AI in Healthcare: How To Assess What Works
At ViVE 2025, healthcare leaders emphasized that AI scribes are proving effective and scalable, especially in reducing physician burnout, while broader clinical AI tools still face challenges around reliability and integration. Speakers urged health systems to focus on long-term value, ease of use, and real-world outcomes over hype when evaluating AI solutions.
Elon Musk asked People to Upload Their Health Data. X Users Obliged
Users on X have begun uploading personal medical images to Grok, an AI chatbot, raising concerns about data privacy and clinical accuracy. Bayesian Health founder Suchi Saria cautioned that without high-quality data and medical expertise, efforts like these risk becoming amateur experiments rather than delivering meaningful, safe health insights.
Artificial intelligence was a hot-button topic at Modern Healthcare’s Digital
At Modern Healthcare’s Digital Health Summit, AI took center stage as leaders debated its promise, risks, and readiness for clinical care. Companies like Bayesian Health continue to stand out by demonstrating real-world impact through clinically validated, scalable AI applications designed to improve outcomes without overwhelming frontline teams.
Cleveland Clinic is gearing up for the integration of artificial intelligence across its operations, with a focus on both clinical and nonclinical applications.
Cleveland Clinic is piloting AI in key areas including sepsis risk prediction, documentation, and coding automation, with a strong focus on ethical oversight and frontline integration. Among its partners is Bayesian Health, whose clinically validated platform supports early identification of high-risk patients, aligning with the system’s long-term strategy for AI-driven care optimization.
AI Model Improves Early Detection of Hospital-Acquired Pressure Injuries
A collaborative study by USC, Johns Hopkins, and University Hospitals developed an AI model that significantly outperforms traditional tools in predicting hospital-acquired pressure injuries. Bayesian Health, led by Dr. Suchi Saria, contributed to the work, showcasing how its platform improves early detection, reduces nursing burden, and advances health equity through unbiased, data-driven risk assessment.
2023 was a transformative year for Bayesian Health and clinical AI. Following significant achievements like FDA recognition and major EMR integrations, Bayesian established itself as a leader in healthcare technology.
Delve into our Year in Review page to uncover the pivotal moments and strategic partnerships that thrust Bayesian into the industry forefront, shaping the trajectory of clinical AI. Join us to explore how these elements coalesced to make 2023 a defining year for Bayesian in the realm of healthcare technology.
In a recent Washington Post article, Leana S. Wen highlights the transformative impact of AI tools in healthcare, particularly in improving patient outcomes. The piece showcases Kaiser Permanente’s success in deploying an AI tool to identify early signs of patient deterioration in hospitals, a task traditionally reliant on clinician vigilance.
This AI system integrates various data points like vital signs and lab tests to alert healthcare teams proactively, helping in the early intervention of potential health crises. The results speak for themselves: a significant reduction in mortality rates. The article also notes the effective use of AI in other hospitals, such as Johns Hopkins, for early sepsis detection.
While Bayesian’s product is referenced in the discussion, the focus remains on the broader benefits and potentials of AI in healthcare.
Read the Washington Post article
Read more about Bayesian Health’s approach to Adaptive AI
Healthcare, like most other industries, has been experimenting with the use of large language models (LLMs) over the past year to streamline things like patient communication, clinical documentation and prior authorization.
While there has certainly been a great deal of hype recently surrounding LLMs and other forms of generative AI, there has also been a lot of skepticism. When it comes to an industry like healthcare, where one small technology failure could be the difference between life and death, many stakeholders are concerned about how to mitigate the risks associated with novel LLMs entering the field.
Read the Med City News article
Read more about Bayesian Health’s approach to Adaptive AI
As a pioneer in the realm of clinical AI, Bayesian Health is both invigorated and deeply aligned with a recent multi-phased initiative spearheaded by the National Academy of Medicine (NAM) Leadership Consortium. With Dr Suchi Saria (Bayesian Health’s CEO) serving as a founding member of the steering committee, this ambitious project aims to establish an Artificial Intelligence Code of Conduct (AICC) for the healthcare sector. Far more than a mere initiative, the AICC serves as a guiding framework that resonates with Bayesian’s unwavering commitment to efficacy and responsible AI use. This endeavor underscores the pivotal role that Bayesian approaches are playing in setting the standard for accurate, safe, reliable, and ethical AI applications in healthcare.
