Revolution (More musings on using artificial intelligence in transfusion medicine)

Updated: 3 Sept. 2020 (Added to Learning Point)

August's blog will discuss the issue of artificial intelligence (AI) & 'big data' in medicine and health.

The idea for the blog was stimulated by a fascinating article in The Economist of June 13-19 as part of its Technology Quarterly: 'Driverless cars illustrate the limits of today's AI.' (Further Reading)

The blog's title derives from a 1968 ditty by the Beatles written by John Lennon

LIMITATIONS OF AI

According to The Economist article, the following are some of AI's current limitations that I've chosen to highlight. Yes they relate to self-driving cars but most have broad applicability relevant to medicine:

1. Self-driving cars work similar to other applications of machine learning. Computers crunch huge piles of data to extract general rules, and in theory the more data, the better the systems perform.  

But deep-learning is statistical, linking inputs to outputs in ways specified by training data. That leaves them unable to cope with “edge cases” ­ unusual circumstances that are not common in those training data.

• You can imagine if this applies to driving cars how much it can apply to the complexities of human beings, their health, symptoms, medical needs, etc. Humans can cope with oddities much better than AI, which in some ways works with only half a brain. 

2. Google’s “Translate” often does a decent job at translating between languages. But in 2018 researchers noticed that, when asked to translate 18 repetitions of the word “dog” into a language spoken in parts of Nigeria and Benin and back into English, it came up with the following hilarious translations:

• “Doomsday Clock is at three minutes to twelve. We are experiencing characters and dramatic developments in the world, which indicate that we are increasingly approaching the end times and Jesus’ return.” 
• Fact: Google’s system doesn't understand language because concepts like verbs and nouns are alien. It's constructed using statistical rules linking strings of letters in one language with strings of letters in another and is baffled by questions a toddler would find trivial. 

3. Richard Sutton, AI researcher at the University of Alberta and DeepMind, published “The Bitter Lesson” (Further Reading) arguing that AI history shows that attempts to build human understanding into computers rarely work. The “bitter lesson” is that “the actual contents of [human] minds are tremendously, irredeemably complex…They are not what should be built in [to machines].” 

Christopher Manning of Stanford University’s AI Lab notes that biological brains learn from far richer data-sets than machines. 

4. About Big Data, which makes AI possible, see 'Big Data in Healthcare' (Further Reading). The conclusion: 'While big data provides great potential for improving healthcare delivery, it is essential that we consider the individual, social and organizational contexts of data use when implementing big data solutions.'  Personal note: 

* The lead author is one of my UAlberta Med Lab Science 'kids'. 

LEARNING POINT

AI has a long way to go before it can be safely used in self-driven cars. Despite the hype, AI has an even longer road to travel before it's as safe and reliable as human health care professionals. Feel free to disagree. 

The entire Technical Quarterly in The Economist, June 13-19, 2020 deals with AI and its limits. As its many proponents hype AI and Big Data, it's prudent to show their limitations too. Included articles:

1. Reality check: After yrs of hype,an understanding of AI's limitations is beginning to set in
2. Data - Not So Big: Data can be scarcer than you think and full of traps
3. Brain Scan | An AI for an eye: Pioneering ophthalmologist highlights the potential, and the pitfalls, of medical AI
4. Computing Hardware | Machine Learning: The cost of training machines is becoming a problem
5. Road Block: Driverless cars illustrate the limits of today's AI
6. The Future | Autumn is coming: As AI's limits become apparent, humans will add more

Note:  If you don't subscribe to The Economist, perhaps take a trial one? Or check if your hospital is affiliated with a university, college (or perhaps the public library) has it available.

As always, comments are most welcome. And there are some.

FOR FUN

I chose this song because pretty much everyone has hopped on Big Data and AI bandwagons as if they're a revolution that's going to sweep traditional medicine aside. Maybe but I suspect not for years.

• Revolution (by the Beatles)

FURTHER READING

Driverless cars illustrate the limits of today's AI - They, and many other such systems, still struggle to handle the unexpected (The Economist, June 13-19, 2020) 

Prior AI blog (30 Nov. 2019): I can see clearly now (Musings on using artificial intelligence in transfusion medicine)

The Bitter Lesson by Rich Sutton (19 Mar. 2019)

Rich Sutton, University of Alberta | Also see this bio

Kuziemsky CE, Monkman H, Petersen C, et al. Big Data in Healthcare - Defining the digital persona through user contexts from the micro to the macro. Contribution of the IMIA Organizational and Social Issues WG. Yearb Med Inform. 2014;9(1):82-9. Published 2014 Aug 15. 

