ADVANCING ARTIFICIAL INTELLIGENCE

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.

Patient Safety and Artificial Intelligence

Opportunities and Challenges for Care Delivery





Content provided by:

Lucian Leape Institute, an initiative of the Institute for Healthcare Improvement, guiding the global patient safety community.

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AI: Promise or Peril for Patient Safety

Beth Daley Ullem, MBA, Martin J. Hatlie, JD, and Olivia Lounsbury


Patient Concerns: 4 Priorities

  • Data Integrity and Bias
  • Efficacy
  • Payment
  • Transparency and Shared Improvement
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National Academy Of Medicine
Digital Health & AI Action Collaborative



As one of four action collaboratives under the National Academy of Medicine’s Leadership Consortium, the Digital Health and AI Action Collaborative (DHAC) works to advance the digital infrastructure necessary for continuous improvement and innovation in health, health care, and evidence development.


A seamless digital infrastructure for the protected capture, sharing, analysis, and use of health data and information is required for health system effectiveness, efficiency, equity and continuous learning and improvement. 



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CoDEx presents

"AI and Me: How Technology Led to the Right Diagnosis"


While much of the AI conversation focuses on how clinicians use technology to improve diagnoses, what happens when patients and caregivers turn to AI for answers themselves? This insightful webinar explores the untapped potential of AI and LLMs as tools for patients seeking second opinions, driving earlier and more accurate diagnoses. Featuring patient safety leaders Susan E. Sheridan, MIM, MBA, DHL, alongside healthcare entrepreneur Courtney Morales Hofmann, MBA, this discussion will highlight real-life stories of AI’s impact, the evolving role of patients in AI-driven healthcare, and key considerations for safety, governance, and co-development. Don’t miss this timely conversation on the future of diagnosis and patient empowerment.

Event Recording