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Plus: eye discomfort on the rise after eclipse


Welcome to your briefing:

  • AI ROUNDUP: ChatGPT gets confused when confronted with good evidence

  • INDUSTRY ROUNDUP: Ten doctors on FDA panel reviewing Abbott Heart Device had financial ties with company

  • WELLNESS BYTES: 100 grams of protein: What it actually looks like and how you can achieve it daily

  • INSIGHTS CORNER: Data sharing with AI startups: How to approach it

  • TRIVIA: What is the AI developed at Johns Hopkins for tracking tools in surgery called?


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  • ChatGPT gets confused when confronted with good evidence (Read More)

  • GE HealthCare brings cardiac AI coaching software to handheld ultrasound probe (Read More)

  • When an antibiotic fails: MIT scientists are using AI to target “sleeper” bacteria (Read More)

  • AI-Based Biomarker for Aortic Stenosis found by Yale researchers (Read More)

  • More deal volume, lower check sizes for digital health in Q1, with AI investment leading the pack: Rock Health (Read More)

  • Dell Technologies and Northwestern Medicine to advance patient care through AI Innovation (Read More)

  • Powering mental health assessments with AI: An SMB’s innovative solution (Read More)


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  • Ten doctors on FDA panel reviewing Abbott Heart Device had financial ties with company (Read More)

  • Concerns about eye discomfort appear to rise after solar eclipse (Read More)

  • More kids are dying of drug overdoses. Could pediatricians do more to help? (Read More)

  • Trump's abortion announcement left one big question (Read More)

  • Unauthorized ACA plan switches drive call for action against rogue agents (Read More)


  • 100 grams of protein: What it actually looks like and how you can achieve it daily (Read More)

  • Brain health and diet: Nutrition helps lower dementia risk (Read More)

  • Addressing inflammaging proactively can mitigate its impact on healthspan (Read More)


Data Sharing with AI Startups: How to approach it

As healthcare organizations venture into partnerships with AI startups for developing custom Large Language Models (LLMs) or to tap into advanced AI capabilities, the pivotal issue of data sharing comes to the forefront.

Who owns the “secret sauce”??

We are at a crucial juncture, balancing the promise of innovation with the imperative to protect sensitive patient data and ensure regulatory compliance.

Navigating this complex landscape demands a nuanced approach. It’s essential to achieve a fine balance, offering AI startups access to the data they need to spark innovation, while steadfastly protecting patient privacy and adhering to stringent data security standards. This balancing act, though challenging, is manageable with thoughtful, strategic data sharing practices.

Consider these strategies to navigate the complexities of data sharing effectively:

  • Emphasize Data Quality and Relevance: Quality trumps quantity. Select high-quality, relevant data that directly supports the AI startup’s specific goals or problem-solving efforts. This precision ensures the startups have potent, meaningful insights, reducing the risk of unnecessary exposure of sensitive information.

  • Advocate for Synthetic Data Generation: Embrace the power of synthetic data to provide realistic, anonymized datasets that mirror the characteristics of real patient data. This approach allows startups to refine their models without compromising patient privacy, striking a balance between innovation and confidentiality.

  • Explore Federated Learning: This technique enables collaborative AI model training without centralizing sensitive data. By training models locally and sharing only the insights, not the data itself, we can maintain data security and privacy, fostering a cooperative yet secure environment for innovation.

  • Enforce Comprehensive Data Governance: Effective data governance acts as a beacon through the data-sharing maze. Establishing and adhering to clear guidelines for data access, classification, and usage helps navigate the intricacies of data sharing while ensuring compliance and responsible stewardship of patient information.

  • Utilize Privacy-Enhancing Technologies: Technologies like differential privacy, homomorphic encryption, and secure multi-party computation offer robust data protection. These tools allow for meaningful data analysis and model training without exposing individual patient details, adding a robust layer of security and ensuring regulatory adherence.

  • Cultivate Trust and Transparency: Openness is crucial in these partnerships. Fostering a transparent relationship with AI startups, underscored by clear communication about data sharing rules and security protocols, builds mutual trust. Regular audits and transparent channels of communication reinforce this trust, aligning with both organizational ethics and strategic goals.

Adopting a mindful and ethical approach to data sharing enables healthcare organizations to harness the power of AI, driving innovation while safeguarding patient privacy and data integrity. This strategic equilibrium not only advances patient care but also sustains the trust of all stakeholders, setting the stage for a future where technology and ethical data management coexist seamlessly.


What is the AI developed at Johns Hopkins for tracking tools in surgery called?

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