1. What is AI in Healthcare?
2. Types of AI in Healthcare
3. Why Does AI in Healthcare Matter?
4. Where is AI Used in Healthcare? The 6 Exciting Applications
5. Ethical Challenges of AI in Healthcare
6. Final Thoughts

What is AI in Healthcare?

With the strong desire to become more accurate, efficient, and user-friendly present in all clinical settings, AI is a powerful tool to achieve these goals.

Nearly 80 AI algorithms are already approved by the Food and Drug Administration (FDA).

And this is only the beginning of the many exciting applications of AI in healthcare. With the power to augment our understanding, there is no limit to what we can do with advanced technologies in medicine.

Before we discuss some use cases of AI, what types of AI are contributing to these advances in healthcare?

Types of AI in Healthcare

Machine Learning in Healthcare

Machine learning algorithms that improve over time are capable of more accurately predicting healthcare outcomes. By processing huge volumes of data, ML technology can assist medical professionals in decision making, identifying diseases, and developing treatment plans.

A key use of machine learning technologies in healthcare is to predict what treatments are likely to be successful for a given patient, based on the context given.

Natural Language Processing

Natural language processing (NLP) is the branch of AI concerned with processing and manipulating spoken or written data that has been generated by humans.

The potential applications of NLP are endless, with this technology currently being used to recognize voice commands, organize written clinical data, and respond directly to patients via chatbots. But more on that later.

Robotic Process Automation

Robotic Process Automation (RPA) is the automated technology that can remove menial or repetitive tasks from the hands of professionals – a fantastic opportunity for healthcare organizations to increase efficiency, reduce costs, and ultimately provide better care to their patients in the process.

Use cases of RPA can include anything from basic admin tasks to processing billing and online communications.

Medical Robots

Physical robots have already been assisting with the provision of healthcare for some time. They’re already assisting with surgery, becoming prosthetics, cleaning, and assisting in pharmaceutical storerooms to retrieve the correct drugs.

These are just 4 key technologies that are being used in healthcare right now, but why does it matter that we keep moving towards AI-driven healthcare technology?

Why Does AI in Healthcare Matter?

How would you like to have your surgeon gain confidence through intelligent computer models that provide more clarity on your individual anatomy?

Or have an algorithm use your previous medical history to predict your likelihood of different adverse outcomes with greater certainty?

That’s exactly what some of the novel developments of AI in healthcare are achieving. Not only are AI technologies assisting in surgery and organizing medical data, they’re making health conditions and complications more predictable to prevent serious outcomes with the earliest warning signs.

Where is AI Used in Healthcare? The 6 Exciting Applications

Healthcare has been at the forefront of AI development for a number of years. Here are 6 areas in which AI is already transforming healthcare:

• Storing Electronic Health Data
• Automatic Image Analysis
• Predictive Analytics
• AI Chatbots
• Surgical Robots
• Medicine and Pregnancy

Storing Electronic Health Data

Over 80% of hospitals and physician practices are now using Electronic Health Records (EHR), which have revolutionized medical documentation and collaboration.

With the ability of EHRs to digitally store countless pieces of patient medical data, is the need for a system to sift through all of that information – enter AI technology.

With the help of AI, important information can be automatically pulled from countless types of clinical notes, diagnoses, order entry, and billing to be simultaneously placed into the hands of medical providers.

In 2018, Amazon launched their Comprehend Medical system based on natural language processing (NLP) and machine learning (ML). Their NLP model is trained to read medical documents and audio transcripts, while the ML algorithm extracts the relevant information and transforms it into an easy-to-read format for physicians.

Not only that, AI software can work alongside physicians and patients to transcribe conversations in real-time and automatically upload information to the appropriate section of a patient’s EHR. Some programs like DeepScribe’s AI average less than one edit per note – hugely reducing time spent on documentation and improving accuracy.

Cloud-based EHRs also allow that same patient data to be shared securely and quickly between medical providers or public health agencies with minimal error.

Throughout the COVID-19 pandemic, burnout among medical professionals gained global attention and efforts are being made to address the problem that has been occurring for years with technology that makes some efforts to reduce their heavy workload.

Time is of the essence in medicine, and AI allows the allocation of time towards tasks of the greatest priority.

AI can even be used to draw information from wearables into the EHR. Studies have highlighted the benefits of using technological vital systems such as electronic blood pressure cuffs, Apple Watches, and Fitbits to automatically upload a patient’s vitals into their EHR.

Physicians are even leveraging the AI technology in wearables for the detection of atrial fibrillation and other diseases by monitoring cardiovascular data.

