
Fortunately, just like in the movies, I was in luck, finding
myself a few weeks ago at a keynote discussion as part of the 8th
annual Biopharma Sustainability Roundtable listening to Dr. Helen Routh. And,
much to my surprise, I learned there is a huge role for corporate
responsibility (CR) professionals in ensuring their organizations practice
“responsible AI.”
![]() |
Helen Routh |
But let’s
start with our leading lady. Helen is as much a super hero as any you’ll find on
the silver screen. She spent 25 years at Philips, in innovation, business and
strategy roles including four years as Senior Vice President of Strategy & Innovation. A common thread throughout her career has
been the use of data to drive significant outcome improvements in health care, leading
to what would be depicted today as “AI.”
Today, she is a board member and advisor in both public
and private sectors and currently chairs Ultromics, an outcomes-based AI
company spun out of the University of Oxford. It develops ultrasound-based
diagnostic support tools for cardiovascular disease by combining deep clinical
insights with machine learning and some of the largest cardiac ultrasound
datasets in the world.
While
Helen’s keynote didn’t involve any car chases or love triangles, it didn’t
disappoint in painting a picture of immense hope for the future of health care
through the power of AI. And, like an Oscar-winning director, Helen infused her
story with dramatic risks, driving home that the only way the world can reap the vast fruits of this innovation is by gaining
a real understanding of the potential unintended negative effects and how to avoid
them.
Let’s
start with the positive. If used well, data and analytics, whether strictly AI,
machine learning or deep learning, has the power to dramatically improve health
outcomes, enhance the quality of care, reduce health care costs, and expand
access globally.
Starting
in the developed world, Helen shared several real-world examples:
·
N of One: Recently acquired by Qiagen, it has developed proprietary
technology and a knowledgebase called MarkerMineTM, which provides
high-quality and actionable clinical interpretation of molecular tests for
oncology patients. The clinical decision
support technology links patients’ tumor profiles with potential therapeutic
strategies, including those still in clinical trials, greatly improving the
chance for effective treatment.
·
HeartFlow:
Developer of cloud-based software that aids cardiologists in coronary artery
disease diagnosis. HeartFlow creates a 3-D model of a patient’s coronary arteries
and applies algorithms to locate blockages to blood flow and helps determine a
more precise treatment plan. It has been in beta testing for the past three
years in 80 health care centers in the
United States and abroad. The technology helps reduce unnecessary angiograms
and other invasive procedures and shortens turnaround time to diagnosis.
·
CareSageTM:
Developed by Philips, this predictive analytics engine integrates data from
patients’ records with Lifeline (medical alert company) enrollment and medical
alert service activity. The information is merged into models to score a
patient’s risk of admission to the hospital or nursing center in the next 30
days. In a retrospective analysis of 2,000 Lifeline subscribers, the software accurately
predicted a 40 percent reduction in admissions.
And here’s
one more that I learned about since Helen’s talk that illustrates how robotics
can be combined with AI in health care delivery. Mazor Robotics uses AI to aid minimally invasive surgical
operations as well as operations with complex anatomy. Before an operation, a
patient’s CT scan is loaded into a 3-D computerized planning system to indicate
where a surgeon should place implants—all before the patient even arrives.
Mazor’s spinal surgery robot arm guides the orthopedic surgeon’s instruments,
allowing for an extremely high degree of precision.
Pretty cool stuff. And AI also has potential to drive
game-changing improvements in health in the developing world. A new report, Artificial Intelligence in Global Health: Defining a Collective Path Forward, recently published by USAID, the
Rockefeller Foundation, and the Bill & Melinda Gates Foundation, sees AI as
a tool to enable community health workers to better serve patients in remote
areas, help governments prevent deadly disease outbreaks, and greatly improve
health care delivery to vulnerable communities.
The report includes dozens of scenarios of AI’s potential use for
good. One tells the story of Anita, a woman living a rural village in Western
Kenya, six hours from Nairobi and two hours on dirt roads from the closest
hospital. Anita has recently became a community health worker and now goes door
to door in her community providing local patients with health advice and
selling basic health products to address their needs.
Anita has a smartphone with various apps that she uses in her
work; she enters simple information on her patients’ health condition,
including symptoms they are currently experiencing. Her AI-enabled apps then
provide health recommendations, diagnoses, treatment advice, and self-care
recommendations that allow her to provide the best possible care to her
patients and allows them to avoid travel to a health facility hours away.
Enter
Dramatic Music
But now comes the moment in the story for drama when the good guys
do their thing (which surely involves some karate kicks and jumps off tall
buildings) to ensure risk is avoided so that the world can benefit from this
life-altering technology.
In the case of the developing world, the USAID/Rockefeller/Gates report
calls out the challenge of taking AI solutions from high-income countries and
deploying and scaling them to address the unique needs of populations in
low-income environments. Fortunately, the report’s authors serve up
recommendations to guide the appropriate use of AI in low- and middle-income
contexts.
Back at the
keynote, Helen revealed several of the critical factors that must be addressed
to deliver the potential of data, analytics and AI to both developed and
developing countries:
·
Data security
and privacy should already be top of mind for all companies and particularly
those in health care. “Any business or organization not already thinking about how
to mitigate the risk of data breaches or other cyber-security-related threats
is at risk.”
·
Data quality is critical
to deliver meaningful results. Helen draws on the principle of “garbage in,
garbage out,” noting that it is key to understand the quality of data used in
building an application and how it applies to the specific clinical problem at
hand.
·
Bias can be embedded
(even unintentionally) in historical data sets, results and interpretations –
whether this arises from using data for a different application, not
understanding the impact of how data was collected, or population differences.
·
Public understanding
and trust are critical to allow AI to thrive. Organizations must clearly
communicate how a patient or citizen’s data will be used, how value is returned
to the patients or citizens and be able to explain at a high level what a
particular application does. It’s hard
to build trust when AI is perceived as a “black box” where something magic
happens to your data.
While not
part of Helen’s remarks, in my own research I came across a tool from
Accenture’s new Applied Intelligence practice called the “AI Fairness ToolTM,”
which helps identify and fix unintended biases in AI solutions. The tool examines
“data influence” of sensitive variables (age, gender, race, etc.) on other
variables in a model, measuring how much of a correlation the variables have
with each other to see whether they are skewing the model and its outcomes.
Another group working to maximize the public good of AI and
related technologies is the Partnership on AI to Benefit People and Society. With
more than 80 partners (including UNICEF) spanning 13 countries, the Partnership
studies and formulates best practices for AI technologies, works to advance the
public’s understanding and trust in AI, and serves as an open platform for
discussion about the influences of AI on people and society.
“Where AI tools are used to supplement or replace human decision making,
we must be sure that they are safe, trustworthy and aligned with the ethics and
preferences of people who are influenced by them,” reads the Partnership’s
website.
In health care, building trust and understanding of AI among the
public will also require engagement with patient advocates and patient/disease-focused
societies, and, as Helen notes, communicating the clinical not the business benefits of the technology.
And this is precisely where the role of CR professionals comes in:
We can help identify potential unintended risks; put in place responsible
policies; bring in the voice of diverse stakeholders who may be affected by the
AI-enabled technologies; and ensure transparency around the organization’s use
of AI (thus, helping to build public trust). In summary, CR professionals must
help ensure their organizations practice “responsible AI.”
Helen’s address taught me that AI is not science fiction; it is
here, all around us already. It has the potential for much good, but society
–business, government, academia and research institutions – must come together
to define and practice responsible AI in order to reap the potential benefits
and ensure a happy ending that would make Hollywood proud.
No comments:
Post a Comment