The rise of technologies such as ChatGPT has thrust artificial intelligence into the spotlight throughout 2023 害羞草研究所� and health care is no exception.

害羞草研究所淲ith the increasing availability of health-care data and the rapid progress in analytic techniques 害羞草研究所� whether machine learning, logic-based or statistical 害羞草研究所� AI tools could transform the health sector,害羞草研究所� the World Health Organization said when it launched a set of regulatory recommendations in October.

As we move into 2024, here are some key AI developments 害羞草研究所� and cautions 害羞草研究所� that will be top of 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 harnessing the ability of a computer model to process and interpret 害羞草研究所渕ulti-modal害羞草研究所� data about a patient, said Roxana Sultan, chief data officer and vice-president ofhealth at the Toronto-based Vector Institute dedicated to AI research.

Right now, AI models can make a diagnosis based on one or two pieces of information, such as an X-ray, Sultan said. That害羞草研究所檚 achieved by training the model on 害羞草研究所渢ons and tons of X-ray images害羞草研究所� so it learns to recognize certain diagnoses.

害羞草研究所淭hat is fantastic. But that is (only) one source of information,害羞草研究所� Sultan said.

In the 害羞草研究所渘ear future,害羞草研究所� she said, machine learning will develop so that AI can take a 害羞草研究所渕uch more comprehensive look at patient health.害羞草研究所�

In addition to a patient害羞草研究所檚 X-ray, for example, AI would be able to process other data, including doctor害羞草研究所檚 notes, lab samples, medications the patient is taking and genetic information.

That ability will not only play a critical role in diagnosing a patient, but also in coming up with a more personalized treatment plan, Sultan said.

害羞草研究所淲hen you have models that understand the complex interplay between a person害羞草研究所檚 genetics and a person害羞草研究所檚 medications and all the different diagnostic tests that you run on that patient, you pull those together into a picture that allows you to not only understand what害羞草研究所檚 happening in the moment, but also to kind of plan ahead that, if I applied this treatment 害羞草研究所� what is the more likely outcome for this particular person?害羞草研究所�

Russ Greiner, who holds a fellowship with the Alberta Machine Intelligence Institute, agreed.

害羞草研究所淭he standard medical practice used to be one size fits all,害羞草研究所� said Greiner, who is also a professor of computing science at the University of Alberta.

害羞草研究所淣ow you realize that there害羞草研究所檚 huge differences amongst individuals 害羞草研究所� different genes, different metabolites, different lifestyle factors, all of which are influential (on health),害羞草研究所� he said.

Machine learning means computers can analyze hundreds or thousands of characteristics about a patient 害羞草研究所� more than a human clinician could possibly process 害羞草研究所� and find patterns 害羞草研究所渢hat allow us to figure out that for this characteristic of patients, you get treatment A, not treatment B,害羞草研究所� Greiner said.

CLINICAL TRIALS

AI害羞草研究所檚 ability to go through enormous amounts of data will also save 害羞草研究所渢ens of thousands 害羞草研究所� probably hundreds of thousands 害羞草研究所� of human hours害羞草研究所� for researchers analyzing the results of clinical trials, said Sue Paish, CEO of DIGITAL, one of five 害羞草研究所済lobal innovation clusters害羞草研究所� across the country funded by the federal government.

害羞草研究所淎I basically can evaluate billions of pieces of data in a fraction of a second,害羞草研究所� said Paish, who is based in Vancouver.

That 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害羞草研究所檚 fed, experts agree.

害羞草研究所淕arbage in, garbage out,害羞草研究所� said Greiner.

害羞草研究所淚f I train on faulty data, the best I can do is to build a model as good as that data, which is problematic.害羞草研究所�

One of the priority areas is to make sure AI is getting data from reliable sources, rather than just indiscriminately taking publicly available information, said Sultan.

ChatGPT, for example, is a technology to 害羞草研究所渆ssentially scrape the internet,害羞草研究所� she said.

害羞草研究所淭he problem with that 害羞草研究所� is first and foremost, it害羞草研究所檚 not always reliable and true,害羞草研究所� Sultan said.

害羞草研究所淎nd second of all, it is riddled with biases and problematic perspectives that get reinforced when you train something that can害羞草研究所檛 make those judgments. It just reads it all, absorbs it and spits it back out for you.害羞草研究所�

One example of a way to improve the quality of medical analyses AI generates is to train it on medical textbooks rather than the internet, Sultan said.

害羞草研究所淚 think the ChatGPTs of the world will seem very caveman, like very rudimentary (in the future),害羞草研究所� she said.

Researchers are also developing AI algorithms to find bias in health information, including racial or gender discrimination, Sultan said.

PATIENT SELF-MANAGEMENT

Another key area where AI will grow is in developing technologies that help patients manage their own health, experts agree.

For example, wearable AI has already been developed to help patients with heart failure self-monitor, Sultan said.

AI has also been used 害羞草研究所渜uite effectively害羞草研究所� in remote areas of Canada to manage some patients害羞草研究所� wounds when they weren害羞草研究所檛 able to access care during the pandemic, said Paish.

The AI technology attaches to a patient害羞草研究所檚 cellphone, takes a 3D image of a wound and assesses whether it害羞草研究所檚 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.

害羞草研究所淚 think we害羞草研究所檙e going to see more and more examples of where AI is actually supporting patient health by reducing the need for a human being to take all the steps in assessment and delivery of health-care services,害羞草研究所� Paish said.

That will take pressure off overburdened doctors, nurses and hospitals and allow them to provide in-person care when it害羞草研究所檚 most needed, she said.

ETHICS AND REGULATION

害羞草研究所淥ne of the big flashing yellow lights in the application of AI is making sure that there is very thorough and thoughtful evaluations of how AI is being trained,害羞草研究所� said Paish.

害羞草研究所淧ublic policy is going to be extremely important.害羞草研究所�

Dr. Theresa Tam, Canada害羞草研究所檚 chief public health officer, said it害羞草研究所檚 critical to develop regulations and safeguards that address ethical issues such as patient privacy.

害羞草研究所淚 think this is a really opportune time to, you know, more systematically look at 害羞草研究所� what governance we have to put in place in order to responsibly use AI,害羞草研究所� Tam said in a recentinterview.

Ensuring data is managed in a way that protects privacy must be 害羞草研究所渋nterwoven害羞草研究所� with AI development, Sultan said, noting that other legal and ethical ramifications are 害羞草研究所渦ncharted territory.害羞草研究所�

害羞草研究所淲e害羞草研究所檙e all trying to figure out what makes the most sense. So issues like consent, issues like data ownership and data custodianship, those are all going to shift in terms of the paradigm that we害羞草研究所檝e looked at them through in the past,害羞草研究所� she said.

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Nicole Ireland, The Canadian Press