What Is Medical AI?

Medical AI refers to the application of artificial intelligence–based methods and technologies to medical data to enhance the quality and efficiency of healthcare. AI enables machines to mimic human intelligence; for medical AI, applications encompass image and signal analysis, pattern recognition, and data science. Due to its high accuracy and versatility in understanding complex medical data, AI is increasingly being adopted within healthcare. The development of medical devices using AI is becoming more widespread, and several AI-powered medical devices have received approval from regulators such as the FDA and are on the market.

Examples of Medical AI in Practice

AI is used in a range of medical applications:

• Medical imaging: Image recognition technologies, such as deep learning, can enhance diagnosis accuracy and reduce diagnostic time for medical images such as X-ray, CT, ultrasound, PET/SPECT, and MRI.
• Electronic health record (EHR) data analysis: AI models can examine a patient’s medical records to estimate their risk of disease and recommend a customized treatment plan.
• Patient monitoring: AI-driven systems can analyze sensor data to monitor patient health in real time and notify medical staff of potential adverse events. Use cases include postoperative care, monitoring of elderly patients, and remote diagnosis.
• Surgical assistance: Robotics enable precise, controlled, minimally invasive surgeries.

The Benefits and Challenges of Using Medical AI

Implementing medical AI approaches can bring various benefits to healthcare, including:

• Improving diagnosis accuracy and speed: AI models can aid in diagnosing by interpreting medical images from different radiology modalities such as X-ray, MRI, ultrasound, and CT. AI algorithms learn from vast image data to detect small anomalies, increasing diagnostic accuracy and speed. With evolving clinical workflows, medical AI can increase interpretation efficiency.
• Facilitating personalized medicine and optimizing treatment protocols: AI models can examine data, including a patient’s medical history, genetic information, and lifestyle factors, to recommend the most suitable treatment plan for each individual. This approach enables more effective and tailored treatments for patients.
• Enabling early detection and prevention: AI models can analyze patterns in large data sets and identify the risk of certain diseases at an early stage. This approach enables preventive measures or early treatment, potentially preventing the progression of the disease.
• Accelerating drug development: AI-driven systems can screen compounds and analyze clinical trial data, reducing time and costs to deliver new therapies.
• Enhancing access to healthcare: Applying AI to telemedicine and mobile health applications can enable the delivery of high-quality healthcare services to individuals who face geographical and economic barriers to accessing traditional healthcare.

Through these benefits, medical AI is expected to contribute to improving the quality of care, improving patient outcomes, and reducing healthcare costs.

Conversely, there are difficulties in training medical AI models and challenges facing medical AI:

• Data bias during training: AI models learn from the data provided by developers. If the data is biased, the model may give inaccurate results for certain populations. For instance, a lack of data for a specific race or gender can affect the AI model’s performance for that group.
• Timely deployment of implemented algorithms: To ensure that medical AI provides benefits to patients, it is essential to apply research findings to clinical practice. If the software or environment used is incompatible with actual equipment or if deployment takes too long, efforts to improve efficiency may result in additional time and effort, thereby increasing the risk of project failure.
• Privacy and security concerns: Medical data includes highly personal and sensitive information. The development and use of medical AI may increase the risk of this information being mishandled or leaked.
• The problem of overconfidence: If healthcare professionals rely on medical AI too much, they might suggest treatments unsuitable for the patient. AI should support, not replace, expert judgment. In complex cases or rare diseases, it’s less effective than specialists. Moreover, data-driven AI, like deep learning, can be hard to interpret and may lead to errors.

Medical AI Development with MATLAB

By using MATLAB^® and Simulink^®, you can develop and deploy medical AI approaches while mitigating the challenges and limitations associated with medical AI. Product capabilities include:

• Visualization and analysis tools: With MATLAB you can create graphs and charts to aid in understanding algorithm outputs and performance metrics. These extensive visualization capabilities enable you to perform a detailed analysis of the algorithm’s behavior and performance, as well as evaluate its reliability and versatility.
• Medical image analysis: Medical Imaging Toolbox™ provides apps, functions, and workflows for designing and testing diagnostic imaging applications. You can perform 3D rendering and visualization, multimodal registration, and segmentation and labeling of radiology images. When you use Medical Imaging Toolbox with Deep Learning Toolbox™, you can also train predefined deep learning networks.
• A workflow from algorithm development to device implementation: MATLAB seamlessly integrates with GPUs, CPUs, FPGAs, and other embedded processors. It also works with Simulink for algorithm simulation and prototyping, enabling you to test and evaluate your algorithms on your data and systems before production deployment.
• Reduced development time: MATLAB provides numerous pre-implemented algorithms, along with extensive support for users. The integration of MATLAB with Simulink facilitates efficient prototyping and simulation, enabling you to detect and resolve issues before deploying the actual device or system, thus reducing physical prototyping costs. Leveraging these capabilities can significantly expedite the development of medical AI applications.