AI Detects Diabetic Eye Disease

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Australian Trial Shows AI Eye Scans Detect Diabetic Retinopathy with 93% Accuracy

Globally, more than 529 million people live with diabetes, and many are at risk of losing their sight to diabetic retinopathy, a complication where high blood sugar damages blood vessels in the retina and can lead to vision loss.

Early treatment can prevent blindness in 90% of cases, but millions miss out because they can’t access timely eye exams. Health systems face huge pressure trying to close that gap.

What’s New: A two-year trial across Melbourne and Western Australia tested an AI-powered portable retinal camera in GP and endocrinology waiting rooms.

More than 860 people with diabetes participated between 2021 and 2023. The AI, trained on over 200,000 retinal images graded by 21 ophthalmologists, achieved 93.3% accuracy compared to human grading.

Participants scanned their own eyes while waiting, then received a QR-coded printout with results to share in their appointment.

Those flagged with signs of disease were referred to eye specialists. Surveys found 86% of patients and 85% of clinicians rated the system highly.

The Takeaway: AI scans could be especially valuable in rural and underserved areas where eye care specialists are scarce.

By embedding screenings into routine diabetes visits, health systems could save costs while preventing avoidable blindness.

The trial also highlighted areas for improvement, better image quality, reduced false negatives, and stronger follow-up. But the results suggest AI-assisted eye exams are feasible, effective, and well-received, making sight-saving care more accessible to those who need it most.

For more details: Full Article 

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Noninvasive Brain-Computer Interface Enhanced With AI Helps Paralyzed Participant Complete Robotic Tasks

UCLA engineers have built a noninvasive brain-computer interface (BCI) that uses AI as a co-pilot to help interpret user intent. The system, described in Nature Machine Intelligence, pairs EEG brain signals with an AI-powered camera platform that guides actions in real time.

In tests, four participants (including one paralyzed from the waist down) used the setup to move a computer cursor and control a robotic arm. With AI assistance, all completed tasks faster and more accurately.

The paralyzed participant, who couldn’t finish the robotic arm task without help, succeeded in about six and a half minutes with the AI-assisted system.

Unlike surgically implanted BCIs, which remain limited to small clinical trials due to risks and costs, this wearable approach offers a less invasive path forward.

The work highlights how AI can make external BCIs more practical, moving the technology closer to restoring independence for people with paralysis and other movement disorders.

For more details: Full Article

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📢 Other Happenings in Healthcare AI

• Lawmakers raised concerns about AI chatbots’ mental health risks, especially for youth, but proposed no major regulations; only one bill to support Medicare coverage for AI tools was mentioned. [Link]

• Researchers used a generative AI model to design 50,000 antimicrobial peptides, with several showing antibiotic-level efficacy against resistant bacteria in preclinical tests. [Link]

• Georgia Tech researchers warn that many AI health tools cater to affluent, tech-savvy users, risking exclusion of vulnerable patients, and call for more inclusive, ethical AI design in healthcare. [Link]

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Trivia Answer: B) Allowing paralyzed patients to control robotic limbs

BCI technology has enabled patients with spinal cord injuries or ALS to control robotic arms, cursors, or keyboards using only their brain signals—restoring some ability to interact with the world. [Source]