The use of AI in healthcare settings carries risk, but that shouldn't stop organizations from moving forward and ensuring the responsible use of AI. 

• Data bias leads to biased AI model and disparate outcomes. Limited data sets, bias, and potential hallucinations can make AI inaccurate and unreliable. 
• Data sharing raises concerns about patient health information and organizational IP. Conversely, a lack of transparency in AI models means decision-making is often hidden from users. 
• With no guidelines on who is liable in the event of AI-caused harm, leaders, patients, and physicians may be hesitant about using AI in healthcare.

Because AI brings with it a set of unique challenges, your organization needs to develop specific processes for evaluation and governance. 

• Understand how health system leaders have changed their attitudes (and plans) around the use of AI. 
• See how Castell identified a key challenge area, identified an AI-powered solution, and implemented a solution across their system.
• Learn how Flagler Hospital used AI and clinical variation reduction to fight pneumonia. 

Your organization doesn't need an AI strategy. Instead, consider how AI can support and evolve your existing strategy, and create an incremental adoption approach to ensure successful investment in and management of AI tools.

• Facing a workforce shortage challenge? Consider technologies that could support your existing workforce in meeting their increased demands.
• Discover how AI solutions can revolutionize patient or member experience and engagement. Gain insights from health system executives on their approach.
• While it may seem impossible to provide the right clinical guidance at the point of care, AI solutions could help your clinical workforce close gaps in care and improve outcomes. 

Successful AI adoption requires active communication, change management, and transparency with a variety of stakeholders. See below for tips on how to communicate your organization's AI efforts:

• Proactively address your workforce’s AI concerns while ensuring responsible use of generative AI tools.
• Learn how AI could shift care team roles, and provide transparency to patients about the use of AI, including any use of patient data.
• Understand how health system leaders are responding to and investing in AI solutions. 
  

AI will transform many service lines and industry sectors. For example:

• See how AI tools have impacted radiology so far, and what it could mean for the future of medical imaging.
• Explore how AI is impacting oncology clinical decision-making and treatment. 
  
• Explore how life sciences organizations are harnessing the power of AI in drug development and testing, and what it will mean for the future of disease treatment.