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Why AI and ML are Critical for the Healthcare Industry

Our healthcare fraternity was among the most affected industries when the pandemic came unannounced, but the happy news is that clinicians, medical providers, and hospital administrators were quick to adapt to AI-enabled techniques like natural language processing and machine learning technology to overcome manual challenges and strengthen the healthcare sector and become more efficient and affordable.

In the last couple of years, the healthcare industry predicted several things – from emergencies and effective treatments to handling staffing and planning rosters, AI and its army of tools have made the life of medical professionals and patients quite simple. Besides predictive analytics, healthcare also benefited from blockchain technology which upholds patient information and protects health data to ensure the patient is receiving the best of both worlds.

The healthcare industry is at the cusp of some sophisticated technological changes and AI and ML are only helping to refurbish the existing systems and improve efficiency by automating processes and helping augment the work of the clinical staff and nurses. For instance, several manual, tedious and repetitive tasks can be automated, so the scope of manual errors can be eliminated.

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The basic functionality of AI and ML with respect to the healthcare industry is to improve clinical workflows, streamline patient records, automate operational processes, and enable smart machines to do repetitive tasks.

With this article, we are highlighting how AI and ML are transforming the way the healthcare industry works and how patients are enjoying a more efficient and accurate medical experience.

Let’s begin with ML and AI’s contribution to the healthcare sector and highlight its numerous applications that are transforming the future of medicine and patient experience.

ML in Healthcare

Predicts and treats diseases with the help of data

By extracting and analyzing large amounts of data, machine learning technology enables medical providers to generate customized medicine solutions according to each patient’s characteristics.

Organization patient information

Another potential advancement in the medicinal field includes telemedicine as several ML companies are striving to organize the process of delivering patient information to doctors during telemedicine sessions.

Discover new drugs

Many pharmaceutical companies are turning to machine learning to seek help in drug discovery as well as drug development. It is predicted that in the future ML could suggest to drugmakers how each patient will react to a certain drug and also predict which patient could benefit the most from these drugs.

Offering medical imaging and diagnostics

Microsoft’s Project InnerEye uses a combination of computer vision and machine learning to find the difference between tumors and healthy anatomy. This type of technology uses 3D radiological images that help medical professionals in surgical planning and radiotherapy. Sometimes, while doing a scan, the radiologist’s primary focus is one thing, but there are also instances where some other things can be seen in the background. With the help of AI, these secondary, unfocused issues can be taken up and researched further.

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A health organization that can fundamentally and rationally reimagine, reevaluate, and rethinks its workflows and processes with the help of AI tools and machine learning techniques can enjoy the pinnacle of success.

AI in healthcare

Monitoring Epidemics

During the Ebola outbreak, even before World Health Organization reported it, AI has already monitored most of the news sites, social media, and government websites to identify and predict the outbreak.

AI-assisted Surgery

Robots are endowed with the technology to analyze pre-op medical data and suggest the best instruments to use as well as new surgical techniques during an operation or surgery. This may seem very simple, but this information can actually help cut down a patient’s hospital stay. Considered invasive at the most minimal level, robot-assisted surgery ensures that a patient does not have to deal with healing with very large incisions. In a study involving a little over 300 orthopedic patients, it was revealed that AI-assisted robotic procedures helped in reducing complications five times as compared to instances where the surgeons were operating alone.

Virtual Nursing Assistants

From taking patient notes and assisting patients with the best clinical care possible to answering patient queries and monitoring patients, virtual nursing could massively cut down the operation cost in the healthcare sector. These super nurses have established a more transparent, streamlined, and effective mode of communication between patients and their healthcare providers. This has helped in preventing readmissions and reducing unnecessary hospital visits.

Automating Administrative Tasks

Another basic function where AI can contribute the maximum is by automating administrative tasks which are mostly repetitive in nature. AI-powered technologies and tools like voice-to-text transcriptions can do a number of manual tasks like writing notes, prescribing medicines, and helping to order tests. This can help administrators and staffers to focus on more productive tasks that will enhance patient service and patient experience.

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Improving Data Efficiency

The healthcare industry majorly deals with high-quality patient data and a lot of rides on this information. Today, doctors and other nursing staff majorly rely on patient data to set up appointments, plan follow-ups, prescribe medicines and so even a minuscule error can result in physicians making bad decisions, selecting treatment plans, etc. AI can help reduce any kind of subjectivity and put in place a more organized process based on wider data.

Automating Protocols

Another area where AI is largely helping physicians is the IVF process. Typically, to start the treatment, patients are injected with medication and this depends on various factors like age, weight, and image values. In a traditional setup, doctors would examine each patient’s data individually to recommend the protocol for treatment and change it later if necessary. With AI assistance, most of this process can be automated hence reducing the scope of trial and error. Once the patients provide data in the electronic medical record system, an AI algorithm report is generated containing details of which protocol faired the best in the past for patients who have similar data.

Final thoughts

Today, AI and ML are not replacing physician’s knowledge or limiting their decision-making process on their known subject, instead, they have the power to enhance the efficiency of the medical staff and nurses or add value by automating and simplifying most clinical procedures and improve the accuracy of diagnostics, reduce manual tasks and storing patient data.

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