Thank you for Subscribing to CIO Applications Weekly Brief
Key Ways AI Helps in Medical Diagnosis
You've probably heard the term "artificial intelligence" everywhere. AI is being heralded as the next big thing in everything from data analytics to cyber security, robots, logistics, self-driving cars, military equipment, and education. The question we want to answer is, "What are the prospects of AI in healthcare?"
Fremont, CA: Medical professionals worldwide are already using AI technology to improve diagnosis, treatment recommendations, and healthcare administration. However, there are more promising opportunities, such as:
AI is Just as Effective as Medical Professionals
Many ongoing studies and observations suggest that AI may be as effective as medical professionals in medical diagnosis. For example, research funded by the NHS Foundation Trust in the United Kingdom found that AI deep learning models' diagnoses were as accurate as health professionals. AI could correctly diagnose diseases in 87 percent of cases, compared to healthcare professionals' accuracy rate of 86 percent.
Sensor Fusion is a method of combining data sources.
Radiology is one area of healthcare where AI-driven analysis can be used effectively. Radiology gathers information and diagnoses diseases using various scanning technologies such as X-rays and CT scans. Depending on the circumstances, X-rays and CT scans may be used in tandem to evaluate a patient's health properly.
Diagnosis of Multiple Diseases
Most mainstream AI-driven analysis software is limited in its application and can only detect diseases on which it has been trained. This can lead to incorrect diagnoses and the omission of other medical conditions. To avoid such diagnostic errors, AI-based diagnosis systems are working to improve their algorithms so that theft does not occur. can identify multiple conditions from a single set of data
Reduced Complexity of Neural Networks
Even though the power of AI is widely acknowledged, it is still not commonly used in healthcare due to the complex development process involved with AI analysis software. Many existing AI software models employ a complex network architecture and necessitate expensive GPUs and massive processing power to deliver their results. In this case,
The apparent improvement expected is a reduction in the neural network complexity used by AI systems, making them easier to adopt in the healthcare industry.
AI Software Integrated Into Equipment
This is a parallel area of AI software development in which image recognition AI software is directly installed on imaging equipment such as CT scanners.
Simple Access to Patient Data
Many medical procedures and treatment plans are currently delayed because doctors have no or delayed access to a patient's medical history. While current AI diagnostic applications are limited to image processing, the future of AI can solve this data access problem by providing a comprehensive knowledge base for physicians to use.