The world’s most advanced AI depends on a single semiconductor manufacturer in geopolitically fragile Taiwan. This article explores why TSMC has become AI’s critical choke point, the risks of overconcentration, and what global diversification means for the future of artificial intelligence.
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The global AI boom has driven massive investment and soaring expectations, but warnings of an imminent bubble burst are growing louder. This analysis explores the risks, opportunities, and impact on healthcare, with insights from Indian and international leaders.
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Apple’s iPhone 17 event unveiled the thinnest iPhone yet but fell short on AI, disappointing investors and users. With pricing challenges in emerging markets and delayed Siri upgrades, Apple must balance design with intelligence and affordability to stay competitive against rivals like Google Pixel.
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AI-assisted imaging is revolutionizing MRI, CT, PET, ultrasound, and digital pathology. By enhancing detection, reducing workload, and integrating multimodal insights, AI empowers clinicians to diagnose earlier and more accurately while preserving human expertise.
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Microsoft is reimagining Windows as an AI-powered agent that understands intent, automates workflows, and proactively enhances security. This shift promises productivity gains and inclusivity but raises critical questions about privacy, over-automation, and user trust.
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OpenAI’s GPT-5 marks a major leap in healthcare AI, with benchmark gains, improved factuality, and free access. Drawing lessons from Penda Health’s Kenyan trial, this article explores global and Indian implications, opportunities, challenges, and the need for responsible clinical integration.
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A 2025 meta-analysis reveals that AI-based machine listening using smartphone audio recordings achieves high diagnostic accuracy for obstructive sleep apnea (OSA), offering a scalable, cost-effective alternative to traditional sleep studies.
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AI and machine learning promise smarter healthcare—but the “black box” problem endangers trust and safety. This article explores the IBM Watson for Oncology failure and offers actionable, transparent solutions to build responsible, explainable AI for medicine.
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