Why AI adoption in healthcare is lagging?

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Welcome to this week’s edition of Supermedic, where we explore the latest developments in artificial intelligence and its transformative impact on healthcare.

Stay informed, stay ahead. Enjoy the dive!


  • AI Boosts Diabetes Prevention 💉

  • Mobile App Detects Depression Before It Starts😔 

  • AI Outperforms Human Specialists in Retina Management 👁️

  • Why Healthcare Lags Behind in AI Adoption? 🐌

  • OpenAI vs Nvidia: Battle of the Titans ⚔️

Read time: under 7 minutes


AI Boosts Diabetes Prevention, Saves Money, and Paves the Way for Personalized Care

The US spends more than $730 billion annually on treating preventable diseases like type 2 diabetes. To improve prevention, researchers from Austin Texas University created an AI tool to identify patients who would benefit most from preventive medication like metformin.

This tool, developed by Professor Maytal Saar-Tsechansky, uses machine learning to analyze electronic health records. It considers factors beyond traditional risk scores, such as body measurements, lab tests, and medications.

The study, involving over 89,000 prediabetic patients, demonstrated the effectiveness of the AI model:

  • Prevented 25% more diabetes cases compared to traditional methods.

  • Saved $2.9 million more per 10,000 patients compared to traditional methods.

  • Could save the US $1.1 billion annually in healthcare costs.

This data-driven approach could be applied to other diseases too, leading to more personalized and effective preventive care, potentially reducing overall healthcare costs.


This Mobile App Can Spots Depression Before It Surfaces

As depression becomes the fastest-growing mental health issue in modern societies, MoodCapture app offers a timely AI-based beacon of hope for early detection.

Researchers at Dartmouth have developed an mobile app called MoodCapture that use AI and facial-image processing to detect early sign of depression.

Using the front camera, it analyzes facial expressions and environmental cues, achieving 75% accuracy in identifying depression symptoms.

They’re hoping it’ll be ready for everyone in about five years, offering a nonintrusive method for early detection and intervention, marking a significant advancement in digital mental health technology.


AI can now Matches or Outperforms Human Specialists in Retina and Glaucoma Management

A study from the New York Eye and Ear Infirmary of Mount Sinai (NYEE) demonstrate that a large language model (LLM), specifically GPT-4, can diagnose and treat glaucoma and retina diseases as well as or better than human ophthalmologists.

This research highlights AI's potential to support decision-making in ophthalmology, a field that deals with a high volume of complex cases.

The study compared the AI's performance on a set of patient cases and commonly asked questions against that of 12 specialists and three senior trainees from the Icahn School of Medicine at Mount Sinai, finding that AI matched or exceeded human performance in accuracy and thoroughness.

This suggests AI could significantly aid ophthalmologists by reducing workload and improving patient care, though further testing is recommended to explore its full potential in clinical settings.


Why Is Healthcare Falling Behind in AI Adoption?

Healthcare's dive into AI isn’t as swift as other industries. Amazon Web Services shed some light on the reasons with an interesting study.

Here's the quick scoop:

  • Just 9% of healthcare folks are pretty confident with AI, the lowest of any group surveyed.

  • Only 53% of healthcare places really see bringing in AI talent as important.

  • 78% of healthcare employers recognize their own lack of knowledge in setting up AI training programs as a significant barrier.

  • They think 38% of their team will be using AI by 2028.

  • The primary anticipated benefit of AI in healthcare is improved workflow and outcomes, as stated by 61% of employers.

  • Nearly half (47%) of healthcare workers believe AI will most benefit communication tasks, such as report generation and invoicing.

  • Healthcare employers and staffers predict a 44% increase in labor productivity after AI is fully deployed.


Open AI vs Nvidia: Battle of the AI Titans

[Disclaimer: This article is distinct from our usual health-centric focus. However, understanding the dynamics between Nvidia and OpenAI is crucial. Nvidia's GPUs play a pivotal role in medical LLM training, while OpenAI's developments, like ChatGPT, find applications in healthcare as personal assistants or diagnostic tools. Their innovation race not only shapes the AI technology we use in healthcare but also underscores the importance of staying informed about the leaders driving AI advancement.]

The commercial world thrives on rivalry, from classic battles like Coke vs. Pepsi to modern clashes such as Walmart vs. Amazon.

Currently, an intriguing rivalry in the AI sector is unfolding between Nvidia and OpenAI, promising to reshape the commercial and consumer AI landscape.

Nvidia, with its strong footing in GPUs and a recent $2 trillion valuation, contrasts with OpenAI's pioneering AI research, valued at around $80 billion. Despite their differing domains—Nvidia in hardware and OpenAI in software—the lines between them blur as both venture into each other's territories, indicating a merging of resources and escalating valuations. Nvidia's foray into AI chatbots and investments in AI startups positions it as a formidable competitor to OpenAI, which relies on Nvidia GPUs for its AI models.

A wild chatbot appear?

A new entrant, Mistral AI, a 9-month-old French Startup, just launched his Chatbot named Lechat to compete with Chat GPT. Already valued at $2 billions, it intensifies this existing rivalty but also signals the dynamic and rapidly changing landscape of AI, where new entrants can swiftly become key players.

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