The National Academy of Medicine (NAM) Leadership Consortium stands as a trusted platform and cornerstone for interdisciplinary dialogue, uniting national leaders across diverse sectors. With a focus on aligning science, informatics, incentives, and culture, the Consortium is committed to fostering a healthcare system that is both innovative and continuously learning. It serves as a linchpin in streamlining the various components essential for improving healthcare outcomes for all, truly embodying the ideal of crafting a healthcare system that benefits everyone.
With Dr Suchi Saria helping to lead the steering committee, the Artificial Intelligence Code of Conduct (AICC) stands as a timely and imperative answer to the rapid expansion of AI technologies in healthcare. Addressing the pressing need for a cohesive set of best practices and ethical guidelines, the AICC’s two-fold vision aims to harmonize existing ethical principles while pinpointing gaps. In doing so, it seeks to establish a universally respected Code of Conduct that not only serves as a starting point but also as a yardstick for ongoing refinement, testing, validation, and improvement in the ever-evolving landscape of healthcare AI.
The project aims to catalyze collective action to harness the potential of AI in transforming healthcare delivery and advancing health research, all while upholding high standards of ethics, equity, privacy, and security.
Scheduled to last for three years, the AICC project has a roadmap that includes steering committee meetings, webinars, and final hybrid discussions. The steering committee, composed of thought leaders in multiple fields, will guide the project to ensure it meets its intended aims and earns broad stakeholder support.
Among its key deliverables, AICC plans to offer a harmonized Code of Conduct, a comprehensive landscape assessment, a systematic review of guidelines, and a final special publication outlining the Code of Conduct framework for deployment in the healthcare system.
While primarily based on U.S. experience, the initiative intends to be informed by international efforts, making it globally relevant.
NAM is also focused on aligning its work with other initiatives in the field, reinforcing and being informed by other projects to ensure the broad adoption of the Code.
For Bayesian Health, the AICC initiative serves as a vital framework that aligns seamlessly with our core values and ambitions, particularly under the strategic guidance of Dr Suchi Saria on the steering committee. Deeply invested in ethical and effective clinical AI, we see the AICC as an essential guide to fine-tuning our practices in concert with broader industry standards.
With backing from various foundations and a commitment to include a diverse set of stakeholders, the project promises to have profound implications for the future of healthcare. Stay engaged with us as we actively participate in this pivotal initiative, shaping and anticipating the transformative impact it will have on making AI a reliable and ethical force in healthcare.
September is Sepsis Awareness Month, a critical period for shedding light on a condition that remains the leading cause of hospital deaths in the United States. Initiated by the Sepsis Alliance in 2011, this annual awareness campaign brings together individuals, healthcare professionals across various disciplines, and organizations large and small to amplify the urgency of understanding and combating sepsis.
Sepsis is a severe and often fatal response to infection, affecting a staggering 1.7 million people and causing the death of 350,000 adults in the U.S. each year. What’s more alarming is that research indicates up to 80% of sepsis-related deaths could potentially be prevented with swift diagnosis and treatment. For every hour that treatment is delayed, the risk of mortality skyrockets by approximately 8%. These numbers underline the imperative nature of education and awareness about sepsis.
Early detection and timely intervention are not just statistical factors; they lie at the very heart of Bayesian Health’s mission. Our commitment to driving efficacy in clinical AI directly aligns with the pressing need for rapid evaluation and treatment of sepsis. By developing tools that empower healthcare providers to diagnose sepsis early, we’re not just innovating; we’re striving to save lives.
Our team at Bayesian Health advocates for effective and immediate action to raise sepsis awareness. We strongly recommend taking advantage of the following resources to escalate public understanding of sepsis in your community:
Awareness is the first step towards a significant impact. This Sepsis Awareness Month, let’s unite to disseminate knowledge, drive timely interventions, and ultimately, save lives.
Spread the word, because when it comes to sepsis, every second counts.
