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LLMs are enabling computers to understand and generate human-like text, making them indispensable in industries ranging from customer service to content creation. The global market for LLMs is expected to skyrocket from $1.59 billion%20market%20size%20in%20terms%20of,79.80%25%20during%202024%2D2030.) in 2023 to $259.8 billion by 2030, with North America alone projected to hit $105.545 million by 2030. The dominance of the top five LLM developers, who currently hold 88.22% of the market revenue,

Ever wonder why so many machine learning (ML) models never see the light of day? Despite their huge potential, only 32% of data scientists say their models usually get deployed. Even more shocking, 43% report that 80% or more of their models never make it into production. This means many businesses miss out on the full value of their AI projects. Imagine a retail company spending months developing a sophisticated customer recommendation system, only for it to never be implemented — losing out on

Healthcare providers manage an enormous volume of data daily, approximately 137 terabytes, most of which is unstructured. This includes a wide array of formats such as medical images, clinical notes, and genetic test results. Unstructured data processing, crucial for patient care, poses significant challenges due to its complexity and the varied sizes of its components. The volume of healthcare data is rapidly increasing, fueled by the widespread adoption of electronic health records and advance

This April, Polina Talapova and Mariia Pahur represented SciForce at the 7th European OHDSI Symposium in Rotterdam – three vivid days of workshops, poster sessions, MindMeetsMachines mapping competition and an oral presentation aboard the SS Rotterdam, a retired ocean liner moored on the Maas river. The symposium's theme was Continuous Collaboration for Living Evidence Generation. The word "living" matters here. Traditional evidence-generation projects are often designed as discrete studies. A

The COVID-19 pandemic changed how healthcare works. When in-person visits dropped, telehealth, remote monitoring, and home care quickly became necessary, and many of these solutions are now here to stay. Virtual hospitals and AI copilots are leading this shift. Virtual hospitals use video calls, remote monitoring, and mobile care teams to deliver hospital-level care at home. AI copilots support clinicians by drafting, summarizing, coding, and prioritizing information, while clinical decisions re

When speech recognition gets things wrong, the consequences show up in customer frustration, extra manual work, compliance issues, and lost revenue. Accuracy determines whether voice automation actually reduces effort, or quietly creates more of it. In practice, the accuracy seen in demos rarely matches production results. Studies show speech systems can perform 2.8–5.7× worse once deployed. A model that achieves about 8.7% word error rate (WER) in clean medical dictation has recorded over 50% W

When nearly 70% of visitors go straight to your search bar, you can’t afford for it to fall short. Yet most on-site search tools still rely on outdated keyword matching – returning irrelevant results or, worse, none at all. That’s why 80% of users abandon a site when the search doesn’t deliver. Meanwhile, companies using smarter search are seeing real gains. Amazon’s conversion rate jumps from 2% to 12% when users use search. The reason: newer AI tools powered by large language models (LLMs) und

Voice is becoming the new interface of work. From warehouses to call centers, AI-powered assistants are showing up everywhere: faster, hands-free, always on. The scale is staggering: the Voice AI Agents Market is forecasted to grow from $2.4 billion to $47.5 billion by 2034, and the approximate number of voice assistants in use globally is now over 8 billion – more than people on the planet. But recognizing speech isn’t enough anymore. In high-stakes environments like healthcare, voice AI ne