Dr. Arif Ahmed Sekh, AI researcher at UiT The Arctic University of Norway and co-author of the book Gender and Diversity Policy in AI, played a key role in the AI Impact Summit 2026 session titled “AI in Healthcare: Navigating Innovation, Ethics, and Regulatory Compliance.” The session brought together global clinicians, researchers, and policymakers to examine how artificial intelligence is transforming healthcare while ensuring ethical, inclusive, and trustworthy deployment.
Recognized for his contributions to artificial intelligence in healthcare and his scholarly work on ethical AI governance, Dr. Sekh emphasized the importance of integrating diversity, fairness, and explainability into healthcare AI systems. With several tens of research publications spanning AI, healthcare analytics, and responsible AI frameworks, he has been actively working at the intersection of technological innovation and societal impact.
Advocating Inclusive and Ethical AI in Healthcare
During the session introduction, Dr. Sekh underscored that artificial intelligence in healthcare must go beyond technical performance to address broader societal considerations. He highlighted that bias in AI systems, lack of representativeness in datasets, and unequal access to advanced technologies can potentially widen healthcare disparities if not addressed proactively.
Drawing from his work on gender and diversity policy in AI, he stressed the need for:
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Inclusive dataset design
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Transparent algorithmic decision-making
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Ethical regulatory frameworks
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Patient-centric AI deployment strategies
He emphasized that responsible AI adoption must ensure equitable benefits across populations rather than reinforcing existing inequalities.
AI Innovation with Societal Responsibility
Dr. Sekh’s research contributions in AI-driven healthcare analytics were reflected in the broader panel discussions, which explored applications ranging from cardiology diagnostics to oncology treatment optimization and fertility medicine innovations. He highlighted how explainable AI models can improve clinician trust, regulatory acceptance, and patient safety.
According to Dr. Sekh, responsible AI innovation requires:
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Interdisciplinary collaboration between clinicians and AI researchers
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Strong validation pipelines before clinical deployment
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Continuous ethical oversight
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Integration of diversity considerations into AI system design
These elements, he noted, are essential for building long-term trust in healthcare AI.
Global Research Engagement and Future Directions
Dr. Sekh also highlighted ongoing collaborative initiatives at UiT focusing on responsible AI, healthcare innovation, and interdisciplinary research. These efforts aim to foster international collaboration between academic institutions, healthcare providers, and policymakers to ensure ethical AI deployment worldwide.
The session also referenced upcoming collaborative initiatives such as the AI4Fertility scientific workshop in Tromsø, Norway, which will further explore AI applications in reproductive healthcare and responsible innovation frameworks.
Building Trust in AI-Driven Healthcare
The session generated strong engagement from clinicians, researchers, and industry participants, reflecting growing global interest in ethical AI deployment. Discussions continued beyond the formal session, indicating the urgency and relevance of the topics addressed.
Dr. Sekh reiterated that the future of AI in healthcare must balance technological progress with human-centered values, ethical safeguards, and inclusivity.
“As AI becomes deeply embedded in healthcare decision-making, ensuring fairness, transparency, and diversity is not optional — it is essential for sustainable and trustworthy innovation,” he emphasized.
About Dr. Arif Ahmed Sekh
Dr. Arif Ahmed Sekh is an AI researcher at UiT The Arctic University of Norway, specializing in artificial intelligence for healthcare, responsible AI governance, and interdisciplinary biomedical applications. He is the author of Gender and Diversity Policy in AI and has published extensively on AI innovation, machine learning, healthcare analytics, and ethical AI frameworks.
