Latest news with #JohnSnowLabs
Forbes
5 days ago
- Health
- Forbes
How Regulatory-Grade Oncology AI Is Transforming Cancer Care
David Talby, PhD, MBA, CTO at John Snow Labs. Solving real-world problems in healthcare, life sciences and related fields with AI and NLP. For decades, the oncology field has faced an unfortunate truth: Extracting high-quality, structured information from clinical charts is a tedious, labor-intensive and largely manual task. Even as AI models have advanced, their outputs remain incomplete without human intervention. And what many people don't know is that behind every patient is a cancer registry specialist (CRS) spending hours reading through charts, identifying events, interpreting dates and ensuring accuracy for each case. But as we approach regulatory-grade accuracy—a level of performance long considered the exclusive domain of highly trained human experts—that's all about to change. In the world of cancer data extraction, this means AI is hitting a consistent threshold of 95% accuracy. That figure isn't arbitrary; it's the benchmark achieved by experienced teams working meticulously, often with multiple levels of quality control. Thanks to the combined power of healthcare-specific natural language processing (NLP) and large language models (LLMs), and a careful approach to model selection and orchestration, we're crossing that threshold in some of the most critical areas of oncology information, including tumor staging, grading and beyond. Here's why it matters. Hidden Complexities Of Oncology Data To appreciate the significance of this leap, it's important to understand the scale and complexity of the problem. A single cancer diagnosis involves hundreds of discrete data points: dates of imaging, biopsies, surgeries, therapies, pathology reviews and more. There are often dozens of potential diagnosis dates, and a specific rule determines which one is considered official for registry purposes. Even determining the primary cancer site or tumor grade can involve navigating contradictory information scattered across different documents. Currently, filling out a registry case takes a herculean amount of time and effort. Registrars estimated taking approximately one hour and 15 minutes to complete an abstract for a simpler case and about two and a half hours to complete an abstract for a more complex case. This is done once a year for each patient, and with growing backlogs, data is often outdated by the time it's available for clinical decisions or research. The delay isn't just inconvenient. It's a barrier to real-time care optimization and scientific discovery. Over the years, AI models have grown steadily more accurate. Best-in-class systems could extract relevant information from charts, but not reliably enough to replace human interpretation. They were assistive tools that were helpful, but not trustworthy enough to operate independently in regulatory contexts. Why General-Purpose LLMs Fall Short Now, with AI systems achieving 95%-plus accuracy on key fields without manual oversight, AI can replicate, and, in some cases, outperform, the gold standard achieved by expert cancer registrars. But not all models are created equally. These AI-driven tools are built specifically to tackle the unique challenges of healthcare, and oncology in particular. Rather than relying on general-purpose AI like GPT-4, which often struggles with domain-specific details, these models are trained on medical texts and structured to understand the nuances of clinical language. It's tempting to believe that large, general AI models can solve these problems with simple prompts like, "Extract cancer diagnosis and treatment." But in practice, they fall short. Too often, they miss subtle distinctions, hallucinate relationships between entities or misinterpret clinical negations. While useful as a starting point, they lack the precision, stability and regulatory readiness needed for real-world healthcare applications. The Power Of Medical Language Models Healthcare-specific language models aren't just a tech upgrade; they're a foundation for the next generation of cancer care. What was once buried in notes and PDFs is now accessible, providing real, actionable insights. What this looks like in practice is automated case findings, real-time reporting and monitoring integrated into existing clinical workflows. Achieving higher accuracy in entity recognition, better handling of negation and superior ontology mapping, domain-specific models produce results that are reproducible and explainable, which are key for auditability and trust. Here are several ways regulatory-grade oncology AI is being applied: • Tumor Registry Automation: Cancer centers are required to maintain registries of patients, including data on diagnosis, staging and treatment. Oncology models can scan pathology reports, read and decode them automatically, drastically reducing the need for manual chart review. • Clinical Trial Matching: Finding eligible patients for a trial targeting a very specific cancer can be like finding a needle in a haystack. AI models can sift through thousands of records, pulling out the relevant biomarker and tumor type to flag potential candidates in near real time. • Quality Monitoring: AI can flag when recommended treatments are missing. For example, if a patient doesn't have a recorded therapy plan, the system can alert the quality improvement team to investigate further. • Adverse Event Tracking: Side effects can be buried in progress notes. AI can extract and monitor such events over time, alerting clinicians when recurring toxicities could signal a need to adjust therapy. • Outcomes Research: For research teams comparing outcomes, AI tools can provide the structured data needed to stratify patients and link treatment patterns to survival trends. Despite the obvious benefits, AI isn't a fix-all for oncology tracking. In rare cancers, evolving treatment protocols or atypical patient presentations, human registrars can be better equipped to contextualize and accurately code information that lacks precedent in training data. Regulatory compliance, ethical considerations and quality assurance also demand expert oversight, ensuring data integrity and alignment with evolving standards. So, for now, human expertise remains vital to the accuracy and reliability of cancer registries. The role will just evolve with the technology. With regulatory-grade AI for oncology, structured cancer data will become as current and accessible as the clinical notes they come from. Instead of data entry, registrars can shift their focus to more meaningful work, like quality assurance. In turn, patients will benefit from faster research and more responsive care. We're nearing the point at which AI is no longer just supporting our work—it's starting to do the work itself, and do it at a level healthcare professionals can rely on. It's just going to take time. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Associated Press
