Qualified Health, in collaboration with Anthropic, has initiated a significant deployment of AI across the University of Texas System (UT System) to improve patient care delivery. This innovative AI system utilizes Anthropic's Claude AI models to analyze a vast array of clinical data across Texas, identifying patients who meet evidence-based care criteria but may have been overlooked.

The AI solution continuously evaluates clinical datasets to pinpoint gaps in guideline-recommended care, ensuring patients are flagged for further clinical review and coordinated care planning. This addresses a critical issue in healthcare where millions, particularly in Texas, do not receive timely evaluations, leading to preventable complications and exacerbating healthcare inequities.

Eric Kauderer-Abrams of Anthropic emphasizes the complexity of healthcare data, stating that Claude reliably navigates this terrain, while Qualified Health's platform enhances governance, making AI deployment safe and effective. The system integrates various clinical data, facilitating a population-level understanding of care gaps, thereby improving decision-making for clinicians.

The initial deployment at the University of Texas Medical Branch focuses on cardiology, utilizing guideline-directed therapies and interventional treatments. Early results have been promising, revealing previously unrecognized patient cohorts for clinician review and accelerating care pathways for eligible patients.

Building on these successes, the platform is set to expand systemwide, with plans for additional specializations by 2026. This initiative supports underserved populations by enhancing access to proven care. The UT REAL Health AI initiative embodies a commitment to improving healthcare outcomes across Texas through responsible AI integration, ultimately aiming to reduce care costs and streamline patient experiences.

AI Magazine has explored the Top 10 AI Platforms in Healthcare, highlighting how these platforms are revolutionizing the industry by delivering faster insights from complex clinical data. By integrating artificial intelligence into daily workflows, they enhance clinical decision-making, alleviate pressure on overwhelmed systems, and allow healthcare providers to allocate more time to patient care.

As healthcare costs escalate and patient expectations rise, AI platforms are crucial in developing a more resilient, predictive, and personalized healthcare model. The following platforms exemplify significant applications and benefits in the sector:

Butterfly Network applies AI in portable ultrasound technology, enhancing image optimization and clinical decision support, thereby expanding access to diagnostics in various healthcare settings. Caption Care provides AI-guided ultrasound, enabling less experienced clinicians to produce quality diagnostic images, facilitating improved care access.

PathAI focuses on digital pathology to boost diagnostic accuracy and workflow efficiency, significantly impacting oncology diagnostics. Merative delivers healthcare data and analytics solutions, aiding population health and outcomes research.

Truveta uses de-identified clinical data to drive insights for population health and therapy development. Tempus leverages AI in precision medicine, especially in oncology, to support personalized treatment and clinical trial matching.

Aidoc enhances medical imaging workflows by deploying multiple AI algorithms to improve clinical collaboration. Google Cloud Healthcare and AWS HealthLake provide robust platforms for data interoperability, analytics, and machine learning, underpinning many healthcare AI applications.

Lastly, Microsoft Dragon Copilot stands out by automating workflows and reducing administrative burdens, thus improving overall care delivery. Together, these platforms illustrate how AI is fundamentally transforming healthcare, making it more efficient and patient-centric.

Amazon One Medical has introduced its innovative Health AI assistant in the One Medical app, significantly enhancing how patients engage with their healthcare. This AI-driven feature simplifies health management by providing personalized assistance in areas such as answering health queries, booking appointments, and managing medications.

Developed in collaboration with One Medical’s clinical leadership, the Health AI assistant offers 24/7 health guidance tailored to individual medical histories. If clinical insights are required, it can effortlessly connect patients to their care teams through messaging or by scheduling same- or next-day appointments.

Unique to the Health AI assistant is its ability to glean insights from a patient's comprehensive medical records without the need for manual data uploads, ensuring a high level of privacy protection compliant with HIPAA regulations. It customizes responses by taking into account past health concerns, lab results, vaccinations, and current medications. Key applications include:

• Answering both general and complex health questions in a personalized context.
• Offering around-the-clock guidance for symptoms, conditions, and wellness queries.
• Assisting in the selection of care options tailored to individual needs.
• Facilitating appointment scheduling and medication renewals, which can be filled through Amazon Pharmacy.

The intention behind this technology is not to replace but to enhance the patient-provider relationship, enabling patients to better manage their health while ensuring easy access to clinical expertise when necessary. The AI proactively recommends appropriate levels of care and can arrange visits for urgent health concerns promptly.

Amazon Health Services emphasizes maintaining rigorous security standards to protect patient data. Those who prefer not to utilize the AI feature can still access traditional One Medical app services. The Health AI assistant, now fully operational, reflects a commitment to improving healthcare experiences by merging advanced technology with patient-centric care.

Researchers at the University of Missouri are advancing the use of artificial intelligence (AI) to detect melanoma, the most dangerous form of skin cancer, through the analysis of images of suspicious skin abnormalities. Rather than substituting for medical expertise, this decision-support tool aims to assist dermatologists in quickly identifying cases that need further examination. “AI can help patients with limited access to dermatologists,” stated Kamlendra Singh, the study’s lead researcher. Early detection can significantly improve health outcomes, making this research vital.

To develop this technology, the team is creating highly accurate AI models capable of evaluating images of skin lesions by analyzing various visual patterns, such as size, shape, color, and sharpness of moles. Utilizing a database of 400,000 images captured via advanced 3D total body photography, which provides a high-resolution digital map of the skin, the AI models were rigorously trained and tested.

Singh's research demonstrated that when three existing AI models were combined, their accuracy in distinguishing melanoma from benign conditions improved to over 92%. This improvement illustrates how integrating AI with medical knowledge can enhance diagnostic precision, especially in underserved areas lacking specialized healthcare professionals.

While it may take time before this technology is used clinically, the research serves as a promising proof of concept. As AI continues to be trained on diverse datasets, its predictive capabilities will enhance, fostering greater trust among healthcare providers. Singh attributes the progress to Mizzou’s strong computational infrastructure, which empowers innovative research with real-world applications. His study was published in Biosensors and Bioelectronics: X, highlighting the intersection of AI, precision medicine, and patient-centered care.