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Aidoc Staff

5 Things You Must Do to Maximize AI’s Impact in Healthcare

Adoption, C-Suite, Care Coordination
Table of Contents

TL;DR In a recent episode of the NerdMD Podcast (above), Aidoc CEO Elad Walach discussed the evolving role of clinical AI, emphasizing its potential to bridge care gaps and improve patient outcomes. Key takeaways include the need for precise AI in clinical care, the importance of breaking down silos in health systems and the growing maturity of clinical AI solutions. Walach also highlighted the necessity of AI platforms for scalability and the critical role of AI in care coordination across diverse specialties.

Health systems are under pressure to perform and exceed previous standards of care while working with less resources at their disposal. In this fast-paced, demanding environment, AI is increasingly being recognized as a pivotal tool in addressing complex challenges and improving patient outcomes. In an episode of the NerdMD podcast, Elad Walach, CEO of Aidoc, shared his insights on the current state of AI in healthcare and the direction it’s heading. Here are five key takeaways from his discussion, all centered around AI acting as a bridge to better care coordination and quality.

1. Acknowledge That Precision AI Remains Vital for Clinical Care

AI has made significant strides in improving accuracy, especially in clinical settings, but there’s still a long way to go in terms of generalizability. Deep learning models, though powerful, are best suited for highly specific tasks. In clinical care, this precision is essential. Clinical AI requires more stringent validation compared to administrative AI. As AI models focused on precision continue to evolve, they will remain crucial in clinical use cases, even as general AI models advance.

2. Understand the Importance of Breaking Down Silos in Healthcare

One of the most significant challenges in healthcare is the fragmentation of AI solutions. With over 900 FDA-cleared AI solutions, each focused on a specific disease or condition, healthcare systems are overwhelmed by the sheer number of point solutions. A more comprehensive approach is needed–a clinical AI layer that integrates multiple solutions and addresses various care gaps. Shifting from siloed solutions to integrated care pathways is essential for maximizing AI’s impact on patient care.

3. Scale Through AI Platforms

Scalability is a critical challenge for AI in healthcare. Initially, many companies focused on creating individual AI solutions, but soon realized that a robust platform is necessary for growth. A strong AI platform supports data normalization, workflow integration and third-party AI tools. By investing more in the platform than the individual algorithms, health systems can scale from one AI use case to 20 quickly. This scalability is key for the widespread adoption of clinical AI.

4. Use AI as a Catalyst for Care Coordination

The ability for AI to enhance care coordination across specialties is one of its most promising applications. AI is being used to help flag potentially critical conditions, such as pulmonary embolism and aortic aneurysms, and ensure that the appropriate specialists are alerted in real time.

5. Know That Clinical AI Is More Mature Than Perceived 

There is a common misconception that AI in healthcare is still in its infancy. However, clinical AI has matured significantly, with over 150 health systems utilizing AI to analyze 100,000 patients per day. The body of evidence supporting clinical AI is growing, with numerous studies and real-world applications proving its effectiveness. While administrative AI still has room for development, clinical AI is already making a meaningful impact on patient care.

The Road Ahead

The discussion on the NerdMD podcast emphasized the potential AI has to continually impact healthcare. From improving precision in clinical care to breaking down silos and enhancing care coordination, AI is poised to play a critical role in the future of healthcare. However, realizing this potential requires a comprehensive approach–one that integrates AI solutions, ensures scalability and continually pushes the boundaries of what’s possible in healthcare.

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