Artificial Intelligence is making inroads in every walk of life. There is a vast scope for Artificial Intelligence to sift through the data if there is massive data. We are constantly learning from it and receiving valuable insights and discoveries which otherwise would have taken years, maybe even decades.
Healthcare is one such field where the record of each aspect of the patient’s health is critical in securing early diagnosis and quick recoveries. It is no wonder then that Artificial Intelligence plays a significant role in the healthcare industry. In the following sections, we will look at what AI technologies are being used for healthcare and how these AI-powered applications and systems bring about a revolution in the healthcare industry.
With a lot of data available within the healthcare industry, Machine Learning is an obvious choice. Machine Learning techniques like Deep Learning, Supervised Learning, and Neural Networks are being used to improve the efficiency and quality of healthcare.
Supervised Learning is used where there is a lot of clinical data with known results the machine can learn from and can draw up a quick and accurate line of diagnosis.
Neural Networks are used to predict and preempt any diseases in the future based on the current clinical data of the patient.
Deep Learning is used to uncover hidden associations and patterns that can detect anomalies in the patient imaging data way beyond what a human eye can perceive.
Natural Language Processing
Natural Language Processing, an AI technology, is used to decode textual and voice-based data using linguistics. Speech recognition is the most prominent among these, used to classify and understand clinical documentation. NLP is used in all its forms to analyse notes on patients, be it in textual or voice format. The ability of NLP to sift through hundreds of prescriptions and diagnosis in no particular order and make sense out of all of it in minutes is improving the overall diagnosis and treatment outcomes.
We shall go through some of the real-world applications of AI in the healthcare sector.
Artificial Intelligence use cases in Healthcare
Let’s now get into a few prominent use cases of AI in the healthcare industry before we list some of the actual implementations of AI in this sector.
Al as an alternative to Rule-based Expert Systems
One of the primary and elementary technologies used in healthcare was the rule-based expert system. It was a sophisticated variation of the ‘if then’ rule-based system. But with the growing number of rules, it has become increasingly complex with conflicting rules. This rule-based system has given way to modern AI based on proprietary medical algorithms, solving scalability, efficiency, and accuracy issues. Integrating AI into clinical workflows and EHR systems is a work in progress but will soon be a reality. Supervised and Deep Learning techniques improve the speed and accuracy of diagnosis, saving time, effort, and money in investigations.
A good part of a healthcare system is its administrative arm. Without efficient administration, a hospital would start crumbling under its weight. AI in the administrative domain in hospitals has brought in substantial efficiencies, although still a lot less than what is achieved in inpatient care. Artificial Intelligence-powered applications are way better at clinical documentation, medical records management, claims processing and more, improving the overall efficiency of any hospital or large healthcare facility.
Let us now look at 12 specific uses of AI in the healthcare sector
Patient engagement and adherence applications
The last mile in healthcare is how the patient conforms to the healthcare plan. This had turned out to be a challenge with patients not engaging in the plan as promptly as you would want, affecting healthcare plans adversely.
With AI-based patient engagement and adherence applications, it has become easy to navigate this last mile, resulting in an effective healthcare plan.
Using AI to Diagnose and Reduce Error Efficiently
One of the phases of healthcare plans which use AI the most is the diagnosis phase. One big reason is the availability of massive data that machine learning algorithms can use to train their systems to arrive at a quicker and accurate diagnosis. AI can help diagnose cancer cases much quicker and at a higher rate than clinical pathologists.
Chatbot based AI assisted doctors
AI-based machines can accept symptoms over a chat or a voice conversation and suggest symptom-based treatment. One such example is the Harvard Medical School, which uses its Buoy AI to help both diagnose and treat patients.
AI-based radiology diagnosis
AI was put to use in the Covid-19 pandemic in 2020-21 to help doctors diagnose the impact of covid on lungs with the help of X-rays. This machine learning-based AI technology could let doctors know of Covid-19 infection in the lungs and scan imaging data of the patient.
Another example is Enlitic, a well-recognized AI products and services company that developed deep learning-based medical tools to analyse unstructured data like imaging, blood tests, genomics and more to give doctors a complete picture on a real-time basis.
AI in the fight against deadly pathogens
AI is being used at Harvard University’s teaching hospital to power up microscopes and help detect harmful pathogens like E-coli, Staphylococcus etc., at a much faster clip than traditional methods. The machines were taught how to identify harmful pathogens using a feed of more than 25000 microscope scans of blood samples.
AI in drug discovery
Artificial Intelligence is playing a pivotal role in advanced drug discovery in the fields of neuroscience and oncology. AI-assisted technology can discover hit and lead compounds providing a quicker validation of the drug.
It takes years or even decades for a drug to reach the market after going through the phases of discovery, development and clinical trials. AI algorithms like Nearest Neighbor classifiers, Deep Neural Networks help predict in vivo activity and toxicity of chemicals involved.
AI in Healthcare automation
With many mundane and repetitive tasks in the healthcare industry, especially on the administrative and documentation front, many products are out there in the market that help automate the healthcare industry’s repetitive tasks.
Applications like the Olive AI platform help automate eligibility checks, claims processing, admission processing, ER management and more.
AI in personalised interactive healthcare
AI-powered systems can enable hospitals to now arrange anytime face-to-face doctor appointments. One case of such AI systems is Babylon. This AI-powered chatbot suggests a virtual check-in or a face-to-face consultation based on the symptoms presented.
AI for smoother delivery of healthcare
AI is being used to improve patient journeys starting from admission to a healthcare facility right until discharge. Using patient data, clinical history, these AI-powered systems can suggest interventions at critical junctions of the treatment, improving the overall experience for a patient.
CloudMedX is one such provider that offers machine learning-based solutions to generate insights into patient journeys while also learning from the data.
AI for predictive analytics in healthcare
AI-powered systems capable of learning from the clinical, financial and operations data of a healthcare institution help with operations and financial risks for a healthcare facility. These applications project everything from clinical to the financial situation into the future. This allows hospitals to take preemptive actions reducing both costs and risks.
KenSci is an excellent example of an application that can forewarn impending healthcare emergencies to suggest measures to drive down healthcare costs.
AI in healthcare workflows
AI is being used in healthcare systems even to predict processes and events. AI can predict events like ICU transfers and hospital-acquired infections in advance, and processes can be readied for a smooth transition.
H2O.ai is one such application that can mine, automate workflows and predict events.
AI assisted robotic surgery
With an estimated failure rate of 0.38% in robotic surgeries, AI-assisted robotic surgeries are much safer than those performed by doctors.
AI-assisted robots capable of conducting surgeries are augmenting a surgeon’s skill and knowledge in the operation theatre. Vicarious surgeries combine virtual reality with AI-powered robots helping doctors perform minimally invasive surgeries with a higher success rate and faster recovery time.
The Future of AI in Healthcare
AI will be an enabler in the future, and humans will have to learn to work alongside surgeon AI playing an ever-increasing role in healthcare. Professionals in this sector will slowly but surely move onto tasks that require more niche and unique skills. The cognitive space is still a grey area for artificial intelligence, although a lot of research is underway on this front.
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