AI Improves Flu Vaccine Picks

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AI Predictions Match or Exceed WHO’s Seasonal Flu Strain Recommendations

Every year, health officials face a difficult gamble: the choice of which influenza strains go into the seasonal vaccines, months before flu season begins. If their pick matches circulating viruses, then protection can reach up to 60%. But in bad years, the effectiveness can fall below 20%, leaving millions vulnerable.

Between 2012 and 2021, CDC data show overall vaccine effectiveness in the U.S. averaged below 40%.

Scientists at MIT’s CSAIL and Jameel Clinic have introduced VaxSeer, an AI program designed to take some of the guesswork out of this process. Using data trained on decades of viral sequences and lab test results, the system predicts how flu strains will evolve and how well vaccines will neutralize them.

Testing the model: In a 10-year retrospective analysis, VaxSeer’s vaccine strain recommendations outperformed the WHO’s picks in nine out of ten flu seasons for A/H3N2, and in six out of ten for A/H1N1. In one case, the model flagged a strain a full year before the WHO added it.

The study also found a strong correlation between VaxSeer’s predictions and real-world outcomes like CDC-reported vaccine effectiveness and reduced flu-related doctor visits.

Why this matters: Seasonal influenza infects millions of people and leads to hundreds of thousands of hospitalizations worldwide each year.

Even a modest improvement in vaccine match can translate into thousands of prevented illnesses and medical visits. Advancements like VaxSeer show the potential of helping public health agencies make faster, more accurate strain selections, closing the gap between viral evolution and vaccine design.

For more details: Full Article 

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Clinicians Gain Back Hours as AI Automates Operating Room Documentation for EHRs

Clinicians spend nearly half their day entering notes into electronic health records (EHR), an average of 3.5 to 6 hours. Artisight thinks AI can change that.

The Chicago-based smart hospital company has launched a platform that uses AI and computer vision to autonomously document operating room activity.

From recording when a patient enters or leaves, to logging the start and end of surgery; every step is automatically timestamped into the EHR. The system also prompts staff in real time, alerting surgeons to scrub in or notifying the next surgical team if a case runs long.

Hospitals already using the platform report a 16x return on investment and a 5% increase in surgical scheduling. By offloading routine documentation, the company says clinicians can focus more on patients and less on paperwork.

This is another step in turning hospitals into data engines, where every action, movement, and outcome feeds back into smarter care.

I do wonder.. at what point do we stop calling it documentation and start calling it surveillance? For now, if it benefits the patient, I’m all on board.

For more details: Full Article

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📢 Other Happenings in Healthcare AI

• Researchers at Uppsala University developed an AI tool that identified early signs of prostate cancer in 80% of biopsy samples initially deemed healthy, offering a potential breakthrough in early detection. [Link]

• AI-driven phishing attacks have surged 700% in healthcare, prompting urgent calls for hospitals to adopt identity-first security, phishing-resistant authentication, and continuous staff education to protect sensitive data and patient care. [Link]

• Colorado delayed implementation of its 2024 AI law to June 2026 after tech industry pushback derailed a special session compromise, giving lawmakers more time to revise the legislation during the next regular session. [Link]

Have a Great Weekend!

Trivia Answer: C) Ferrets

Ferrets catch the flu in a similar way to humans and show similar symptoms, making them the go-to lab animal for testing how flu viruses behave and how well vaccines might work. [Source]