Ready for its closeup, or not ready for primetime?
Headlines about the advent of artificial intelligence, AI, in pretty much every sector of human life or enterprise seem to be a daily occurrence. Other phrases that get thrown around in stories about AI are machine learning, deep learning, neural networks, and natural language processing.
Here’s a handy list, from the transcription company Sonix, which uses some of these AI tools to drive their service:
- Artificial Intelligence (AI) –the broad discipline of creating intelligent machines
- Machine Learning (ML) –refers to systems that can learn from experience
- Deep Learning (DL) –refers to systems that learn from experience on large data sets
- Artificial Neural Networks (ANN) –refers to models of human neural networks that are designed to help computers learn
- Natural Language Processing (NLP) –refers to systems that can understand language
- Automated Speech Recognition (ASR) –refers to the use of computer hardware and software-based techniques to identify and process human voice
A lot of the stories I see about AI are focused on how it might impact, improve, or otherwise influence healthcare. Depending on who you listen to, it sounds like AI is already diagnosing cancer successfully – here are two pieces, from science savvy sources, on how that’s working, “AI is already changing how cancer is diagnosed” from The Next Web, and “AI matches humans at diagnosing brain cancer from tumour biopsy images” from New Scientist, for your reading pleasure.
As aspirational as the idea of AI in healthcare is, and despite the fact that it’s showing some promise in cancer diagnosis, I’m not thinking that it’s time for the champagne, balloons, and glitter … yet.
One of the biggest barriers to AI is the same barrier everyone – on both sides of the stethoscope, and all the way up to the c-suite – in healthcare confronts daily: data access and liquidity. Data fragmentation is rife across the entire healthcare landscape, with EHR systems that don’t talk to each other well (if at all), and insurers unwilling to open their datasets to anyone under cover of “trade secrets.” In “The ‘inconvenient truth’ about AI in healthcare” in the journal Nature, the authors (British, so this is not just an American problem) point out that, “Simply adding AI applications to a fragmented system will not create sustainable change.” Healthcare systems may be drowning in data (they are), but tools to parse all those data lakes into actionable insights aren’t able to bust the dams holding in that data.
Access is one barrier. Another is the ethics of using AI in healthcare. The American Medical Association’s Journal of Ethics devoted an entire edition to that issue in February 2019, with AMA J Ethics editor Michael J. Rigby calling for deeper discussions about preserving patient preferences, privacy, and safety before implementing AI technology widely in healthcare settings. He particularly notes the impact AI could have in medical education, with medical education being shifted from a focus on absorbing and recalling medical knowledge to a focus on training students to interact with and manage AI-driven machines; this shifting would also require attention to the ethical and clinical complexities that arise when humans interact with machines in medical settings.
AI, across all uses, but particularly in healthcare, has to take a long, hard look at how bias can spread algorithmically, once it’s baked into the code that’s running the machines. There are data scientists doing bias detective work, but will the detectives be able to prevent bias, or just bust perpetrators once the biased outcomes appear? Stay tuned on that one.
Is there an upside to AI in healthcare? Absolutely, *if* the ethical issues on privacy and error prevention, and the practical issues on data access, are addressed. AI could pave the way to fully democratizing information, both for patients and front-line clinicians. It could liberate all clinicians from data-input drudgery, or “death by a thousand clicks.” The Brookings Institution has a solid report, “Risks and remedies for artificial intelligence in health care,” as part of its AI Governance series, that breaks down the pros and cons.
Circling back to the question in the headline, is AI in healthcare ready for primetime? This person’s answer: it depends. I think that rigorous study, in the development of AI in medicine and its use in the healthcare system, is required as an ongoing feature of AI tech used in human health. Upside there? A whole new job classification: AI oversight and management.
Casey Quinlan covered her share of medical stories as a TV news field producer, and used healthcare as part of her observational comedy set as a standup comic. So when she got a breast cancer diagnosis five days before Christmas in 2007, she used her research, communication, and comedy skills to navigate treatment, and wrote “Cancer for Christmas: Making the Most of a Daunting Gift” about managing medical care, and the importance of health literate self-advocacy. In addition to her ongoing work as a journalist, she’s a popular speaker and thought leader on healthcare system transformation from the ground up.