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AIHealthTech Insider: Issue #120

September 28, 2026

This week: What if a scan taken before cancer treatment could help flag a serious side effect before it happens? MD Anderson researchers found a possible warning in routine lung CT scans. We also look at an AI prediction for brain tumor recurrence, a newborn infection standard screening may miss, and a striking finding about glucose monitors.

Here’s what changed, in plain English.

Summaries are for education, not medical advice. Always verify locally before clinical use.

☕ Quick question before we dive in 👇

Image: AI-generated, AIHealthTech Insider

👇 Later in this issue: A newborn can pass a hearing test and still have an infection that affects hearing later. Here’s what a large screening study found.

🔬 The Big Story

A routine CT scan could flag lung inflammation before cancer treatment

Researchers at MD Anderson developed an AI model that analyzes routine CT scans to identify patients at higher risk of pneumonitis, a potentially serious lung inflammation linked to immunotherapy. The study included 347 patients, and the model was tested on an external group, with a reported AUC of about 0.83—a measure of how well it distinguished higher-risk from lower-risk patients.

Image: AI-generated, AIHealthTech Insider

Why it matters: If further testing confirms the result, clinicians could use scans they already have to decide who may need closer attention during treatment.

The honest part: This was a retrospective study. The model has not yet shown that using its predictions improves patient outcomes.

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𝘐𝘯 𝘮𝘢𝘬𝘪𝘯𝘨 𝘢𝘯 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯, 𝘪𝘯𝘷𝘦𝘴𝘵𝘰𝘳𝘴 𝘮𝘶𝘴𝘵 𝘳𝘦𝘭𝘺 𝘰𝘯 𝘵𝘩𝘦𝘪𝘳 𝘰𝘸𝘯 𝘦𝘹𝘢𝘮𝘪𝘯𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘵𝘩𝘦 𝘪𝘴𝘴𝘶𝘦𝘳 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘵𝘦𝘳𝘮𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨, 𝘪𝘯𝘤𝘭𝘶𝘥𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘦𝘳𝘪𝘵𝘴 𝘢𝘯𝘥 𝘳𝘪𝘴𝘬𝘴 𝘪𝘯𝘷𝘰𝘭𝘷𝘦𝘥. 𝘋𝘐𝘛 𝘈𝘨𝘛𝘦𝘤𝘩 𝘩𝘢𝘴 𝘧𝘪𝘭𝘦𝘥 𝘢 𝘍𝘰𝘳𝘮 𝘊 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘪𝘦𝘴 𝘢𝘯𝘥 𝘌𝘹𝘤𝘩𝘢𝘯𝘨𝘦 𝘊𝘰𝘮𝘮𝘪𝘴𝘴𝘪𝘰𝘯 𝘪𝘯 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘪𝘵𝘴 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨, 𝘢 𝘤𝘰𝘱𝘺 𝘰𝘧 𝘸𝘩𝘪𝘤𝘩 𝘮𝘢𝘺 𝘣𝘦 𝘰𝘣𝘵𝘢𝘪𝘯𝘦𝘥 𝘩𝘦𝘳𝘦: https://bit.ly/4bzuWCi​  ​

⚡ Quick Hits

🔸Can AI tell when a brain tumor will return?

A system called FastGlioma showed promise in predicting glioblastoma recurrence from tumor tissue. Researchers developed it using data from 60 patients and tested it with 20 more.

Image: AI-generated, AIHealthTech Insider

The honest part: The test group was small, and the study has not shown that acting on the prediction helps patients live longer or receive better care.

🔸An AI correction works better when its tone fits the reader

In a study involving 857 parents, Washington State University researchers tested AI responses to HPV vaccine misinformation in a simulated social media setting. The most effective tone varied with how humanlike participants believed the AI to be.

Image: AI-generated, AIHealthTech Insider

Why it matters: Getting a medical fact right may not be enough; how a correction is delivered can affect whether people accept it. The result comes from a simulated setting, so real-world effects still need testing.

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🌍 Beyond AI

The infection a newborn hearing test can miss

Researchers screened 48,556 newborns for congenital cytomegalovirus (CMV), an infection that can affect hearing or development later. They found 176 infected babies; 100 would not have qualified for the usual targeted screening, often because they appeared healthy at birth.

Image: AI-generated, AIHealthTech Insider

Why it matters: A baby can pass an initial hearing test and still need follow-up. The study shows how broader screening might find families who otherwise would not know.

The honest part: The research came from two hospitals in Jerusalem. It did not establish whether universal screening is cost-effective everywhere.

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🧠 Brain in Brief

Blood proteins shift during Alzheimer’s treatment

In a study of 197 people receiving lecanemab, researchers identified changes in 34 blood proteins during treatment. Those shifts could offer clues about how the drug affects the body beyond its intended target.

Image: AI-generated, AIHealthTech Insider

The honest part: This was a single-clinic study. The protein changes are research findings, not a validated way to tell whether an individual patient is benefiting.

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🌿 Worth Knowing

Glucose monitor users had a lower death rate—but the cause is uncertain

Among people with type 2 diabetes taking basal insulin, researchers compared 13,935 people who started continuous glucose monitoring with the same number of matched nonusers. After one year, 1.16% of monitor users had died, versus 2.09% of nonusers.

Image: AI-generated, AIHealthTech Insider

Why it matters: The difference is large enough to warrant closer study of how glucose monitors might affect longer-term health, beyond day-to-day blood sugar control.

The honest part: This is preliminary observational research being presented at a conference. Even after matching patients on 29 characteristics, it cannot prove that the monitors caused the lower death rate.

Stay ahead of AI Healthcare

This week’s takeaway: Earlier warnings are promising. The next test is whether finding them changes what happens to patients.

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