ADVANCING ARTIFICIAL INTELLIGENCE

Sue Sheridan named co-chair of the National Academy of Medicine’s

Patient Safety in the Era of AI Initiative

FOR IMMEDIATE RELEASE


Atlanta, GA – Patients for Patient Safety US (PFPS US) is pleased to announce that Sue Sheridan, President and CEO, has been named co-chair of the National Academy of Medicine’s Patient Safety in the Era of AI initiative, alongside Gianrico Farrugia, CEO of Mayo Clinic, and Wright Lassiter, CEO of CommonSpirit Health.


The specific focus of this two-year initiative, launching in March 2026, will be on using AI to improve patient safety. It will capitalize on AI as an enabler of breakthrough improvement in detecting, predicting, and preventing patient harm, grounded in the principles and safeguards set forth in NAM’s Artificial Intelligence Code of Conduct for Health and Medicine. Its work will culminate in a national strategy for AI-enabled patient Safety with an emphasis on patient-centeredness, exploring potential topics such as patient-directed AI to improve outcomes, as well as AI-powered patient-generated data that capture patient and diagnostic safety events and experiences more effectively.


“I am thrilled to be part of such a transformational initiative. Too many patients and families are left with painful ‘what ifs’ after harm occurs from unsafe care,” said Sheridan. “The NAM initiative has the potential to leverage AI to prevent harm before it happens, empower patients and families, and optimize learning from our lived experiences to ensure those ‘what ifs’ never occur to others”.


“Patient safety cannot be meaningfully advanced without patients and families as full partners in the work”, said Laura Adams, Senior Advisor at the NAM and the initiative lead. “Sue Sheridan and Patients for Patient Safety US bring an essential moral compass to this effort, ensuring that patient experience is a driving force in shaping AI-enabled safety strategies that are trustworthy, humane, and effective”.


PFPS US will be uniquely positioned to contribute real-world examples of how patients are using AI to improve their care and outcomes and serve as a critical sounding board for ideas and innovations that emerge throughout the NAM initiative.

AI Revolutionizes Healthcare

From Faster Diagnoses to Personalized Treatment

Artificial intelligence is rapidly transforming healthcare by enabling faster, more accurate diagnoses, personalizing treatment plans, and streamlining both clinical and administrative processes. AI systems can analyze vast amounts of medical data-including imaging, genetic information, and electronic health records, far beyond human capacity, helping clinicians detect diseases earlier and with greater precision. In practice, AI powers everything from robot-assisted surgeries and virtual health assistants to predictive analytics that identify at-risk patients before symptoms arise. These technologies not only improve patient outcomes but also reduce costs and administrative burdens, allowing healthcare providers to focus more on patient care. As AI continues to evolve, its integration promises a future where healthcare is more proactive, efficient, and tailored to individual needs.

Patient use of AI

  • AI algorithms analyze medical images such as X-rays, MRIs, CT scans, and ultrasounds to detect abnormalities, tumors, or other signs of disease. These tools can highlight areas of concern for radiologists and sometimes autonomously identify conditions like lung nodules, fractures, or signs of cancer, improving both speed and accuracy of diagnosis.
  • For example, tools like Zebra Medical Vision and Arterys Cardio AI rapidly interpret imaging data, enabling earlier detection of diseases and supporting clinicians in making more informed decisions.

  • AI systems integrate and analyze diverse patient data-including medical images, vital signs, electronic health records, lab results, and demographic information-to provide a comprehensive diagnostic assessment. This holistic approach can uncover patterns and correlations that might be missed by human clinicians, reducing misdiagnosis and improving accuracy.
  • By combining various data sources, AI helps create a fuller picture of a patient’s health, supporting more precise and personalized diagnoses.

  • AI enables earlier detection of diseases by identifying subtle patterns in data that may not be apparent to clinicians. For example, IDx-DR autonomously screens for diabetic retinopathy by analyzing retinal images, even in settings lacking specialized eye care professionals.
  • AI’s ability to process large volumes of data quickly is especially valuable for screening programs, catching diseases in early, more treatable stages.

  • Patients and clinicians can use AI-powered decision support tools (such as DxGPT) to input symptoms and medical history, receiving suggestions for possible diagnoses. These tools help guide further testing and streamline the diagnostic process.
  • AI reduces variability in diagnosis by providing consistent, data-driven insights, supporting clinicians in complex decision-making and minimizing human error.

  • AI-driven diagnostics are democratizing healthcare by making advanced diagnostic capabilities available in regions with limited access to specialists. Cloud-based AI tools can analyze uploaded images or data remotely, providing diagnostic support even in underserved areas.
  • AI models are being developed to recognize diseases across diverse populations, helping to address disparities in diagnostic accuracy.

  • AI automates routine diagnostic tasks, such as preliminary image analysis or data sorting, freeing clinicians to focus on complex cases. This increases efficiency, reduces wait times, and can lower healthcare costs.