 Machine-learning mania
If you’re a living human, you’ve probably heard A LOT about “artificial intelligence” by now. It’s not in your imagination – everyone is seemingly obsessed with AI right now. First thing’s first. AI = any system in which tasks that typically require human intelligence are instead completed by a computer or machine. Where we are with AI in the U.S: - AI is to 2019 what blockchain was to 2018. Mentions of “artificial intelligence” have now surpassed mentions of “blockchain” on public quarterly earnings calls.
- There’s a whole lot of money where the machine-learning mouth is…Since 2013, healthcare AI startups have raised $4.3B, the most AI-funding across all industries.
- On just one day of BIO, three separate panels had “artificial intelligence” somewhere in their title.
- Doctors are being trained in AI. This week alone, The Icahn School of Medicine at Mount Sinai announced a new AI center, and the American Medical Associationannounced that AI will be incorporated into medical education.
- There’s even a Congressional Caucus on artificial intelligence.
There’s huge potential for AI in healthcare, and a diverse set of uses. - There are already FDA-approved AI softwares for clinical imaging & diagnostics. One software program can screen patients for diabetic retinopathy, another analyzes cardiac imaging with its cloud platform. Yet another identifies liver and lung lesions for cancer screening.
- AI could disrupt clinical trial design and recruitment. Apple has established AI-enabled platforms that help recruit patients for clinical trials - and monitor their health remotely.
- Big pharma sees AI potential in drug discovery. This year, a number of pharma companies have announced partnerships with AI-startups to discover new drugs, using algorithms to identify novel therapeutic candidates.
- Made in China. China has set a goal to be the world leader in AI by 2030, with a $150 billion industry and a particular focus on health. AI is already being used to treat millions of Chinese patients through telemedicine platforms and technologies like Ping An Good Doctor, which connects patients to a remote AI “doctor.”
But there are also a number of risks. The use of AI in healthcare poses new threats to data privacy and security, challenges the patient-physician relationship, and creates some legal and ethical conundrums. And then there’s the potential for AI to fail, as we saw with IBM Watson Health, which had trouble integrating data and sometimes provided inaccurate medical advice. (IBM has since scaled back its hospital business and halted sales of its tool for drug discovery) As the industry warms to AI more and more, the stakes for failure get higher and higher. Overwhelmed? So are we. Here’s one more tidbit for the road… Automated agents designed by DeepMind, Google’s AI lab, can now play virtual capture the flag. In fact, they exhibit “humanlike behavior” when playing. We’re still getting over how creepy the name “DeepMind” is for an artificial intelligence group…  Who wrote this? The managing editors of TWTW are Dana Davis, whose weekend goal is to achieve “inbox zero” and Randi Kahn, who wishes she could use AI to pack for her.
Syneos Health Communications' Reputation & Risk Management Practice is a team of healthcare communications consultants, policy-shapers and crisis response specialists. We provide unique solutions to the evolving communications challenges in today’s healthcare industry, using evidence-based approaches to help our clients successfully navigate the most sensitive of situations.  Got thoughts? Contact Dana   
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