The use of artificial intelligence (AI) and computer vision in the healthcare industry continues to progress along a steep upward trajectory. Advanced AI technologies enable machines to interpret and analyze medical images, videos, and other data with unprecedented speed and accuracy. Computer vision combines that functionality with deep learning networks to enhance diagnostic precision, automate routine medical tasks, and support clinical decision-making. The evolving capabilities of computer vision help healthcare providers to better detect abnormalities, treat chronic conditions, and improve overall workflow efficiency.
As healthcare organizations integrate AI-backed solutions into clinical, surgical, remote health, and environmental workflows, computer vision is poised to become more vital to improving outcomes, streamlining operations, and reducing costs. An increased focus on privacy and ethical implementation accompanies this growing reliance.
Background and Evolution of Computer Vision
The foundation for computer vision was established in 1943 when a pair of scientists attempted to model the behavior of biological neurons to gain a more comprehensive understanding of how the brain produces highly complex activity patterns to assist various cognitive functions. Their work is said to have formed the basis for neural network theory. This theory has remained key to understanding how computers can see and interpret images, videos, and other visual inputs to process visual data. Today, neural networks have evolved into convolutional neural networks (CNNs) that contain filters that extract relevant image features, which are especially useful in the medical space for analyzing images such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) to assist in making diagnoses, establishing care plans, and conducting research. By learning patterns directly from raw images, the use of CNNs eliminates the need for manual feature engineering and improves machine learning (ML) model performance. Modern architectures such as residual neural networks, EfficientNet, and vision transformers enable deep, robust models with enhanced accuracy and real-time object detection capable of identifying dozens of objects with high speed and precision in video feeds. Today’s neural networks are also trained on massive datasets that recognize thousands of categories with very low error rates and pre-trained models that can be fine-tuned for specific tasks, improving domain adoption.
The benefits of CNNs include reduced potential for patient misidentification, medication errors, compromised patient care, the ability to build reliable solutions through image recognition, and object detection techniques for patient monitoring and identity verification. CNNs are often utilized for face-recognition applications that can analyze input images to identify individuals based on relevant features extracted from facial structures before administering treatments. Computer vision revenue is projected to see a 7% projected growth rate up to $20.88 billion by 2030 due to increasing demand in such industries as transportation, healthcare, and security (Figure 1).
Figure 1: Computer vision evolution. Image courtesy Laxman Sayaji Khandagale.
Computer Vision Tools: Advanced Applications and Advantages
The future of computer vision in healthcare is expected to be led by AI integration through systems that operate beyond diagnostics for more holistic purposes and care planning (Figure 2). Among the more notable examples of how these technologies are used include:
- Cross-domain applications. Most research in this space isolates computer vision to medical imaging, but additional work highlights the use of cross-domain applications for environmental hazard detection in the hospital environment and the observation of clinical facility monitoring (for example, patient fall detection and staff hygiene compliance). This ecosystem-wide perspective redefines the presence of computer vision as a tool for overall healthcare optimization. One recent study of a cross-domain transfer module proposes to transfer natural vision domain features to the medical image domain, facilitating efficient fine-tuning of models pre-trained on large datasets.
- AI-driven surgical skill assessment. By capturing and analyzing surgical procedure video recordings, systems can identify patterns that indicate various skill levels and connect specific metrics to performance and outcomes, such as instrument movement, eye tracking, and error rates/time to completion, to revolutionize training protocols. A recent review of collective research determined that intraoperative AI data analysis has the potential for automated technical skill assessment.
- Federated learning that preserves data privacy. With this emerging paradigm, data decentralization becomes more collaborative and ensures data safety through a distributed ML approach where multiple entities train a model utilized by numerous facilities without sharing their respective raw data.
Figure 2: How computer vision advances the healthcare industry. Image courtesy Laxman Sayaji Khandagale.
Addressing Ongoing Trust Issues
There are trust issues to navigate among patients and providers as AI and computer vision become increasingly pervasive, especially as some systems continue to operate in a “black box” fashion, making interpretation of decisions more challenging. Many of today’s computer models are trained on data collected from high-income countries and demographic groups, which raises questions about inherent biases and fairness when deploying AI within diverse populations or low-resource settings. When not appropriately addressed, algorithmic biases can worsen instead of alleviate healthcare disparities.
Another complicating factor is the significant uncertainty surrounding who bears the burden of responsibility and legal liability when system errors occur, or issues arise, such as an incorrect diagnosis or surgical error. According to Stanford University officials, this is a developing area of law for which the benefits and risks are balanced. While this dynamic is determined over time, accountability in decision-making among current stakeholders is crucial for establishing patient trust and meeting regulatory compliance standards. Stringent management practices throughout the industry limit unnecessary data retention and protect patient confidentiality, as risks of data breaches, hacking, and unauthorized commercial use of patient data are on the rise. Still, ethical concerns and challenges around clinician surveillance and autonomy could outlast any patient trust fallout. These traits can be perceived as punitive or intrusive, especially in skill assessment and staff monitoring (see Figure 3).
Figure 3: Importance of interpretable/explainable AI in healthcare. Image courtesy Laxman Sayaji Khandagale.
Explainability and Use Cases
Much like computer vision has vastly evolved, so has the concept of explainability, or the ability to more easily allow humans to understand and interpret the decision-making processes of computer vision models. This is particularly critical for their influence on healthcare outcomes and to ensure that clinicians validate and trust their AI-related decisions. Improved explainability is expected to foster more trust-building by sharing valuable insights into the impetus behind model designs. Explainability is among the many requirements to comply with medical regulations and ethical standards. Many regulators demand interpretable AI models for healthcare applications. For example, the European Union’s (EU’s) AI Act classifies AI systems used in healthcare as high-risk and, hence, must have sufficient transparency and explainability. Users must be able to interpret the system output. Additionally, the National Health Service has issued AI ethics framework guidelines with key principles for understandability. The U.S. Food and Drug Administration also emphasizes the need for transparency and interpretability as best practices (Figure 4).
Figure 4: Regulatory guidance framework for AI in healthcare. Image courtesy Laxman Sayaji Khandagale.
Explainable models also let researchers and clinicians more accurately identify potential errors and biases in their systems, such as patterns in misinterpreting specific image types. Among the explainability use cases developed are automated interpretation of images to support radiologists, real-time surgical guidance to assist surgeons with visual overlays, retinal scans, and stroke detection through analyzing CT scans. With computer vision poised to gain prominence as a pathway to healthcare innovation, the blooming potential will require organizational preparedness to maximize these benefits.
About the Author
Laxman Sayaji Khandagale has two decades of experience in leading and implementing robust, secure, high-performing applications in the card processing, clearing house and managed services industries. He worked with leading financial organizations using AI to guide decision-making and implemented fraud detection, data-replication lag detection, and other applications in fintech using AI/ML algorithms. Laxman has developed, tested, and deployed best-performing distributed and secure applications and processed data from multiple sources for business analysis and visualization. In addition, he is an expert in different SDLC phases of application development. Connect with Laxman on LinkedIn.
