CERRITOS, Calif., April 22, 2025 (GLOBE NEWSWIRE) -- The Oncology Institute, Inc. (NASDAQ: TOI) ("TOI" or the "Company"), a pioneer in value-based community oncology care, is proud to announce an enterprise-wide expansion of its strategic partnership with Helios Clinical Research, a nationally recognized research site network. This initiative builds upon a successful collaboration in Florida and reflects TOI's ongoing commitment to integrating clinical research into community oncology settings. "Expanding o ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The clinical research arena is experiencing transformative advancements due to the successful application of Generative AI (GenAI) tools, enhancing efficiency and quality in clinical trials [4][6] - Regulatory agencies, including the FDA, are beginning to establish guidelines for the responsible use of AI in clinical studies [6] - The report emphasizes a multi-pronged approach to safeguard efficiency and quality in clinical trials through various AI methodologies [19] Overview - The report highlights the increasing anticipation among industry professionals regarding the potential of AI to improve clinical trials and healthcare [4] - There are numerous opportunities to leverage AI technologies across the clinical trial ecosystem, including design, patient engagement, and regulatory submissions [5] AI Methodologies - Distinction is made between Generative AI, which generates responses based on training data, and agentic AI, which independently handles complex problems [10][11] - A holistic approach is necessary for developing AI frameworks, emphasizing the importance of training, ethical considerations, and human oversight [12][13] Safeguards for AI in Clinical Trials - Five critical categories of safeguards are identified to ensure the safe and efficient use of AI in clinical studies: curating and containerizing data, integrating "human-in-the-loop," harmonization of response, objectivity, and recognizing uncertainty [19][20] - Curating training data is essential to avoid poor-quality responses and ensure reliability in clinical operations [24][25] - The integration of human oversight is crucial to optimize quality and prevent erroneous outputs from AI systems [26][29] Use Cases of AI in Clinical Trials - The report discusses successful applications of AI, including a scientific Q&A chatbot used in a Phase III trial, which improved the efficiency of protocol clarifications and reduced the burden on medical monitors [39][40] - The chatbot's success was attributed to rigorous training, harmonized responses, and the ability to recognize knowledge gaps [41][42]