27-05-2025
- Business
- Associated Press
John Snow Labs Acquires WiseCube to Refine and Safeguard Medical AI Models with Knowledge Graphs
LEWES, Del., May 27, 2025 (GLOBE NEWSWIRE) -- John Snow Labs, the AI for healthcare company, today announced the acquisition of WiseCube, a pioneer in biomedical knowledge graphs and AI-powered literature analysis. The acquisition strengthens the company's mission to deliver responsible, accurate, and explainable healthcare AI solutions enhanced by WiseCube's billion-scale knowledge platform. WiseCube unifies and analyzes disjointed biomedical datasets to provide fast, literature-backed answers to complex medical questions. Its integration of cutting-edge biomedical ontologies and documents ensures access to the most current and comprehensive medical knowledge. This capability has proven indispensable, uncovering new use cases and solutions John Snow Labs can support, such as drug discovery and precision medicine within the Medical Chatbot Platform. The WiseCube acquisition will enable John Snow Labs to: 'With John Snow Labs' leadership in healthcare AI, our combined teams can now bring safe and effective AI solutions to the market at scale,' said Vishnu Vettrivel, CEO, WiseCube. 'We look forward to improving research productivity, clinical decision-making, and patient outcomes together.' 'The integration of WiseCube's knowledge graph technology into our healthcare AI solutions enables a new level of accuracy and reliability for our customers,' said David Talby, CEO, John Snow Labs. 'We're excited to accelerate the ability to deliver real-world, production-ready solutions that clinicians and researchers can trust.' About John Snow Labs John Snow Labs, the AI for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations put AI to good use. Developer of Medical LLMs, Healthcare NLP, Spark NLP, the Generative AI Lab No-Code Platform, and the Medical Chatbot, John Snow Labs' award-winning medical AI software powers the world's leading pharmaceuticals, academic medical centers, and health technology companies. Creator and host of The NLP Summit, the company is committed to further educating and advancing the global AI community. Contact Gina Devine Head of Communications John Snow Labs [email protected]
Associated Press
20-03-2025
- Health
- Associated Press
John Snow Labs Introduces First Commercially Available Medical Reasoning LLM at NVIDIA GTC
LEWES, Del., March 20, 2025 (GLOBE NEWSWIRE) -- John Snow Labs, the AI for healthcare company, today announced Medical LLM Reasoner, the first commercially available healthcare-specific reasoning large language model (LLM) to date. Rather than simple knowledge recall with traditional LLMs to mimic reasoning [ 1, 2 ], these models represent a significant advancement in AI-driven medical problem solving with systems that can meaningfully assist healthcare professionals in complex diagnostic, operational, and planning decisions. The model was trained using a recipe inspired by that of deepseek-r1 [ 3 ], introducing self-reflection capabilities through reinforcement learning. Developed with NVIDIA tools, the company is releasing the Medical LLM Reasoner at the NVIDIA GTC 2025 Conference. Clinical reasoning is central to healthcare, encompassing the cognitive processes physicians use to evaluate patients, consider evidence, and make decisions. John Snow Labs' medical reasoning models are designed to emulate three types of common reasoning patterns in clinical practice [ 4 ]: Deductive reasoning - such as systematically applying clinical guidelines, protocols, and established medical knowledge to specific patient scenarios Inductive reasoning - such as identifying patterns across individual patient cases and generating hypotheses about underlying causes or connections Abductive reasoning - making the most plausible inference with limited information, as happens when making time-sensitive decisions about a patient These models benefit from a reasoning-optimized training dataset, a hybrid training methodology, medical decision tree integration, and self-consistency verification layers. They are designed to elaborate on their thought processes, consider multiple hypotheses, evaluate evidence systematically, and explain conclusions transparently. The Medical LLM Reasoner can track multiple variables, hypotheses, and evidence points simultaneously without losing context. The Medical LLM Reasoner is available in two sizes, 14B and 32B, both with a 32k context window. The 32B model achieves an average score of 82.57% on the OpenMed benchmarks, while the 14B model achieves 80.04% - along with the benefit of verbalizing the chain of thought leading to each answer. These scores outperform the 32B reasoning models by Qwen2.5 (82.02%) and R1 (79.40%). The models also perform well on reasoning benchmarks like Math 500 (81.5% for the 32B model) and BigBench-Hard (64.8% for the 14B model). The Medical Reasoning LLM is designed to run privately inside each customer's infrastructure, without any calls to third-party APIs, simplifying compliance when reasoning over confidential medical information. The training process ran on a cluster of NVIDIA H100 -accelerated servers and makes use of a number of NVIDIA software libraries, including NCCL for efficient multi-GPU communication during distributed training and TensorRT for inference optimization and deployment testing. While existing benchmarks effectively measure medical knowledge, they inadequately assess the sophisticated reasoning capabilities that are essential for clinical practice. To address this gap, John Snow Labs is developing new specialized benchmarks for clinical reasoning, consistency, safety, and uncertainty quantification, furthering its commitment to responsible AI. John Snow Labs, the AI for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations put AI to good use. Developer of Medical LLMS, Healthcare NLP, Spark NLP, Spark NLP, the Generative AI Lab No-Code Platform, and the Medical Chatbot, John Snow Labs' award-winning medical AI software powers the world's leading pharmaceuticals, academic medical centers, and health technology companies. Creator and host of The NLP Summit, the company is committed to further educating and advancing the global AI community. Contact Gina Devine
