The healthcare market size was valued at USD 6.7 billion in 2020 from global artificial intelligence and is expected to expand at a compound annual growth rate (CAGR) of 41.8% from 2021 to 2028 (Source Grand View Research). Data scientists and engineers build many tools. They have an abundance of data and several opportunities to build AI/ ML models. The objective is to provide healthcare professionals with the right model at the right time in the right way so that tools are utilized and drive value. Let’s review some business use cases for healthcare where Artificial intelligence and Machine learning is playing an instrumental role.
Let’s review some business use cases for healthcare where Artificial intelligence and Machine learning are playing an instrumental role.
Deep learning and medical image recognition
Recent progress in deep learning has shed new light on medical image analysis by allowing discovering morphological and/or textural patterns in images solely from data. AI is helping in identifying imaging pattern changes that are not easily amenable to human identification.
Gene Sequencing
Using machine learning both RNA and DNA sequencing data can be easily harmonized. A large amount of genetics-related data can be analyzed through ML models. Machine learning models will utilize the carriers of information to perform predictive analysis such as cancer biomarkers to assist in the early diagnosis of cancer.
Precision Medicine
Predicting what treatment options are likely to succeed on a patient based on various patient attributes and the treatment setting. Taking the enormous amount of genomic data can be analyzing it together with phenotypic data to inform future clinical practice, in the form of precision (personalized) medicine. Using genomic data, with other medical data, can provide an individual “personalized” view of treatment options to achieve the goal of optimum treatment of an individual’s illness.
Wound detection/Skin analysis
Identify wound size, shape, area, and volume using artificial intelligence (AI). Image Segmentation technique to develop a solution to measure the wound (length, width, tissue analysis), determine dead tissues through the Computer Vision technique.
Natural language processing
There is a huge amount of healthcare and medical data which could be in form of unstructured text like Practitioner notes as captured while doing analysis, medical publications, electronic health and medical records, Transcripts captured by scribes as well as clinical trials protocols. Drawing meaning full insights from all that information is an incredible challenge. AI/ML can play an instrumental role here. Meaningful insights are always needed for patient engagement , patient monitoring, and improving overall efficiency.
For a US-based Health Insurance plan pre-audit platform, smartData created an NLP POC to extract specific demographics and provider data from hundreds of medical billing charts and categorized them based on specific medical keywords.
Augment Reality and Virtual Reality
To simulate surgery before performing it should bring about a significant improvement in the surgical outcome. To be able to visualize the anatomy before the actual procedure would help anticipate the difficulties that would be encountered and help formulate strategies to counteract them. It requires the superimposition of multiple scan images to obtain the precise anatomy, correctly depicting the bony and soft tissue as well as the vessels and possibly the nerves.
Virtual AI
artificial intelligence in medicine is also becoming commonplace and in Obstetrics and Gynecology will enable health care professional to make better decisions quicker. The virtual AI will make determinations based on imagery live and on-demand data and serving use cases for age and gender detection.
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