AI in Clinical Decision Support: Balancing Innovation, Safety, and Trust in Healthcare

The Role of AI in Clinical Decision Support

Large language models (LLMs) have sparked interest in their potential to support clinical decision-making, especially in identifying drug-to-drug interactions. However, a recent retrospective analysis revealed a gap: traditional clinical decision support systems detected 280 clinically relevant interactions, while AI identified only 80. This highlights why healthcare providers remain cautious about integrating AI into their workflows.

A 2024 Healthcare IT Spending study by Bain and KLAS Research found that concerns around regulation, legal issues, cost, and accuracy affect adoption. These concerns are valid, given the critical importance of patient safety. Still, AI is gaining interest among healthcare professionals, who are optimistic about experimenting with generative AI to improve patient outcomes.

The Central Dilemma of AI Integration

The challenge lies in using technology to enhance care without increasing risk. Clinical decision support, particularly for medication safety, illustrates this well. Clinicians face an overwhelming amount of medical literature—PubMed alone has over 30 million citations, growing by about one million annually.

Decision support tools help by continuously monitoring literature, regulatory updates, and clinical guidelines. They curate and synthesize evidence into actionable recommendations at the point of care. Trusted systems rely on expert curation to ensure accuracy. AI can speed up information retrieval and reduce clicks, especially when designed specifically for clinical use.

General AI vs. Purpose-Built AI

LLMs like ChatGPT excel in general language understanding but fall short in clinical decision support when used without customization. Studies show ChatGPT missed important drug-drug interactions or failed to accurately assess severity and onset, highlighting the risks of using general AI instead of purpose-built tools.

Key Considerations for Healthcare Organizations

Healthcare organizations should ask these questions before adopting AI decision support tools:

• Who is this AI designed for? Purpose-built AI targets specific clinical questions and user groups, often outperforming general AI in its niche.
• What data trains this AI? Reliable decision support must cite evidence from peer-reviewed, expert-vetted sources. General AI may draw from unverified internet content, missing crucial details. Frequent updates with the latest research and regulatory info are essential.
• How does this AI interpret my question? Clinical queries can be ambiguous. The AI should clarify how it understands the question and allow users to refine it before providing answers.
• Does this AI offer multiple relevant answers? Clinicians often need several options, such as different intravenous drug combinations. The AI should support informed judgment rather than a single rigid answer.
• Will this AI recognize its limitations? AI should be transparent about what it can and cannot do, avoiding fabricated or misleading responses that could jeopardize patient safety.
• Were clinicians involved in development? Clinician input is critical in creating and validating decision support tools to ensure safety and usability.

A Collaborative Approach for Better Outcomes

Purpose-built AI aims to help clinicians access trustworthy information quickly at the point of care. Combining human expertise with AI assistance offers the best chance to improve clinical decisions and patient outcomes.

For those interested in expanding their knowledge on AI applications in healthcare, Complete AI Training offers tailored courses for healthcare professionals.