🩺 Cardiologist Builds Award-Winning AI Health Assistant in 7 Days

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AIHealthTech Insider: Issue #89

February 23, 2026

This issue highlights physician-built AI tools, generative models accelerating biomedical research, new biological pathways linking exercise to brain health, machine-learning risk scores tied to cancer, and AI systems improving diagnostic precision in pathology.

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

🩺 Cardiologist builds 7‑day AI health assistant that wins big prize

A practicing cardiologist built PostVisit.ai, an AI care platform powered by Opus 4.6, in just seven days. Ranked 3rd out of 13,000 applicants, it unifies a patient’s medical history, connected devices, and evidence‑based resources into one interface showing how modern AI now lets physicians independently create advanced, patient‑facing tools.

Why it matters

• Shows clinicians can now build full-stack health apps solo

• Centralizes fragmented patient data into one AI-guided companion

• Demonstrates real-world use of massive‑context models in care

• Highlights a new class of physician‑built agentic platforms

👉 Source → PostVisit.ai

🧠AI crunches pregnancy and gut data in minutes, not months

Researchers at UCSF and Wayne State used generative AI on complex pregnancy and microbiome datasets, creating prediction pipelines that matched or outperformed human teams, completing weeks of coding in minutes.

Why it matters

• Cuts months from medical data analysis timelines

• Helps accelerate research on preterm birth, which affects ~1,000 U.S. babies daily

• Reduces dependence on large, specialized data science teams

• Allows researchers to focus on scientific questions instead of debugging code

👉 Source → Benchmarking large language models for predictive modeling in biomedical research with a focus on reproductive health

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Do you know:🏃Exercise may protect the brain from Alzheimer’s

UCSF researchers found that exercise boosts the brain's protective barrier in aging by causing the liver to release an enzyme (GPLD1) that removes a harmful protein (TNAP) from brain blood vessels, reducing leakiness, inflammation, and improving memory in older mice.

Why it matters

• May explain how exercise lowers Alzheimer’s risk

• Identifies a new liver-to-brain anti-aging pathway

• Shows memory decline can be reversed late in life (in mice)

• Points to new drug targets beyond traditional brain-focused therapies

👉 Source → Liver exerkine reverses aging- and Alzheimer’s-related memory loss via vasculature

🔎 New AI “sugar stress” score tied to higher cancer risk

A Nature Communications study used machine learning to estimate insulin resistance from 9 routine clinical measures. In UK Biobank data, the AI-derived score (AI-IR) predicted future diabetes better than BMI and common proxies and was linked to increased risk of several cancers, including uterine, kidney, esophagus, pancreas, colon, and breast.

Why it matters

• Uses routine labs to flag risk
• Links insulin resistance to cancer risk
• May refine screening beyond BMI
• Supports earlier prevention strategies

👉 Source → Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer

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🧬 AI spots hard‑to‑see blood cancers on bone marrow slide

A Leukemia study developed an AI to differentiate between prefibrotic primary myelofibrosis (prePMF) and essential thrombocythemia (ET) using digitized bone marrow images. The model achieved an AUROC of 0.89 and 92.3% accuracy in thrombocytosis cases, suggesting it detects signals beyond classic criteria, such as higher marrow fat in ET and higher cellularity in prePMF.

Why it matters

• Helps separate two similar MPNs
• Could reduce pathologist discordance
• Flags adiposity/cellularity as signals
• May improve risk-aligned care decisions

👉 Source → Artificial intelligence differentiates prefibrotic primary myelofibrosis with thrombocytosis from essential thrombocythemia using digitized bone marrow biopsy images

🩺 New AI “Doctor” app tracks your health for life

Superpower introduced an AI Doctor designed to provide continuous healthcare by maintaining detailed records of symptoms, medication, lab trends, lifestyle, and goals. It incorporates "healthy skepticism" to flag inconsistencies between behavior and biomarker data, and offers citations and reasoning for verification.

Why it matters

• Brings continuity back to primary care
• Connects labs, symptoms, and habits
• Holds users accountable to their data
• Shows transparent sources and reasoning

👉 Source → Superpower | AI Doctor

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