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Revise fundamental AI webpage structure and content
Updated the structure and content of the fundamental AI webpage, including new sections on AI Safety and Human–AI Ecosystem.
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‎fundamental-ai.html‎

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Fundamental AI — BioIntelligence Lab</title>
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<style>
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body {
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margin: 0;
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font-family: "Inter", sans-serif;
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color: #222;
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line-height: 1.6;
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background: #ffffff;
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}
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/* NAVIGATION BAR (same as homepage) */
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nav {
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width: 100%;
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padding: 20px 40px;
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box-sizing: border-box;
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position: sticky;
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top: 0;
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background: rgba(255,255,255,0.90);
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backdrop-filter: blur(8px);
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display: flex;
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justify-content: space-between;
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align-items: center;
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border-bottom: 1px solid #eee;
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z-index: 1000;
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}
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nav .logo img {
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height: 45px;
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cursor: pointer;
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}
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nav ul {
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list-style: none;
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display: flex;
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gap: 30px;
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margin: 0;
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padding: 0;
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}
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nav ul li a {
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text-decoration: none;
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color: #222;
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font-weight: 500;
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transition: color 0.2s;
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}
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nav ul li a:hover {
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color: #003d99;
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}
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/* PAGE HEADER */
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.header-block {
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text-align: center;
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padding: 120px 20px 80px;
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background: #f7f9fc;
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}
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.header-block h1 {
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font-size: 2.6em;
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margin-bottom: 10px;
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font-weight: 700;
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color: #111;
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}
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.header-block p {
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font-size: 1.2em;
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color: #555;
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max-width: 800px;
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margin-left: auto;
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margin-right: auto;
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}
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/* SECTION TITLE */
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h2.section-title {
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text-align: center;
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margin-top: 70px;
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margin-bottom: 25px;
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font-size: 1.9em;
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font-weight: 600;
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}
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/* CONTENT SECTIONS */
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.content-block {
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max-width: 900px;
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margin: 0 auto;
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padding: 10px 20px 40px;
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color: #333;
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font-size: 1.05em;
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}
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.placeholder-img {
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width: 100%;
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height: 220px;
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background: #e9eef5;
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border-radius: 8px;
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margin: 30px 0;
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display: flex;
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align-items: center;
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justify-content: center;
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color: #708090;
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font-size: 1.1em;
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font-style: italic;
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}
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/* PUBLICATION LIST */
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.pub-section h3 {
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margin-top: 40px;
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font-size: 1.3em;
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color: #003d99;
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}
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.pub-section ul {
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margin-top: 10px;
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line-height: 1.55;
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}
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</style>
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</head>
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<body>
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<!-- NAVIGATION BAR -->
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<nav>
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<div class="logo">
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<a href="index.html">
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<img src="https://github.com/BioIntelligence-Lab/BioIntelligence-Lab.github.io/blob/main/images/Lab_logo3.png?raw=true" alt="Lab Logo">
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</a>
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</div>
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<ul>
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<li><a href="index.html#research">Research</a></li>
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<li><a href="index.html#tools">Software</a></li>
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<li><a href="index.html#people">People</a></li>
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<li><a href="index.html#contact">Contact</a></li>
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</ul>
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</nav>
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<!-- HEADER -->
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<section class="header-block">
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<h1>Fundamental AI Research</h1>
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<p>
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Advancing the foundations of Artificial Intelligence through safety, trustworthiness, human–AI ecosystems,
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and multi-agent autonomy. Our work pushes beyond application-driven AI to explore how intelligent systems
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learn, collaborate, and evolve over time.
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</p>
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</section>
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<!-- AI SAFETY -->
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<h2 class="section-title">AI Safety & Trustworthiness</h2>
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<div class="content-block">
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<p>
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This sub-area focuses on ensuring AI systems are reliable, transparent, and equitable.
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We investigate algorithmic bias, uncertainty modeling, robustness to real-world variation,
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security vulnerabilities, demographic leakage in foundation models, and safe use of generative AI
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in clinical decision-making.
