Artificial intelligence could transform healthcare, but we need to accept it first

Artificial intelligence could transform healthcare

Scientists in Japan reportedly saved a woman’s life by applying artificial intelligence to help them diagnose a rare form of cancer. Faced with a 60-year-old woman whose cancer diagnosis was unresponsive to treatment, they supplied an AI system with huge amounts of clinical cancer case data, and it diagnosed the rare leukemia that had stumped the clinicians in just ten minutes.

The Watson AI system from IBM matched the patient’s symptoms against 20m clinical oncology studies uploaded by a team headed by Arinobu Tojo at the University of Tokyo’s Institute of Medical Science that included symptoms, treatment and response. The Memorial Sloan Kettering Cancer Center in New York has carried out similar work, where teams of clinicians and data analysts trained Watson’s machine learning capabilities with oncological data in order to focus its predictive and analytic capabilities on diagnosing cancers.

IBM Watson first became famous when it won the US television game show Jeopardy in 2011. And IBM’s previous generation AI, Deep Blue, became the first AI to best a world champion at chess when it beat Garry Kasparov in a game in 1996 and the entire match when they met again the following year. From a perspective of technological determinism, it may seem inevitable that AI has moved from chess to cancer in 20 years. Of course, it has taken a lot of hard work to get it there.

But efforts to use artificial intelligence, machine learning and big data in healthcare contexts have not been uncontroversial. On the one hand, there is wild enthusiasm – lives saved by data, new medical breakthroughs, and a world of personalised medicine tailored to meet our needs by deep learning algorithms fed by smartphones and FitBit wearables. On the other there’s considerable scepticism – a lack of trust in machines, the importance of individuals over statistics, privacy concerns over patient records and medical confidentiality, and generalised fears of a Brave New World. Too often the debate dissolves into anecdote rather than science, or focuses on the breakthrough rather than the hard slog that led to it. Of course, the reality will be somewhere in the middle.

In fact, it may surprise you to learn that the world’s first computerised clinical decision-support system, AAPhelp, was developed in the UK way back in 1972 by Tim De Dombal and one of my colleagues, Susan Clamp.

This early precursor to the genius AI of today used a naive Bayesian algorithm to compute the likely cause of acute abdominal pain based on patient symptoms. Feeding the system with more symptoms and diagnosis helped it to become more accurate over time and, by 1974, De Dombal’s team had trained the system to the point where it was more accurate at diagnosis than junior doctors, and almost as accurate as the most senior consultants.

 

Share it:
Share it:

[Social9_Share class=”s9-widget-wrapper”]

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

You Might Be Interested In

How to prepare for a career in machine learning and artificial intelligence

30 Apr, 2019

Thinking about pursuing a career in ML and AI? Here’s what you need to know. Staying ahead of the tide …

Read more

Why digital transformation is poised to make a dramatic impact

3 Jan, 2019

Digital transformation is sweeping across the enterprise landscape with billions of dollars being spent on moving organisations to be more …

Read more

How startups can compete with enterprises in artificial intelligence and machine learning

3 Aug, 2016

When I woke up this morning, I asked my assistant a simple question: “Siri, is it going to rain today?” Siri …

Read more

Recent Jobs

Cyber Security Engineer – P2

Hybrid (Aurora, CO, USA)

5 Mar, 2024

Read More

Sr. Manager – Data and Analytics Technical Lead

Hybrid (Dedham, MA, USA)

5 Mar, 2024

Read More

Manager, Business Data and Analytics

Hybrid (Troy, OH, USA)

5 Mar, 2024

Read More

Do You Want to Share Your Story?

Bring your insights on Data, Visualization, Innovation or Business Agility to our community. Let them learn from your experience.

Get the 3 STEPS

To Drive Analytics Adoption
And manage change

3-steps-to-drive-analytics-adoption

Get Access to Event Discounts

Switch your 7wData account from Subscriber to Event Discount Member by clicking the button below and get access to event discounts. Learn & Grow together with us in a more profitable way!

Get Access to Event Discounts

Create a 7wData account and get access to event discounts. Learn & Grow together with us in a more profitable way!

Don't miss Out!

Stay in touch and receive in depth articles, guides, news & commentary of all things data.