Automatic Image Analysis

72.4% of AI-based FDA-approved medical devices and algorithms are innovations in radiology.

It can be difficult to detect adverse outcomes on imaging and scans and an immense amount of specialized training is required to do it correctly. The timeline for diagnosis and intervention relies upon the ability of medical professionals to quickly and accurately read the images.

Through structure recognition training, AI technology can help radiologists identify difficult-to-detect fractures that may otherwise have gone undetected by alerting them to structures, abnormalities, or details that may not be visible at first glance.

Hip fractures can sometimes be particularly challenging. Over 300,000 people over 65 are hospitalized every year in the US for hip fractures diagnosed using x-rays. But when the fracture is hidden under soft tissue, it can be difficult for radiologists to detect and treat it.

A study published in the European Journal of Radiology describes the use of Deep Convolutional Neural Networks (DCNN) to help radiologists identify hip fractures.

With the help of this technology, Radiologists reading hip x-rays were able to spot 88% of fractures, versus 83% without.

Other chatbots like Florence remind patients to take their medications through Facebook Messenger, Skype, or Kik. After inputting your medication, dosage, and frequency, Florence will message you when you need to take the pill. With patient input, Florence can also monitor body weight, mood, or menstrual cycle.

In the future, chatbots like Ada Health are expected to become the first point of contact for primary care. Answering basic questions, documenting complaints or concerns, offering solutions for simple medical issues, and managing appointments to free up time for medical providers to spend with the patients who need it.

These convenient pocket medical providers can also actually help increase people’s willingness to monitor their health by making it so easy, enabling greater predictive and proactive care in the long run.

The possibilities for chatbots in healthcare are endless.

Surgical Robots in Healthcare

AI has played a major role in the intelligence and automation of medical robots. Beyond simpler tasks like taking a patient’s vitals or reading clinical notes, there has been a shift towards developing robots for surgical procedures that require less invasive, more patient-tailored methods.

Over 50% of pregnant women take at least one prescription drug at some point in their pregnancy.

Without randomized controlled trials to collect the type of data essential to ensuring that medication is safe for women and their children, another method of determining safety is crucial.

That’s where retrospective analysis comes in – using AI to evaluate past data without putting participants in any future danger or medical uncertainty.

AI methods to analyze electronic health records can aid in the lack of knowledge about pregnancy and pharmaceutical consequences, ultimately helping pregnant women and their doctors make more informed medical decisions.

Instead of randomized controlled trials, a retrospective design can be used to look at what has happened in the past and why. Public registries have made this easier by providing longitudinal data of complex cases that would be time-consuming to compile retrospectively.

Ethical Challenges of AI in Healthcare

With any model mimicking human thoughts and behaviors, we must always pay extra attention to ethics.

When training models that determine important, high-priority details to predict outcomes and potentially save lives, it is crucial that focus is paid to the quality of data used in training.

Entered data should be accurate, unbiased, and not reflect the disparities we unfortunately still see in the medical field. Entering biased data into AI models will only create prejudiced outputs, which perpetuates the racism-based imbalance of power between patients and providers.

This prejudice is exactly what we are trying to prevent by creating these models in the first place.

Final Thoughts

AI and ML are not necessarily meant to replace human tasks, threaten our job security, or threaten our purpose. Instead, they can be used to reimagine what is possible and make work previously conducted solely by humans easier, more accurate, and more equitable.

AI-driven advances are freeing up more time for medical professionals and researchers to focus on tasks that need human intervention and driving more informed medical decisions that have a huge impact on improving the outcomes for patients.

Researchers across the globe are constantly working on new and exciting applications of AI in healthcare, progressing the field of medicine every day and ultimately helping to provide better care to patients.

As AI technology continues to expand into new realms of medicine, there are no limits to its potential benefits.

Thirona, a Dutch AI medical image analysis company, has licensed a patent allowing it to develop an AI algorithm that helps identify abnormalities in CT lung scans, ensuring early detection of Cystic Fibrosis (CF). The company will use the license to integrate a quantitative method for the analysis of chest CT scans as well

With only approximately 70,000 known cases every year, CF is a rare genetic disorder. Current clinical methods to detect the disease are both time consuming and require extensive training. Thirona lets clinicians automatically detect and quantify lung disease, improve care, and enhance clinical decision-making

This is a competitive space. One of Thirona’s competitors, NuvoAir recently shared three key pulmonology related studies presented at the 2020 North American Cystic Fibrosis (CF) conference showing that patients with the respiratory disease can achieve good quality spirometry at home through their platform

“Artificial intelligence algorithms make medical image analysis easier, more accurate and more effective. But it is hard work,” said Dr Eva van Rikxoort, managing director of Thirona. Our algorithm quickly and accurately detects “bronchiectasis and mucous quantifications in very young patients with CF, making personalized treatment easier. We believe this will have a substantial impact for patients and are proud to be part of the process,” she added.

