The use of AI in healthcare settings carries risk, but that shouldn't stop organizations from moving forward. Healthcare leaders need to understand, acknowledge, and take an active role in mitigating these risks to ensure responsible use of AI: 

• Data bias: Biased training data will inevitably lead to a biased AI model and disparate outcomes. 
• Data accuracy and reliability: AI can be inaccurate or unreliable due to limited data sets, bias, and potential hallucinations. 
• Data privacy: While data sharing increases concerns around the sharing of patient health information and organizational IP.  
• Transparency: Many AI models lack transparency as the decision-making happens outside of the purview of the user. 
• Liability: 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. See below for guidance around creating AI-specific processes and teachings from successful implementations. 

• 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 does not need an AI strategy. Instead, consider how AI can support and evolve your existing strategy. From there, create a strategy around adoption and governance to ensure successful investment in and management of AI tools. See three examples of major challenges and potential AI solutions:

• Facing a workforce shortage challenge? Consider AI tools that could support your existing workforce in meeting their increased demands.
• Unsure how to meet patient or member expectations around experience and engagement? AI solutions could transform how your organization interacts with them.
• While it may seem impossible to provide the right clinical guidance at the point of care, AI solutions may exist to 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 organizations’ AI efforts:

• Proactively address your workforce’s AI fears 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:

• Radiology has been on the forefront of AI in healthcare for years. See how AI tools have impacted radiology so far, and what it could mean for the future of medical imaging.
• While many AI use cases focus on the physician, Advisory Board believes that there is a real, untapped opportunity for AI and automation to alleviate the burden on nurses.
• 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.