‘The Future of AI and Older Adults 2023’ now published

June 14, 2023 | by

Laurie Orlov of Aging and Technology Watch in her latest paper tackles the latest iterations of AI and ML, tracing their roots back to 2014 to the original smart speakers and voice assistance, technologies that enabled older adults to access services with convenience and at reasonable cost. What will be the impact of AI using tools such as large language models (LLM) like ChatGPT to develop improved search, voice assistance, answers to health questions, and care plans written in understandable and empathetic language? For care facilities and senior housing, will they leverage AI with voice and sensor tech to improve safety monitoring for both residents and caregivers, plus the dream of predictive health for residents or those living at home with limited assistance? Will chatbots get a lot smarter versus obnoxious? Find out what both the short term and long term (5+ year) impact could be. 

Ms. Orlov’s somewhat gimlety view includes Gartner’s infamous Hype Cycle chart on page 5. As of today, most AI technologies reside in the balmy Peak of Inflated Expectations, the place where whatever investment funding is going. There’s lots of innovation and kitchen table hackathoning. Looming about two years out is the inevitable Trough of Disillusionment which has already been kicked off by Big Thinkers such as Steve Wozniak. As this Editor observed last month, it is a double-edged sword, with the bad side in its potential for data misuse, fraud, fakery, and malicious action. It’s already created controversy that this Editor predicts will crest in the next year with demands for regulation. We’re not there yet, however.

Download of the PDF is here and free.

A sobering, mercifully hype-free view of AI in healthcare

October 3, 2018 | by

Way up there on the Peak of Inflated Expectations in the Gartner Hype Cycle is that two-letter creature, AI. Artificial Intelligence has been invoked in multiple tech fields, and Microsoft in the US currently is running 30 second commercials about how AI is “making tomorrow today” but without much explanation as to how.

If AI’s current puffery makes you dizzy, long-time observer of the Healthcare Scene Anne Ziegler’s article in Hospital EMR and EHR might stabilize the whirlies. In direct and brief terms, she classifies the realities of healthcare AI adoption in three areas:

  1. Lack of Transparency. How does AI reach its conclusions in making ‘good decisions’? Sometimes the logic of the conclusion is obvious, but often it is not, and what you get is physician and clinician bypass–and suspicion.
  2. That Old Monkey Wrench Tossed into Existing Processes. It’s taken a long time for organizations to fully integrate their EHR inputs and documentation. Throwing in an AI implementation even in a limited sense may require more adjustments than the outcomes are worth.
  3. It’s Too, Tooooo Much Data. Healthcare organizations do not suffer from a paucity of data. AI feeds on data. Sounds like a good match, doesn’t it. Except that a lot of this data isn’t usable without filtering and mining, and that takes a lot of processing. The future may have more advanced data processing and indexing tech to do that, but right now even natural language processing to identify useful information is rare in the field.

Widespread AI use in healthcare is, despite the IBM Watson Health hype, a long way off. In healthcare, the rubber must meet the road of patient care and clinical practicality to be useful to us with Non-Artificial Intelligence. Problems We Need To Address Before Healthcare AI Becomes A Thing