Airtract

richard devis

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Artificial Intelligence (AI) is continuously evolving and impacting every industry known to humans. But there are a few fields where this technology offers more opportunities for dynamic human life improvement - Healthcare and Medicine. 

In a nutshell, AI in healthcare and medicine is all about the effective use of data and business-critical information via deep learning and machine learning algorithms to generate the best possible patient outcomes.

Here’s a compilation of some use cases of AI in medicine to give you a better idea.


Medical Imaging


In Radiology, physicians analyze images collected from PET scans, Mammography, Ultrasound, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, etc. AI-enabled medical imaging technology can expand these image analysis capabilities with pattern recognition. It can help physicians to identify early predictors of cancer or even highlight certain image features at ease.  


Hospital Resource Optimization


Another important application of AI in medicine is effectively maintaining the administrative demand-supply balance of resources. AI systems can forecast future demands and monitor treatments in real-time to help hospitals effectively use the available resources including budget. These systems can successfully automate the entire administrative process to combat an upcoming mass outbreak.


Clinical Workflow Optimization


With the right implementation of AI, medical facilities, diagnostic centers, doctors, etc. can automate the process of routing patients to the right departments, securing reports, and returning for further diagnosis. Such clinical care-centric applications of AI in medicine and healthcare can guide the professionals to optimize internal/ external workflow and support quality caregiving.


Tissue Sample Diagnosis 


Machine learning techniques can help pathologists to classify, sort, and differentiate tissue samples in a better manner. These techniques identify the areas of interest in tissue samples via certain algorithms that enable the diagnostics centers to group samples into categories so that they can frame useful alterations. These alterations can then be used to assist future patient conditions.   


Drug Discovery


Pharma companies spend a lot of money on discovering the right drugs for treating a particular disease. This process appears to be complex with added probabilities of failure resulting in high drug prices. However, leveraging AI in pharmaceutical research simplifies this process by pioneering drugs that machine learning can categorize, prioritize, and examine to bring the best to the table. 


Clinical Decision Support (CDS) Systems


Medical professionals often make treatment and clinical decisions with their prior and existing experience and expertise. Indeed, machines cannot perform the same as they depend upon large volumes of inferential data. However, with AI, medical facilities can ensure a certain level of accuracy to make effective, patient-centric, and realistic decisions by offering clinical decision support systems.


Medical Devices


AI tools can be trained to handle time-series data which is too difficult for human beings to monitor and analyze. These tools can be automated to identify errors and crunch this type of information thereby predicting health issues. Besides, ventilators and anesthesiology machines can also be targeted with automation to process lumps of data and support patient outcomes.


Tailored Precision Medicine


Machine learning has the ability to analyze genetic data across vast populations of individuals. This facilitates individualized medical prescriptions and treatments for different kinds of illnesses. Such a simplified approach with AI-based recommendations shall help to avoid potential risks related to unanticipated drug interactions and doctors can offer the needed care on-demand.  

AI will keep unlocking potential across various areas of human health and only time can tell what these potentials would actually be. 


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