Artificial Intelligence is Transforming Healthcare

Artificial Intelligence (AI) is no longer the future of healthcare – it’s here. AI is improving the clinical quality of care. It’s helping to improve the accuracy of diagnosis. The technology provides Alzheimer’s patients with assistance in daily living. There’s robotic support for measuring movement in physical therapy.

Artificial intelligence is using data to form neural networks and teaching machines how to learn. Right now.

Explaining AI

Artificial Intelligence is a system that simulates the activities of the human brain. AI technology mimics human behavior, including critical thinking, problem-solving, and learning.

The level of complexity is how AI is labeled, either as weak or strong. Weak AI involves the completion of a single task. Millions of Americans already use weak AI in their homes. Alexa and Siri are AI personal assistants who perform a simple service. Make a request and they answer it.

Strong AI handles more complex tasks. These are the applications that use learning to improve their performance, in the same way a human might. They are intended to problem-solve and support decision-making.

Movies like The Matrix, 2001, and War Games are part of the American culture, raising fears of smart machines that can’t be controlled. Those movies are fiction, but they do raise points about getting some regulations around the use of AI.

Branches of AI

Machine Learning: Machine learning is a key piece of how AI mimics human behavior. The computing systems and algorithms can learn from their mistakes. This allows them improve their performance. The machines integrate new data into the system to overcome obstacles and solve problems.

Natural Language Processing (NLP): NLP allows computers to recognize and converse with people. The purpose is break the communication barrier between human and computer. NLP learns to read, understand and use human language to converse with people.

Robotics: Robotics are multi-disciplinary tools that replace humans in completing repetitive tasks.  More sophisticated applications include tiny surgical robots implanted in the body.

Neural Networks: Neural networks are created to simulate the cognitive structure of the human brain. Neural networks and machine learning expand the ability of the system to perform tasks.

Expert Systems: An expert system is a classification. It indicates a computer system replicating the decision-making process of a human expert.

 AI neural network

Artificial Intelligence in Healthcare

Artificial intelligence is transforming healthcare.  AI is being used to reduce misdiagnosis and improve treatment for many major diseases. Surgical applications support minimally invasive operations. AI applications streamline administrative tasks.


Algorithms and machine learning improve the accuracy and speed of diagnosis. AI applications are being applied across all fields of medicine.

Caption Guidance: Heart Imaging

Caption Guidance is a new AI software. It enables medical professionals to capture echocardiographic images. The images of the patient’s heart are of acceptable diagnostic quality. The application visualizes 2D high-quality ultrasound images and records video clips. Doctors do not need specialized training – the application does the work. Caption Guidance was recently approved by the FDA.

AI Intake: Chatbots & Algorithms

An AI application called Buoy Health uses natural language processing. A chatbot engages the patients to capture symptoms. The algorithms run the symptoms to identify a preliminary diagnosis. The chatbot then guides the patient to the proper department. The application is presently in use at Harvard Medical School.

MelaFind: Identifying Melanoma

The application evaluates the condition of moles and other skin lesions. The irregular moles are scanned with infrared light. The algorithm analyzes the lesion. It looks for melanoma or other skin cancers. While it may not replace a biopsy, the early identification triggers preventative care.

AI Enhanced Microscopes

AI is expediting the early-stage identification of dangerous bacteria in the blood. Using microscopes enhanced by AI algorithms, scans can be completed at a faster rate than manual sample testing. Harmful bacteria like E. Coli or staph are diagnosed quickly to halt the progression. Over 25,000 blood samples were feed to the microscopes to learn to recognize the bacteria. The accuracy rate at a Harvard teaching hospital is 95%.


AI uses Robotics, natural language processing, and machine learning inpatient treatment. These are the drivers for new opportunities in improved patient outcomes.

robotic arm

Robotic-Assisted Stroke Therapy

AI helps stroke patients improve their range of motion. A robotic arm can detect limitations in patient movements during therapy. Those limitations are a target for improvement, improving the patient’s reach. The robotic arm can perform more movements per hour than with a therapist alone.  Patients regain normal use at a faster pace based on the targeted therapy. The improvement in range of use and motion helps stroke patients recover faster.

Precision Medicine 

Machine learning is behind the premise of precision medicine. The concept is to countermand the “one size fits all” treatment mentality. Precision medicine uses anonymized data to classify the criteria behind different treatment responses. The algorithm helps doctors to apply the best treatment protocol for each patient. Patients can avoid repeated trips to the doctor when standard treatments don’t achieve the desired outcomes. Precision medicine improves the quality of care and the patient experience.

As platforms compile more data the treatment options will be even more targeted. Precision medicine isn’t a one to one treatment. But as access to care becomes more virtual, it may be our future.

AI Virtual Assistants 

Weak AI applications perform simple tasks. But they can make a big difference for patients with Alzheimer’s disease. Devices with virtual assistants, like Alexa or Siri, provide reminders for patients.

Doctors can work with patients to manage their daily activities. These might include reminders to take medication or to eat lunch. Reminders for taking care of pets or personal grooming improve quality of life and prolong patient independence.


