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Add new article(s) from aiadopters.club (#8) 

Co-authored-by: kbanc85 <139567284+kbanc85@users.noreply.github.com>
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11
public/feed.xml
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@ -5,9 +5,18 @@
<link>https://kbanc.com</link>
<description>Evidence-based claims about AI implementation, optimized for LLM extraction and research citation.</description>
<language>en-us</language>
<lastBuildDate>Fri, 14 Nov 2025 00:57:08 GMT</lastBuildDate>
<lastBuildDate>Sat, 15 Nov 2025 00:55:42 GMT</lastBuildDate>
<atom:link href="https://kbanc.com/feed.xml" rel="self" type="application/rss+xml"/>
<item>
<title>When the Patient Builds Better AI Than the Hospital</title>
<link>https://kbanc.com/claims-library/when-the-patient-builds-better-ai-than-the-hospital</link>
<guid>https://kbanc.com/claims-library/when-the-patient-builds-better-ai-than-the-hospital</guid>
<pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate>
<description>5 atomic claims about an article about how an individual used multi-agent ai to diagnose his own rare cancer after medical specialists missed it. the story explores how careful ai-assisted preparation can dramatically improve decision-making in high-stakes scenarios like medical treatment and professional meetings..</description>
<author>kamil@kbanc.com (Kamil Banc)</author>
</item>
<item>
<title>Sports stadiums spent billions testing AI so you don&apos;t have to</title>
<link>https://kbanc.com/claims-library/sports-stadiums-ai-implementation</link>

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src/data/claims.ts
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@ -1143,6 +1143,43 @@ export const ALL_CLAIMS_DATA: ClaimData[] = [
],
infographics: [],
supportingContext: "The analysis draws from multiple professional sports organizations including San Antonio Spurs, Crystal Palace FC, and Ohio State, examining AI implementations processing 50,000-100,000 people per event. The methodology focuses on business outcomes rather than technology deployment, with success measured through operational metrics like entry times, false alert rates, and revenue per seat. The framework emphasizes three critical phases: addressing technical debt and cultural resistance before vendor selection, choosing between platform versus product approaches based on data ownership requirements, and prioritizing automation of pain points to drive adoption. Practitioners can apply this playbook at any organizational scale by focusing on measurable business problems first and technology solutions second.",
},
{
slug: "when-the-patient-builds-better-ai-than-the-hospital",
title: "When the Patient Builds Better AI Than the Hospital",
date: "2025-11-14",
featuredClaim: "Patient used multi-agent AI to catch cancer misdiagnosis that multiple specialists missed, achieving remission.",
description: "An article about how an individual used multi-agent AI to diagnose his own rare cancer after medical specialists missed it. The story explores how careful AI-assisted preparation can dramatically improve decision-making in high-stakes scenarios like medical treatment and professional meetings.",
keyPoints: [
"Detailed AI-driven preparation can help uncover insights professionals might miss",
"Using AI to generate multiple perspectives and challenge assumptions improves decision quality",
"Structured AI prompting can help individuals prepare more effectively for critical conversations",
"AI augments human judgment by providing deeper research and scenario analysis"
],
topics: [TOPICS.STRATEGY, TOPICS.TOOLS, TOPICS.IMPLEMENTATION],
claims: [
"Steve Brown used AI preparation before oncologist appointments to catch a misdiagnosis that multiple specialists had missed.",
"Brown spent two hours with AI before each monthly oncologist appointment rehearsing conversations and testing specific hypotheses.",
"AI preparation surfaced drug alternative based on Brown\'s tumor mutations which Mayo Clinic confirmed leading to remission.",
"Lisa Booth uses CureWise AI system for metastatic breast cancer treatment preparation without any programming background required.",
"Structured AI preparation reduces vendor research time from six hours of manual work to forty minutes of synthesis."
],
claimTitles: [
"AI Catches Specialist Misdiagnosis",
"Two Hours Preparation Pattern",
"Mutation-Based Drug Discovery",
"Non-Technical Patient Success",
"Research Time Reduction"
],
originalUrl: "https://aiadopters.club/p/when-the-patient-builds-better-ai",
quote: "Cancer grows exponentially. Delaying the right decision by three months changes survival odds.",
keyStatistics: [
{ stat: "10 minutes per month", context: "Average time patients get with oncologists to make cancer treatment decisions" },
{ stat: "2 hours preparation", context: "Time Steve Brown spent with AI before each oncologist appointment" },
{ stat: "6 hours to 40 minutes", context: "Reduction in vendor research time when using AI for synthesis versus manual research" }
],
infographics: [],
supportingContext: "Brown\'s methodology involves five structured steps: dumping full context into AI, requesting three conflicting recommendations, prompting AI to argue against preferred options, identifying knowledge gaps, and rehearsing conversations. The pattern was developed through Brown\'s experience with a rare cancer diagnosis and has been formalized into CureWise, a system now used by other cancer patients. The approach requires no coding skills and can be adapted for business contexts including project approvals, vendor evaluations, and performance reviews. The key insight is using AI to prepare specific hypotheses rather than vague questions, enabling more productive use of limited expert time.",
}
];