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What if businesses could access unlimited, high-quality data without privacy risks or tedious preparation? Synthetic data for AI model training is making this possible, offering a scalable, efficient alternative to real-world data. Gartner predicts synthetic data will surpass real data in AI model training by 2030, with the market growing from $351.2 million in 2023 to USD 2,339.8 million by 2030, at a CAGR of 31.1%. Data preparation is a major hurdle, with data scientists spending over 60% of t

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

When an Air Canada customer asked the airline’s website chatbot about bereavement fares, it told him he could book first and claim the discount within 90 days. The same answer linked to a policy page saying retroactive requests weren’t allowed. The passenger followed the chatbot’s instructions and later had his refund request rejected. The civil tribunal found the airline liable for negligent misrepresentation after concluding that he’d reasonably relied on the inaccurate guidance. The correct i

A health database rarely captures the exact clinical state a researcher wants to study. It contains traces of that state: diagnoses, medication orders, laboratory results, procedures, changes in the level of care, and clinical notes. These records support care, communication, billing, and hospital operations. They were not created for future research questions. Computable phenotyping turns these traces into an algorithm for identifying patients, clinical events, or periods of interest. The proce

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

Building a telehealth platform at clinical scale means solving for hospital network restrictions, HIPAA compliance and auditability, and the data load of continuous remote monitoring – and the architecture decisions that determine whether it holds up are mostly made in the first few sprints. The engineering debt from early decisions starts showing up at scale: video sessions dropping when hospital firewalls, restrictive egress policies, or network address translation prevent a direct media path;