Get back (Musings on transfusion medicine's future)

Updated: 1 Sept. 2019 (Learning pt 4, Further Reading)

August's blog was inspired by a blog I saw on the UK's BBTS website:

• Transfusion 2024: What did we learn and where will we be? (Further Reading)

To me Dr. Nicholas Watkins' blog had 3 related themes:

• How to replace retiring staff (and their experience) with new staff, including retaining them.
• Innovation and technology can help 
• As can big data (electronic donor and patient records)

The blog's title comes from a 1969 Beatles ditty.

MY TAKE
Be aware I've worked in transfusion for decades. My views are biased by long experience as are the opinions of everyone.

Staffing
In the 1990s I saw how regionalization and centralization of hospital transfusion service laboratories affected staffing, along with semi-automatic instruments. In Alberta, Canada (perhaps everywhere?) that meant many transfusion labs required fewer knowledgeable specialists and could get by with mostly medical technologists who were generalists,  plus lab assistants. Another factor was an AB conservative government that removed 40% of the province's lab budget to decrease a budgetary deficit.

Similar changes across Canada resulted in all medical lab educational programs closing in Western Canada except for the two in Edmonton, NAIT and the University of Alberta's MLS. I taught in MLS but as the University of Alberta Hospital's transfusion service clinical instructor I also taught  NAIT med lab students.

Automation came much earlier to blood supplier donor testing labs. In effect donor testing labs could be mostly staffed by technologists experienced in highly automated clinical chemistry labs.

Learning point #1:
To me, these events meant a huge loss of laboratory transfusion expertise in immunohematology. In Edmonton, Alberta, for example, experienced technologists had to compete for the few remaining jobs based on seniority and many left the field. Those with a BSc in Med Lab Science (who wrote ASCP exams) were able to move to USA (and overseas to countries such as NZ) and work for years.

I don't see 'innovation and technology' as truly helping the loss of expertise except in the sense it means:

• Med lab profession can be 'dumbed down.' With increasing technology no one needs much expertise to perform routine tasks. And I don't mean generalists and lab assistants are 'dumb', I respect them for their skills, just that their lack of transfusion expertise is the new normal in many labs. 
• We can only hope so long as serology survives, there's a safety net in all workplaces where the few knowledgeable staff catch any errors.

Learning point #2:
Today the biggie is molecular testing, which means immunohematology expertise will eventually become passé. Presumably, if biotech manufacturers succeed with marketing campaigns that promote matching blood donors and transfusion recipients for antigens with known genes, not just in multi-transfused patients but as the gold standard for ALL transfusion recipients, serologists will no longer be needed.

Transfusion recipients will no longer develop alloantibodies from transfusion, except for ones the DNA PhD gurus haven't identified. But let's hype the hell out of precision medicine to increase profits of commercial interests.

Sounds like a perfect world, no? Local med lab staff numbers shrink to a precious few. Their pesky staff benefits are greatly reduced as an employer cost. Instead of supporting a local economy, money is funneled to foreign biotech companies, who thrive by pleasing their shareholders who grow richer and richer.

Big Data
Yep, big data can provide insights and feed into artificial intelligence (AI) to further remove error-prone humans from healthcare decisions. The downsides include patient privacy and the reality that machines make mistakes. GIGO rules and AI is only as good as human input.

Learning point #3: Privacy is big data's greatest challenge and if it fails (as is likely), big data will become just another failed trend. As to AI, I suspect it's decades away from filling the skilled worker shortage in the transfusion world. But it's already got niche roles in medicine (Further Reading).

Learning point #4: Presumably one day in the distant future AI, automation, and robotics will make human work passé. It's already started and not just on car manufacturing assembly lines. Have you seen the Android robots from Japan or those providing robotic nursing care? With an aging population and worker shortage, robots can fill the bill. (Further Reading)

My vision for the future includes humans who cannot communicate with other humans by talking and have developed enormous thumbs for texting and perhaps sexting. 😉

FOR FUN
Choose this Beatles ditty for blog's title song

• Get back (Paul McCartney, Live in Lisbon 2004)

It's my attempt at a joke as we can never get back to the days where oldsters like me once belonged. 😄

As always, comments are most welcome. And there are some - see below.

FURTHER READING

• Transfusion 2024: What did we learn and where will we be?
• Android robots (3:07 min. video)
• Desperate for workers aging Japan turns to robotic nursing care (25 July 2019)
• Artificial Intelligence in Medicine: Applications, implications, and limitations (19 June 2019)
• Chen JH, Asch SM. Machine learning and prediction in medicine — beyond the peak of inflated expectations. N Engl J Med. 2017 Jun 29; 376(26): 2507–9.