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Meghan Oates-Zalesky 

Meghan Oates‑Zalesky is Chief Marketing Officer at CapeStart and MadeAi, with over 20 years of experience in healthcare technology and life sciences. Previously CMO at Apollo Intelligence, she helped grow the company from a small startup into a global enterprise. A PharmaVOICE Woman of Influence (2021), Meg’s thought leadership has appeared in leading industry publications. She holds degrees from Harvard and the London School of Economics and is a published novelist and avid equestrian.

83 articles published Follow:
How-AI-is-Transforming-Literature-Reviews-into-Drug-Repurposing-Opportunities

How AI is Transforming Literature Reviews into Drug Repurposing Opportunities

White PaperJune 25, 202630Views0Likes0Comments
Drug repurposing has the potential to deliver new therapies faster and at a fraction of the cost of traditional drug development. However, identifying meaningful opportunities remains difficult, with valuable signals often hidden across thousands of research publications. MadeAi combines AI-powered literature review with expert human validation to accelerate evidence synthesis while maintaining transparency, accuracy, and…

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How-MadeAi-Accelerated-PSUR-Evidence-Generation-Through-AI-Assisted-Systematic-Literature-Review

How MadeAi Accelerated PSUR Evidence Generation Through AI-Assisted Systematic Literature Review

Case StudyJune 17, 202665Views0Likes0Comments
How do you prepare Periodic Safety Update Reports (PSURs) without spending months manually reviewing clinical literature and safety evidence? In this case study, MadeAi partnered with a medical device manufacturer to conduct a systematic literature review supporting PSUR requirements. Using AI-assisted screening, expert validation, and structured evidence extraction, the team streamlined the review process and…

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From-AI-Assisted Screening to Publication-Grade-Evidence-for-a-Global-HEOR-Team

From AI-Assisted Screening to Publication-Grade Evidence for a Global HEOR Team

Case StudyJune 11, 202681Views0Likes0Comments
How do you screen thousands of abstracts with transparency, reproducibility, and submission-ready outputs in a fraction of the time? In this case study, MadeAi partnered with a leading pharmaceutical HEOR team to design and validate an AI-powered pipeline for systematic literature review. Using a structured, PICO-based framework, the team screened thousands of records across multiple…

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The AI Evaluation Problem in Evidence Synthesis

The AI Evaluation Problem in Evidence Synthesis

BlogJune 3, 2026131Views0Likes0Comments
Why the Industry is Testing the Wrong Thing The life sciences industry faces a growing paradox: organizations are investing heavily in AI, yet many evaluation methods fail to measure how these systems actually perform in real-world evidence synthesis workflows. Every week, new evaluations emerge, and task forces publish guidelines. Academic papers rank AI tools. Organizations…

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How do you prepare a Clinical Evaluation Report (CER) without spending months on literature review and evidence synthesis

How MadeAi Accelerated Clinical Evaluation Report Development Through AI-Assisted Literature Review

Case StudyJune 2, 202655Views0Likes0Comments
How do you prepare a Clinical Evaluation Report (CER) without spending months on literature review and evidence synthesis? In this case study, MadeAi partnered with a medical device regulatory team to conduct a systematic literature review aligned with MDR and MEDDEV requirements. The team searched multiple databases, screened studies, extracted key evidence, and generated structured…

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Rethinking AI Assessment in Evidence Synthesis

Rethinking AI Assessment in Evidence Synthesis: A System-Level Framework for Rigorous Evaluation

White PaperMay 22, 202668Views0Likes0Comments
AI adoption in evidence synthesis often stalls when traceability, governance, and real-world validation requirements enter the picture. Most evaluations still focus on isolated models instead of the full systems used in regulated workflows. MadeAi embeds human validation, workflow orchestration, auditability, and governance into every stage of evidence synthesis helping organizations move toward defensible, production-ready AI…

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