www.lexpert.ca 9
that there are a lot of malevolent actors out
there," says Miller Olafsson, with the potential
ability to hack into centralized systems as part
of a ransomware attack or other threat.
Even in its more basic uses, the potential
of AI and machine learning is enormous.
But the tricky part of using it in the health
care sector is the need to have access to
incredible amounts of data while at the same
time understanding the sensitive nature of
the data collected.
"For artificial intel-
ligence to be used in
systems, procedures, or
devices, you need access
to data, and getting
that data, particularly
personal health informa-
tion, is very challenging,"
says Carole Piovesan,
managing partner at INQ
Law in Toronto.
She points to the devel-
oping legal frameworks in
Europe and North America for artificial intel-
ligence and privacy legislation more generally.
Lawyers working with start-up companies or
"FOR ARTIFICIAL INTELLIGENCE TO BE
USED IN SYSTEMS, PROCEDURES, OR
DEVICES, YOU NEED ACCESS TO DATA,
AND GETTING THAT DATA,
PARTICULARLY PERSONAL HEALTH
INFORMATION, IS VERY CHALLENGING"
Carole Piovesan
INQ LAW
health care organizations to build AI systems
must help them stay within the parameters of
existing laws, says Piovesan, and provide guid-
ance on best practices for whatever may come
down the line and help them deal with the
potential risks.
Risk can take many forms, including the
"human risk factor" behind these AI systems
and whether they have the right talent
trained to use them. "And then there is the
governance structure needed to ensure that
the system operates as intended. So I think
lawyers have a huge role to play in the process
of using AI wisely."
Piovesan's partner at INQ, Mary Jane
Dykeman, agrees. "ere must be confi-
dence in the data itself. Is it clean? Is it
usable? Is the data biased? And one assumes
the data is legally obtained and meets
privacy obligations."
• CENTRE HOSPITALIER DE
L'UNIVERSITÉ DE MONTRÉAL –
developed a tool in collaboration
with Gray Oncology to optimize
appointment scheduling in radiation
oncology and medical oncology. Tests
show a reduced waiting time of 20
percent.
• EASTERN HEALTH IN ST. JOHNS, NL
– partnered with IBM to develop an AI-
based mental health tool for health care
workers. A chatbot interacts with users
to make recommendations for support
services. With its introduction, the
number of employees accessing mental
health services increased dramatically.
• UNITY HEALTH TORONTO – using AI
to manage staffing levels and triage
acute patient care in the emergency
room. After implementing its machine-
learning platform, St. Michael's Hospital
saw a 20 percent reduction in mortality
among internal medicine patients.
ARTIFICIAL INTELLIGENCE
IN ACTION WITHIN
CANADIAN HEALTH CARE
