Artificial Intelligence is the buzzword you probably are hearing everywhere. From data analytics, cyber security, robots, logistics, automatic cars, military equipment to education, AI is hailed to be the next big thing. The question we want to address is – what are the prospects of AI with respect to healthcare?

Medical experts worldwide are already applying AI technology to improve diagnosis, treatment recommendations, and healthcare administration. But there are more promising opportunities like:

Let’s explore each of these advantages in this blog.

AI Is As Effective As Healthcare Professionals

Many ongoing studies and observations say that AI could be as effective as health care professionals when it comes to medical diagnosis. For example, research funded by the NHS Foundation Trust in the UK has shown that the diagnoses made by AI deep learning models were as accurate as those of health professionals. AI could accurately diagnose diseases in 87% percent of cases compared to the accuracy rate of 86% for the same by

healthcare professionals. While these studies are made on limited test samples and biases could have indeed played a role in the actual results, it does prove a case for AI based medical diagnosis.

While AI diagnosis cannot replace the keen eye of an expert physician, it can help close the gap between the tech industry and healthcare and provide better means to optimize health care operations.

Combining Data Sources through Sensor Fusion

Radiology is one specific department in healthcare that can readily employ AI-driven analysis. Radiology uses different kinds of scanning tech such as X-rays and CT scans to gather information and diagnose diseases. Depending on the case, both X-ray and CT scans could be used in succession to evaluate a health condition properly.

The more information or inferences you can make out of these scan images, the more accurate your diagnosis could be. In addition, using image recognition AI software enables better diagnosis as it allows radiologists to combine and interpret data from multiple imaging sources and get deeper insights into the patient’s pathology.

Multiple Disease Diagnosis

As of now, most mainstream AI-driven analysis software is limited in use and can only detect the diseases that they are trained on. This can lead to wrong diagnoses where certain other medical conditions could be overlooked. To avoid such diagnostic errors, AI-based diagnosis systems are on the road to improving their algorithms so that theft

can detect multiple conditions from a single data set. These AI systems are capable of detecting more than 50 ocular medical conditions from a single retinal image.

As being able to detect multiple diseases will be the most profitable in the long run, many companies are starting to develop such systems despite the initial high investment costs. This would, in turn, force companies offering single disease detection software to extend their capabilities as well.

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Reduced Neural Network Complexity

While the power of AI is globally recognized, it is still not widely adopted in healthcare due to the complex development process involved with AI analysis software. Much of the existing AI software models use a complex network architecture and require expensive GPUs and immense processing power to deliver their results. So the

obvious improvement that can be expected would be to reduce the neural network complexity used by the AI systems so as to make them easier to adopt in the healthcare industry. Reducing the number of layers in the network would help decrease the computing power demanded by the AI systems, improve performance, and reduce server costs.

Equipment-Integrated AI Software

This is a parallel AI software development area where the image recognition AI software is directly installed on imaging equipment such as the CT scanner. This is advantageous in many aspects compared to a standalone AI software where you feed the data manually.

But there are certain downsides too. With such equipment integrated AI, the end-user is left with little choice as to the software they want to use for diagnosis. If the user finds the software’s performance to be sub-par, they might still want to go with cloud-based software.

Easy Access to Patient Data

Right now, many medical procedures and treatment plans get delayed because doctors have either no access or delayed access to a patient’s medical history. While the current AI diagnostic applications are limited to image processing, the future of AI can solve this data access issue by providing a comprehensive knowledge base that physicians can use.

AI models could provide ways to identify the optimal treatment strategies, deeper insights into the various medical ailments the patient has undergone, the best combination of medication that will suit the patient’s physiology, and so on.

The implementation of such advanced AI systems are still in progress. Itis riddled with practical challenges like accumulating data from multiple sources and interoperability among the various medical institutions and external databases.

Multifactorial Machine Learning To Detect Symptom Patterns

Another interesting ongoing development in the AI space is using multi-functional ML models to improve better diagnosis and medication prescription.

Intelligent big data platforms can improve multi-functional ML algorithms and help in the early detection of cancer. In addition, more predictive diagnosis can be performed for several constitutional disorders by drawing data from clinical sources, disease relationship mappings, EHR, and health stats data collected from wearables and IoT.

Conclusion

There is so much to look out for in the AI technologies in the healthcare sector. With each day, AI is proving to be a great asset that physicians worldwide could use to improve clinical consultations, speed up accurate diagnosis, and provide personalized care and treatment plans. But besides the obvious need for definitive guidelines on using AI, there are several more factors like the economic, data collection, and compliance regulations that need to be sorted out before AI can indeed become a mainstream part of modern-day medicine and healthcare.

How are AI technologies advancing medical diagnosis?