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Good morning! 

Welcome to Healthcare AI News, your weekly dose of the latest developments and headlines in the world of Healthcare AI.

In this issue, we explore:

✅ Headlines: Why Bill Gates says AI will supercharge med innovations

✅ Industry: How to navigate the privacy crossroads between AI and HIPAA

✅ Interesting Reads: Scientists link Christmas to penile fractures

✅ Tech: Top 5 Kafka usecases

✅ Feature: Five Multimodal AI applications changing the healthcare game

🌟Please note that there will be no publication on Tuesday, the 2nd. We will resume our regular publishing schedule on Thursday, the 4th. Wishing you all a Happy New Year! 🌟

HEADLINE ROUNDUP

• Engineers develop a vibrating, ingestible capsule that might help treat obesity (Read More)

• Why Bill Gates says AI will supercharge medical innovations (Read More)

• New stretchy, wearable throat sensor processes and predicts health data faster (Read More)

• Watch out for fake, AI-generated medical information (Read More)

• AI-powered app could be ‘revolutionary’ for cystic fibrosis patients (Read More)

• AI health coaches are coming soon to a device near you (Read More)

• Injection of “smart insulin” regulates blood glucose levels for one week (Read More)

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INDUSTRY NEWS

• Diabetes increases mortality risk for long-term cancer survivors (Read More)

• Subscription-based health care can deliver medications to your door, but its rise concerns some experts (Read More)

• How to navigate the privacy crossroads between AI and HIPAA (Read More)

• Private equity ownership of hospitals made care riskier for patients, a new study finds (Read More)

• Weight-loss drug use, mental health concerns among trends in employer-based health care in 2024 (Read More)

• More hospitals and health systems are automating revenue cycle operations, with AI (Read More)

• Kindbody’s push for profitability stirs ethical debate in fertility industry (Read More)

INTERESTING READS

• Why do doctors still use pagers? (Read More)

• Benefits of running in the cold outweigh warm weather running (Read More)

• Scientists link Christmas to penile fractures (Read More)

TECH NEWS

• 10 things software developers should learn about learning (Read More)

• December healthcare data breach round-up (Read More)

• Top 5 Kafka usecases (Read More)

• Japan to crack down on Apple and Google app store monopolies (Read More)

HEALTHCARE AI JOBS

• Senior Machine Learning Engineer

• Lead Cognitive/Machine Learning Professional – MLP

• National Health Tech and AI Initiatives Director

THE FEATURE

Five Multimodal AI Applications Changing The Healthcare Game

“AI can’t do this yet, but…”

There’s a lot of qualifying in conversations about healthcare AI right now.

So, what will it take for our dreams about biomedical AI to match up better with reality?

If you ask us, it’s the rise of multimodal AI.

These types of models can process multiple different types of data at the same time, drawing all of those insights together.

So what kinds of transformations can we expect multimodal AI to bring to healthcare? Let’s get into it.

Today, we’re briefing you on 5 areas of multimodal AI disruption:

1. Personalized medicine

2. Remote patient monitoring

3. Virtual health assistants

4. Public health

5. Interoperability

1. Personalized medicine

By integrating all kinds of diverse datasets (i.e., imaging, clinical notes, omics, wearable data), medical AI’s predictive and diagnostic abilities are going to get crazy precise.

We can imagine a million scenarios where highly personalized predictive power would supercharge healthcare. But to save time, here are three examples:

• More effective “curbside consults” — where clinicians can turn to multimodal LLMs for an expert second opinion based on comprehensive case data.

• Digital twin tech representing real living, breathing patients more accurately.

• Personalized longevity tech startups that can more precisely tailor biological age analysis and longevity-focused preventive care to individual consumers.

In sum: Diverse dataset analysis means better patient-and-treatment matching. Finally, complex cases can be interpreted and analyzed with LLMs.

2. Remote patient monitoring

Heart rate monitors. Blood pressure monitors. Your Garmin watch. 

All of these devices track a lot of data about your health. Analyzing all of that non-standardized data? Not a cakewalk.

But multimodal AI can handle it.

Making that wealth of real-time data usable would not only improve diagnostics overall. It would help bring clinical monitoring out of traditional clinical settings.

Our favorite potential application? The rise of hospital-at-home programs. 

In sum: Taking personalized medicine and elevating the remote experience. Better remote monitoring will finally enable digital clinical trials.

3. Virtual health assistants

Since we’re talking about taking health out of the traditional clinic, we’d be remiss not to mention virtual health assistants.

When it comes to self-management of various chronic health conditions, these tools can be game-changers.

We’re thinking of tools like Virta for diabetes and weight management or Woebot for depression.

Multimodal AI makes them more effective communicators.

Diverse, real-time data analysis would allow for frequent, personalized feedback for the patient. 

Plus, multimodal LLMs would be able to tackle patient communication across language barriers.

In sum: Multimodal AI promises greater personalization and responsiveness for generative AI screening and monitoring tools.

4. Public health surveillance

We’ve been talking a lot about personalization for individual patients. Now, let’s zoom out a bit.

Multimodal can take our pandemic—or even just localized outbreak—surveillance to a whole new level. 

COVID monitoring apps showed us how integrating multiple real-time data sources to track disease spread can work. And also how ineffective it can be without the right tools.

In sum: Geospatial data, mobility data, and EHRs all coming together is an epidemiologist’s dream. We can get much more effective and pinpointed outbreak and pandemic surveillance.

5. Ushering in healthcare interoperability

Especially in the U.S., we’ve been fighting an uphill battle in our quest toward healthcare interoperability.

Yes, there’ve been many strides as more health systems have adopted FHIR. 

But multimodal AI can take us a step further. 

Imagine a multimodal LLM as a hospital’s “central hub” for data exchange. Unimodal AI managing diverse data systems (e.g., radiology software, insurance systems, EMRs) would be connected through this hub, allowing the entire system to operate in concert.

In sum: The efficacy of many healthcare SaaS and AI solutions depends on health systems’ progress with interoperability. Can we finally get there with one solution?

Final thoughts from HAN

Granted, multimodal AI is not a magic wand.

For it to effectively work across these exciting applications, a few other things need to be in place. Namely, we’ve really got to nail down data quality. 

Plus, we can’t just abandon privacy concerns that arise with the kind of data analysis the public health surveillance example brings up. 

But for now, we’re going to let ourselves get excited as we help others understand what (we hope) is in store for this next stage of the healthcare AI revolution.

Tell us: Which multimodal AI applications are you most excited about? And what questions do you have about multimodal AI that we can dig more into?

TWEET OF THE WEEK

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