Understanding AI in Healthcare: Enhancing Intelligence, Transforming Care

Establishing a Common Language

In the rapidly evolving landscape of healthcare, Artificial Intelligence (AI) has emerged as a transformative force. To fully harness its potential, it is crucial to understand what AI entails and how it can support the patient-physician relationship without undermining it.  

Equally important is the need to demystify the AI “black box” and ensure transparency and confidence in its recommendations and decision-making processes. Without a clear definition of AI and an understanding of why it offers certain answers, it is difficult to appreciate how it can support the human elements of care that are fundamental to medicine, rather than replace them. 

The American Medical Association (AMA) defines AI as “augmented intelligence,” highlighting its role in enhancing human intelligence rather than replacing it. Webster’s dictionary defines it as the capability of computer systems or algorithms to imitate intelligent human behavior. 

Regardless of which definition you ascribe to, it can be agreed that AI is a broad category that includes various technologies, from simple algorithms to complex neural networks. This blog aims to provide a basic understanding of AI and its applications in healthcare, particularly for electrophysiologists and clinicians.  

Basic Definitions of Types of AI

AI encompasses a range of technologies designed to assist and enhance human intelligence. In the context of healthcare, it is meant to assist clinicians in decision making. AI can be as simple as rules-based algorithms meant to mimic human decision making or as sophisticated as deep learning models that will learn and adapt with more information. Understanding these categories is essential for navigating the AI landscape in healthcare. 

1. Rules-Based AI. This is the simplest form of AI, consisting of programmed algorithms with predefined rules. In the EP world, medical devices like pacemakers have used this type of “AI” for decades.
2. Machine Learning (ML). ML is a broad category where systems learn and evolve by analysing new data inputs. It includes several subtypes, including:

• • Deep Learning. This subset of ML uses multiple layers of analysis, each building on the previous layer’s results. While deep learning models, such as neural networks, are being researched and developed for diagnostics in dermatology, radiology and pathology, their widespread application is still emerging. These models are also increasingly being used in the diagnosis of cardiovascular disease. How these models derive their outputs is often a question. Understanding the data used to train and validate the models will provide a lot of insight into their outputs. It is also helpful to understand how these models are monitored for drift as they are applied across different populations. A limitation of deep learning, identified across studies that compare human to algorithmic performance, has been the challenge of clinical context associated with the diagnosis. This article does an excellent job diving into detail of the different AI models in healthcare. We will explore these topics in more depth in the next article.

3. Natural Language Processing (NLP). NLP involves algorithms designed to understand and generate human language. It is particularly useful for specialized tasks like parsing and entity recognition, which are critical in processing medical records and patient interactions.
4. Generative AI. Generative AI, popularized by tools like ChatGPT, uses large language models to handle a broad range of language tasks. While versatile, it isn’t as precise as NLP for specific tasks. Medium has a nice article that outlines the differences in these AI tools. 

Applications of AI in Healthcare

AI’s applications in healthcare are diverse and continually expanding. Here are some key areas where AI is making a significant impact: 

• Image Analysis: AI models, particularly deep learning, are proving effective in interpreting medical images for diagnostics in dermatology, radiology, and pathology. 
• Cardiac Care: At the recent Heart Rhythm Society Conference in Boston, numerous sessions focused on AI solutions for identifying and predicting cardiac diseases, showcasing the extensive research and application of AI in this field. 
• Patient Assistance and Record Management: Both NLP and generative AI are being used for tasks ranging from automated patient assistance to summarizing medical records, improving efficiency and accuracy in clinical workflows. 

Conclusion

AI in healthcare is a broad and evolving field. While some AI technologies have been integrated into healthcare for decades, the latest advancements are at various stages of adoption and research.  

As we continue to explore AI’s potential, it is important to remember that its primary goal should be to support and enhance the patient-physician relationship. A clear understanding of what AI entails and why it produces specific outcomes can help in leveraging its benefits fully. While it is not always possible for companies to explain every AI outcome in detail, transparency about the development and validation of these solutions can build trust and facilitate their integration into clinical practice. 

In the next part of this series, we will examine different AI solutions in the cardiovascular space, providing insight into different applications and technology being developed and used.