ConcertAI’s TeraRecon, Integrates Clinical and Generative AI, Supporting Clinical Research and Advancing Clinical Care Solutions

New solutions empower clinicians and research teams with AI orchestration, model development, visualization, and multi-modal data management to power clinical research, clinical trials, and patient care strategies.

ConcertAI’s TeraRecon, the advanced visualization and clinical AI leader, recently unveiled first-in-category capabilities in advanced visualization, AI, and expansions to its AI partner ecosystem to enhance research capabilities and support complex clinical study workflows. These solutions will provide researchers with enhanced data analysis tools for in-depth study, contributing to more informed care strategies that can improve patient outcomes.

At this year’s RSNA 2023, ConcertAI, and its TeraRecon clinical imaging solutions group, are formally launching a multi-modal AI platform aligning clinical, radiological, whole slide images/digital pathology, along with real-world patient data in support of AI model development for clinical research, clinical trials, and deployment of AI models for prospective clinical validation.

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As part of this announcement, ConcertAI is engaging in a multi-year strategic agreement with Memorial Sloan Kettering Cancer Center (MSKCC) focused on advancing Clinical AI in oncology, with an emphasis on enhancing clinical trials and improving oncology decision-making. Leveraging ConcertAI’s Eureka Life Science AI Platform, powered by TeraRecon, MSKCC will drive advancements in multi-modal AI workflows, using clinical and radiological imaging data. This collaboration aims to use advanced data analysis and AI methods to predict specific tumor mutations and cancer treatment response, contributing to the broader field of oncology research. Our joint vision is clear: advance the utility and efficacy of Clinical AI in oncology research, extending its benefits to more patients and institutions in the future. Read the recent press release.

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“Over the past two years, we have focused on the importance of multi-modal data AI model development, validation, stability monitoring, and deployment for insights, as part of clinical trials, and ultimately, clinical care,” said Jeff Elton, PhD CEO of ConcertAI. “Multi-modal data enable causal inferences and elimination of confounders for interpretations and predictions. Enabling this required rethinking the tools available to biomedical researchers, while leveraging the power of Generative AI. As a ground-up new approach that allows multi-modal data management, AI model development validation, model management, and model release we can bring the power of deep and wide data at scale, our large research network, and latest predictive AI and Gen AI together for the broadest research community use. Having MSKCC as a multi-year strategic collaborator is an additional assurance of meeting our commitments to patients, clinical providers, and biomedical innovators at pace and the highest possible standards of outcomes.”

ConcertAI’s TeraRecon will also be showcasing new and expanded collaborations within the Eureka Clinical AI Partner Program, including: Avicenna.AI, Bot Image, Cercare Medical, Coreline Soft, GLEAMER, Infervision, Lucida Medical, Optellum, Riverain, and Us2.ai, adding to its large catalog of AI models, offering solutions that enable clinicians to efficiently interact with AI findings, in one streamlined place by integrating seamlessly to PACS or Advanced Visualization solutions.

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ConcertAI’s TeraRecon is also announcing the launch of Pulmonology Suite, a collection of the leading, vendor-neutral, Pulmonology AI algorithms that aid in the analysis and interpretation of Chest CT scans to assess, monitor, and provide insights to support clinicians in treatment decisions for pulmonary conditions. Possible applications of Pulmonology Suite are to aid physicians including pneumothorax, lung nodules, and incidental pulmonary embolism. Pulmonology Suite complements Neurology Suite and Cardiology Suite, released earlier this year.

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