medications which the patient are taking and genetic information.
That ability will play an important role in diagnosing a patient and also come up with a more personalized treatment plan, says Sultan.
CLINICAL TRIALS
AI’s ability to go through massive amounts of data will also save thousands of human hours, for researchers who are investigating the results of clinical trials, said Sue Paish, CEO of DIGITAL, one of five global innovation clusters across the country which is funded by the federal government.
Mostly AI can evaluate billions of pieces of data in a fraction of a second, which means that new medications could be evaluated for safety and efficacy much faster, she said.
IMPROVING QUALITY OF DATA
Whether AI is being used for clinical care or for health research, the results it generates can only be as good as the data it’s fed, experts agree.
One of the priority areas is to make sure AI is getting data from reliable sources, rather than just extensively taking publicly available information, said Sultan.
For example, ChatGPT, is a technology to essentially scrape the internet, she said.
The issue with that is first and foremost, it’s not always reliable and true, says Sultan
And second of all, it is riddled with biases and problematic perspectives that get reinforced when you train something which can’t make those judgments.
Researchers are also developing AI algorithms to find bias in health information, including racial or gender discrimination.
PATIENT SELF-MANAGEMENT
According to experts, another key area where AI will grow is in developing technologies, which help patients to manage their own health.
wearable AI has already been developed to help patients with heart failure self-monitor, Sultan said.
AI has also been used quite effectively in remote areas of Canada to manage some patients wounds, that is when they weren’t able to access care during the pandemic, said Paish.
The AI technology attaches to a patient’s cellphone, takes a 3D image of a wound and assesses whether it’s infected or healing well.
That information is then sent to a doctor or nurse, who can advise the patient remotely on how to care for the wound.
ETHICS AND REGULATION
One of the big flashing yellow lights in the application of AI is making sure that there are very thorough and thoughtful evaluations of how AI is being trained,” said Paish.
Dr. Theresa Tam, Canada’s chief public health officer, said it’s critical to develop regulations and safeguards that address ethical issues such as patient privacy.
Safeguarding data is managed in a way that protects privacy must be interwoven with AI development, Sultan said.
We’re all trying to find out what makes the most sense. So, problems like consent, problems like data ownership and data custodianship, are all going to shift in terms of the paradigm which we’ve looked at them through in the past, she said.