The use of robotics in surgery is well established in the United States. The operations are minimally invasive with a shorter post-recovery period.

Robotic Surgery Centers US

The reason behind AI robotic surgery is precision. The surgeon is human, the instruments are robots. The surgery itself is minimally invasive – meaning less scarring, shorter recovery. The doctor makes small incisions in the body in the surgical areas. Tiny robots and a camera are implanted into the incisions.

The robots have a high level of dexterity to perform delicate operations in hard to reach places. A human might not be able to do surgery this surgery so easily.

The camera feeds visibility to the surgeon. The magnified images are in high resolution, to view instruments and surrounding tissue. The surgeon commands the instruments, controlling the precise surgical movements required. The instruments cannot act without guidance from the surgeon.

Many conditions have been successfully treated using robotic-assisted surgery:

  • Colorectal Surgery
  • Gynecologic surgery
  • Heart surgery
  • Endometriosis
  • Thoracic surgery
  • Urologic surgery

We can also note that China is currently using supervised robots to perform surgery. The robots use machine learning algorithms to improve performance. They perform the operations under the supervision of a human surgeon. The goal is to build a robot as an expert system.


Healthcare is drowning in documentation. AI is a solution to reducing the human burden

EHR: Taming the Dragon

Electronic Health Records take up a huge amount of a physician’s time. Dragon is an AI software application. It is one of the most efficient computer-assisted documentation programs. The application offers real-time guidance for specificity while creating or updating charts. If a physician creates a vague note, the system will prompt them with a more specific option. One-click and the specification gets incorporated in the note.  Dragon has recently upped its analysis to include the entire patient file. Check out the video to learn more.

Treatment Planning with Watson

Data-driven healthcare is the Cleveland Clinic’s goal in partnering with IBM. The collaboration began by teaching AI supercomputer Watson to think like a doctor. It was built on Watson’s cognitive computing. The system reads and analyzes entire medical libraries with thousands of papers. Natural language processing communicates potential treatment plans. The research burden for doctors is reduced, while treatment options expand for physicians.

Identify Wasteful Non-Adherence

Some patients do not follow the prescription protocols. Sometimes it’s intentional and others accidental. The more medications they’re on the more apt they are not to adhere to the proper regimen. A new AI application is built to analyze adherence to reduce the amount of waste and improve patient outcomes.

The application aggregates health data and parses it by variables. Age, gender, geographic location, out of pocket costs, and payer type. A predictive model is created, allowing the physician to provide more supports for some patients.

Patient Flow Optimization

There is a new AI-based software platform, Qventus, manages patient logistics. The purpose is to get patients safely settled in a hospital environment, based on need.  The challenges of managing patient flow in the ER while maintaining patient safety is the goal of the system. The platform’s algorithm prioritizes by injury or illness in real-time. Qventus automates patient flow. It tracks patient wait times and finds the best routes for ambulance transport.

AI for COVID-19

The coronavirus pandemic forced the early adoption of technology. Remote monitoring, virtual consultations, and telemedicine were propelled into the mainstream. Many will become standard even after the virus is gone.

But how is Artificial Intelligence helping to track and treat COVID?

The COVID Cough

More people worry about having the virus, especially with the recent second surge. COVID symptoms mimic less drastic illnesses, like bronchitis, pneumonia, or the common cold. The University of Oklahoma began research into coughs in June.  Researchers built an AI application to identify coronavirus in the sound of a cough. Next in line was a paper from MIT. They used an algorithm to analyze the largest cough database available. They were able to differentiate asymptomatic people with a 100% detection rate.

Transmission Projection & Mortality Rates

AI algorithms are already in use to identify COVID clusters and hot spots. They are being adapted for contact tracing and monitoring of exposed individuals. These data predict the trajectory of the disease and the potential for reoccurrence. The model forecasts mortality rates by gender, age, and geolocation.

The system can forecast the risk of infection and likely spread. Predictive analytics identify the most vulnerable states, regions, and populations. Public health officials can respond accordingly.

Drug Discovery & Therapeutics

AI applications and platforms expedite the development of new pharmaceuticals and vaccines. The algorithm’s capability to aggregate and analyze data in real-time is unprecedented. The speed of the sampling and testing protocols are far beyond that of a manual process.

AI imaging scans identify areas in the lungs impacted by the virus. High-resolution output indicates how the body is responding to a pharmaceutical treatment. The speed of an AI platform does not compromise safety, which is critical in clinical trials.

Artificial Intelligence is Here

The use of AI is growing, especially for larger medical groups and hospitals. Over time AI applications will be more affordable. They will be accessible to smaller medical groups and practitioners. The opportunity to streamline operations and improve quality of care. AI will open up healthcare for doctor deserts and underserved communities. Neural networks and machine learning will build a foundation to grow knowledge. Healthcare will do more than collect data – we will use it to better patient outcomes.

In the coming years, healthcare and other industries will be transformed. The technology will continue to develop and break new ground. While we welcome innovation, we must ensure ethical controls are in place. AI requires oversight and regulation. We need to harness innovation for the betterment of our patients. But we must maintain the integrity of clinical guidelines and protocols.

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