How AI Tensor Models are Used in Healthcare

Author

Campion Quinn
December 18, 2024

By Campion Quinn, MD

 

Medicine continually evolves to enhance patient care, and artificial intelligence (AI) now stands at the forefront of this progress. At the core of many cutting-edge AI solutions used in healthcare is the concept of the “tensor model,” a mathematical framework that can handle complex, multidimensional information. Although the term may seem unfamiliar, developing a basic understanding of tensors and how they work can illuminate how these models contribute to more informed and precise clinical decision-making.

What Is a Tensor Model?

At its core, a tensor is a mathematical structure representing data in multiple dimensions. You can think of it as a generalization of simpler data formats:
- A scalar is a single number (e.g., blood glucose level: 120 mg/dL).
- A vector is a one-dimensional array of numbers (e.g., a patient's vitals over a day).
- A matrix is a two-dimensional array (e.g., a table with rows for patients and columns for lab values).
- A tensor extends this concept to three or more dimensions.

To better understand tensors, start with something you already know: data tables. Imagine a single lab test result for one patient, recorded at a specific time. That’s one-dimensional data. Now consider a laboratory report with multiple tests taken over several days. It forms a two-dimensional table with rows and columns—one axis for tests and another for time points. However, in healthcare, data rarely stops at just two dimensions. Patients have imaging studies, demographic factors, medication histories, genetic markers, and more. When we combine all these factors, we create a data structure with not just length and width but multiple “layers” of information stacked upon each other. These “layers” might simultaneously represent imaging data, lab values, and clinical notes. Such a multidimensional data structure—a shape that exists beyond just rows and columns—is what we call a tensor. It’s like a highly organized storage unit that can contain many categories of clinical information in a single mathematical object.

A helpful metaphor is to think about a Rubik’s Cube. Each small square on a face could represent a data point, such as a patient’s white blood cell count on a given day. The adjacent squares on the cube might represent related labs, while another face might represent imaging data. Another face could represent patient demographic factors—age, sex, and ethnicity. Tensors allow us to hold all these variables simultaneously, enabling AI models to consider them together rather than separately. This comprehensive perspective can lead to more nuanced analyses and insights.

Why Is Multidimensional Data Important in Medicine?

Understanding multidimensional data is essential because healthcare data is inherently multi-dimensional. Consider a single patient’s profile, which might include temporal data, such as heart rate or blood pressure readings recorded at different time intervals; imaging data, like MRIs or CT scans composed of multiple layers and angles; genomic information involving an entire sequence of genes and their varying expression levels; and clinical notes, often unstructured text found in electronic health records (EHRs). Each of these data types carries its own layer of complexity and meaning, and integrating them effectively is essential for gaining a holistic understanding of a patient’s health.

How does this help in your clinical work? Consider predicting a patient’s risk of a major cardiovascular event over the next five years. Rather than looking at just the patient’s cholesterol level in isolation, a tensor model might integrate their entire lipid profile over time, electrocardiographic (ECG) readings, echocardiographic measurements, imaging-based plaque assessments, and relevant genomic markers. By “seeing” all these dimensions simultaneously, the tensor-based AI can identify complex patterns and interactions that simpler models might miss. For example, subtle changes in ECG patterns combined with a patient’s family history and serum markers might better predict future risk than any one factor alone.

Applications of Tensor Models in Clinical Practice

Tensor models are transforming other areas of clinical practice, too.  By analyzing medical images such as X-rays, CT scans, and MRIs, these models can identify patterns that alert radiologists to potential abnormalities, ranging from tumors to vascular blockages. In predictive analytics, tensor models mine large datasets to forecast disease progression or treatment response, assisting clinicians in making timely and informed decisions—such as identifying ICU patients at high risk of sepsis. In genomics, these models manage vast and intricate data sets to uncover links between genetic variants and diseases, which supports the development of personalized treatments like targeted cancer therapies. They also enhance the usability of electronic health records (EHRs) by applying natural language processing (NLP) techniques to unstructured clinical notes, reducing administrative burdens for physicians.

Beyond these applications, tensor models improve remote patient monitoring by analyzing continuous data streams from wearables, detecting early warning signs of conditions like arrhythmias or sleep apnea. In drug discovery, they expedite the search for effective treatments by examining three-dimensional molecular structures and predicting drug-target interactions. They even optimize hospital operations by forecasting patient volumes, improving staffing decisions, and efficiently allocating resources. 

For practicing physicians, tensor models translate into better tools and more favorable patient outcomes. These advanced AI systems can accelerate diagnoses by rapidly highlighting abnormalities in medical images, reducing the cognitive burden of sifting through countless scans. They also enhance the personalization of care, tailoring treatments to individual patients with improved precision. By automatically extracting and summarizing critical information from extensive patient records, tensor models free physicians from time-intensive administrative tasks, allowing them to focus more directly on patient care. Furthermore, these models can detect subtle shifts in vital signs or laboratory values, enabling earlier interventions and potentially preventing serious complications before they arise.

Limitations

 However, we must also acknowledge the limitations. Tensor models rely on high-quality, representative data. Biases in the data can lead to skewed results, and privacy concerns must be carefully addressed. No matter how advanced the AI, human clinicians must remain at the center of the decision-making process. As a physician, you would review the AI’s recommendations, interpret them in the clinical context, and decide how to apply them. The AI’s job is to highlight patterns and possibilities, not to replace your judgment. If I do not know something—such as the exact predictive accuracy of a specific tensor-based system for a rare neurological disease without published data—I will say, “I don’t know this.”

The Bottom Line

Tensor models are complex under the hood, but their purpose is simple: to make sense of the multi-dimensional data that defines modern healthcare. Whether assisting in diagnostics, predicting patient outcomes, or optimizing operations, these models empower physicians to deliver more precise, efficient, and personalized care.

You don’t need to understand the intricate mathematics to appreciate their impact. As these tools become more integrated into clinical workflows, they will likely become indispensable allies in your practice.

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