The adaptability of deep learning as a pattern recognition tool has enabled its use across a wide range of healthcare contexts. As deep learning models can be trained to recognize many different types of patterns—whether in medical images, physiologic signals, or free-text documentation—their clinical utility is broad and continually evolving. Depending on the nature of the pattern being analyzed, more specific terms are often used to describe how deep learning is applied in practice, including computer vision, biomedical pattern recognition, and natural language processing.
One of the most well-established areas of success is computer vision, which refers to the use of deep learning to recognize patterns in images or videos. Computer vision applications have the potential to detect abnormalities, localize findings, classify conditions, and even quantify the extent of disease. In radiology, AI tools have been shown to improve the performance of radiologists in detecting lung cancer on chest radiographs19 and breast cancer on mammograms.20,21 Other models have demonstrated high sensitivity and specificity in localizing radius and ulna fractures on wrist radiographs.22 In ophthalmology, deep learning tools have shown effectiveness in screening for diabetic retinopathy, with the potential to reduce workload and improve diagnostic accuracy.23 Looking ahead, computer vision is poised to evolve toward real-time applications in procedural settings and integrated multimodal diagnostics, combining imaging with other clinical data sources for more holistic decision making. These advances highlight how deep learning can augment clinical judgment and reduce diagnostic error—an impact that extends beyond imaging to other modalities.
Biomedical pattern recognition refers to the use of deep learning to recognize patterns in physiologic or biometric data. These models are increasingly applied across a range of clinical contexts from high-acuity inpatient monitoring to population-level screening in outpatient or consumer settings. The AI-enabled sepsis prediction algorithms discussed earlier have demonstrated meaningful clinical impact, having been associated with a 9.5 percent reduction in in-hospital mortality, a 32.3 percent reduction in hospital length of stay, and a 22.7 percent reduction in 30-day readmissions for sepsis-related stays.24 Deep learning models have also been embedded in wearable technologies, offering real-time physiologic monitoring outside the hospital. Studies involving over 400,000 participants found that atrial fibrillation detection algorithms achieved positive predictive values of 84 percent and 98.2 percent using Apple Watch and Fitbit devices, respectively.25,26 In more specialized applications, deep learning is being used to analyze genomic sequencing data to help guide individualized care, supporting efforts to tailor treatments to a patient’s unique biology.27 Future applications of biomedical pattern recognition may shift from episodic detection toward continuous health monitoring, leveraging wearable and genomic data streams to enable earlier disease prediction and personalized preventive care.
Natural language processing (NLP), or the application of deep learning to recognize patterns in text data, enables AI to interpret and generate language. NLP is often accompanied by automatic speech recognition (ASR), another deep learning application that transcribes spoken language into text, allowing AI systems to process verbal input. The use of deep learning in this context has led to the development of ChatGPT and other Large Language Models (LLMs), which are effectively next word prediction algorithms. There are numerous uses of NLP and LLMs in healthcare, often offering workflow and clerical support across various clinical settings. Ambient digital scribes, an LLM-driven technology that provides drafts of clinical documentation for providers, have been shown to significantly reduce documentation time and provider burden.28,29 Similarly, AI-generated draft responses to patient messages have demonstrated the potential to reduce clinician burnout and cognitive load.30,31 While most current popular applications focus on using LLMs for clerical tasks, the latest models have been shown to excel in medical reasoning: an LLM outperformed physicians on a diagnostic reasoning task, even when those physicians were allowed to use the AI system as an assistive tool.32 As LLMs continue to improve, future models may effectively act as colleagues, assisting clinicians with both clerical and diagnostic tasks.
Taken together, these examples highlight the versatility of deep learning across domains of care delivery, diagnostic support, administrative burden reduction, and patient engagement. While these applications are far from exhaustive, they underscore the potential of deep learning to transform multiple healthcare processes.
