AI Risks in Healthcare: Top Threats to Patient Safety and 2025 Challenges

AI in healthcare offers great potential but also significant risks, highlighting the need for transparency, monitoring, and robust governance to ensure patient safety. (Source： Fotor AI)

Overview of AI Risks in Healthcare

A recent report by ECRI, a nonprofit safety and quality research organization, identifies risks associated with artificial intelligence (AI) as the most significant technology hazard facing the healthcare sector in 2025. While AI has the potential to enhance patient care, issues such as biases, inaccurate responses, and performance degradation pose serious risks to patient safety.

Key Findings from the ECRI Report

1. Defining AI Goals:

   
   

   • Healthcare organizations must clearly define their AI objectives to ensure effective implementation. This includes validating and monitoring AI performance continuously.

     
     

2. Transparency from Developers:

   
   

   • Organizations should insist on transparency from AI model developers regarding data sources and operational metrics. Understanding how AI systems function is crucial for mitigating risks.

     
     

3. Potential Bias and Misuse:

   
   

   • AI systems can perpetuate biases present in training data, potentially worsening existing health disparities. Performance issues may arise when models are applied to populations that differ from their training datasets.

     
     

4. Monitoring and Governance:

   
   

   • Establishing a robust governance structure is essential for managing AI risks. Organizations should train staff on the capabilities and limitations of AI tools while continuously monitoring their performance.

     
     

Implications for Healthcare Providers

Healthcare leaders recognize that AI can address critical labour challenges, such as provider burnout and staffing shortages, by streamlining various applications—from triaging imaging results to assisting with patient scheduling. However, the quality of care may be compromised, if there is any careful assessment and risk management.

Regulatory Landscape

The regulatory environment for AI in healthcare remains fragmented, with ongoing efforts from federal agencies to develop comprehensive strategies. Many AI applications, such as those used for clinical documentation, may significantly impact patient care but might not be classified as medical devices by regulatory bodies like the FDA.

Cybersecurity Concerns

In addition to AI-related risks, cybersecurity threats from third-party vendors pose significant challenges for healthcare organizations. Recent incidents have demonstrated the potential ramifications of cyberattacks on patient care, emphasizing the need for thorough vendor risk assessments and incident response plans.

Source: Healthcaredive