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</p>
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<div class="placeholder-img">[ Placeholder: Diagram on AI Safety / Bias / Robustness ]</div>
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<div class="pub-section">
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<h3>Representative Publications</h3>
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<ul>
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<li>Beheshtian et al., Radiology, 2022 — Bias in pediatric bone age prediction.</li>
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<li>Bachina et al., Radiology, 2023 — Coarse race labels masking underdiagnosis patterns.</li>
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<li>Santomartino et al., Radiology: AI, 2024 — Stress testing and robustness evaluation.</li>
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<li>Trang et al., Emergency Radiology, 2024 — Sociodemographic bias in ICH detection.</li>
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<li>Kavandi et al., AJR, 2024 — Predictability of demographics from chest radiographs.</li>
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<li>Santomartino et al., Radiology, 2024 — Bias in NLP tools for radiology reports.</li>
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<li>Garin, Parekh, Sulam, Yi et al., Nature Medicine, 2023 — Need for demographic transparency.</li>
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<li>Yi et al., Radiology, 2025 — Best practices for evaluating algorithmic bias.</li>
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<li>Zheng, Jacobs, Parekh et al., arXiv, 2024 — Demographic predictability in CT embeddings.</li>
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<li>Zheng, Jacobs, Braverman, Parekh et al., arXiv, 2025 — Adversarial debiasing in CT models.</li>
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<li>Kulkarni et al., MIDL, 2024 — Hidden-in-plain-sight imperceptible bias attacks.</li>
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</ul>
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</div>
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</div>
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<!-- HUMAN–AI ECOSYSTEM -->
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<h2 class="section-title">Human–AI Ecosystem Modeling</h2>
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<div class="content-block">
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<p>
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We study how humans and AI systems can learn from each other, share experience, collaborate across institutions,
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and form collective intelligence. This includes the development of SheLL (Shared Experience Lifelong Learning),
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multi-agent reasoning frameworks, and the foundations needed to build autonomous research workflows.
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</p>
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<div class="placeholder-img">[ Placeholder: SheLL / Multi-Agent Collaboration Diagram ]</div>
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<div class="pub-section">
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<h3>Representative Publications</h3>
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<ul>
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<li>Uwaeze, Kulkarni, Braverman, Jacobs, Parekh, ICCV 2025 — Counterfactual augmentation for equitable learning.</li>
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<li>Kulkarni et al., MIDL 2024 — Stealth bias attacks informing ecosystem resilience.</li>
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<!-- Add more as papers emerge -->
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</ul>
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</div>
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</div>
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</body>
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</html>
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---
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layout: default
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title: Fundamental AI
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---
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<section class="header-block">
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<h1>Fundamental AI Research</h1>
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<p>
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Advancing the foundations of Artificial Intelligence through safety, trustworthiness, human–AI ecosystems, and
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multi-agent autonomy — exploring how intelligent systems learn, collaborate, and evolve over time.
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</p>
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</section>
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<!-- AI SAFETY -->
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<h2 class="section-title">AI Safety & Trustworthiness</h2>
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<div class="content-block">
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<p>
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We develop methods to ensure AI systems are reliable, transparent, and equitable — addressing bias, uncertainty,
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robustness to real-world clinical variation, security vulnerabilities, demographic leakage, and safe use of
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generative AI in radiology and beyond.
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</p>
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<div class="placeholder-img">[ Placeholder: Diagram — Bias / Robustness / Security ]</div>
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</div>
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<!-- HUMAN-AI ECOSYSTEM -->
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<h2 class="section-title">Human–AI Ecosystem</h2>
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<div class="content-block">
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<p>
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We investigate how AI systems can collaborate with each other and with humans — learning across sites, agents,
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and tasks. This includes the development of SheLL (Shared Experience Lifelong Learning), multi-agent reasoning,
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and foundations for autonomous research workflows.
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</p>
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<div class="placeholder-img">[ Placeholder: Diagram — SheLL / Multi-Agent Learning ]</div>
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</div>

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