What is Atrial Fibrillation?

Atrial Fibrillation is a heart condition that causes irregular and often a fast heart rate. It’s unclear exactly what causes it, but we do know that it’s common in patients with other existing heart conditions.

How Are Wearables Helping with Detection?

On May 16, 2019, Preventice Solutions presented clinical data validating its BodyGuardian Remote Monitoring System with the BeatLogic deep learning platform at Heart Rhythm 2019 – the Heart Rhythm Society’s (HRS) 40th Annual Scientific Sessions in San Francisco.

This technology leverages machine learning and artificial intelligence (AI) for detection of atrial fibrillation (AF) and was validated using clinician adjudicated data. The system is designed to create a constant connection to monitor cardiovascular data in patients in their daily lives.

Today, AI, machine learning, and deep learning tools allow physicians to manage the massive amount of data produced on wearables and other sensor technology to track patient data, then diagnose health problems like AF, arrhythmia and hemodynamic instability.

What Does This Mean for Healthcare?

Without these AI technologies in healthcare, it would be impossible for physicians to manage and review the massive amounts of data collected on patients by wearables. With AI accurately identifying atrial fibrillation episodes, physicians can focus on the unique treatment for each patient.

Results from the study show how BeatLogic accurately detects the beginning and end of arrhythmias, ensuring accurate burden calculations while maximizing clinical value.

The platform leverages multiple deep neural networks to detect AF episodes at rates that meet or exceed the best reported values within the literature. Perfect detection performance was achieved for AF episodes lasting more than one minute.

Wearable patch electrocardiogram (ECG) monitoring devices combined with deep learning algorithms help detect the beginning and end of AF episodes, providing physicians with important clinical context for determining the appropriate treatment approach.

On June 21, 2021, Sensome, a pioneer in connected medical devices, announced that its Clotild^® Smart Guidewire System achieved the Breakthrough Device designation by the Center of Devices and Radiological Health (CDRH) of the Food and Drug Administration (FDA). The revolutionary sensor technology uses machine learning algorithms to instantly identify biological tissues while turning invasive medical devices into connected healthcare devices.

Although ischemic stroke is a leading cause of long-term disability it can be treated by removing the clot blocking the brain blood vessel, but the clot needs to be removed as quickly as possible. Without knowing a clot’s unique characteristics, Clotild^® Smart Guidewire System can remove the clot on the first attempt in one out of three cases. Its AI-powered tissue sensor provides physicians with critical information about the clot.

The FDA Breakthrough Devices Program is a voluntary program for medical devices that provide more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions. It is intended to help patients receive more timely access to breakthrough technologies and, in 2021, other breakthrough technologies have included batteries, implants, catheters, and ultrasound equipment. As part of this program, the FDA will provide Sensome with priority review and interactive communication on its path from device development to commercialization.

Lung cancer is the deadliest cancer in the world and about 75% of those are diagnosed with it will die within five years of discovery. Most patients are only diagnosed after the disease has advanced to the serious Stage III or IV level. However, if tumors are small and confined to the lung, the survival rate increases substantially. The need for early detection is clear and AI is powering the development of systems that can detect ever-smaller lung tumors.

Optellum, a lung health company aiming to redefine early diagnosis and treatment of lung disease, has received FDA 510(k) clearance for Virtual Nodule Clinic, a revolutionary AI-powered clinical decision support system that helps pulmonologists and radiologists uncover early stages of lung cancer. This is the FDA’s first application of AI decision support for early lung cancer diagnosis.

The world’s first FDA-cleared imaging AI/” Radiomics”-based digital biomarker for lung cancer, Virtual Nodule Clinic helps pulmonologists identify and track at-risk patients with suspicious lung nodules, then make optimal clinical management decisions for them. The software provides a clinically validated Lung Cancer Prediction (LCP), which is computed from full patterns of 3D pixels in standard CT image scans.

Use of the Virtual Nodule Clinic dramatically improves diagnostic accuracy and clinical decision-making. In the clinical study underpinning the FDA clearance, all readers in the study demonstrated a statistically significant improvement in diagnosing lung nodules when using the system, which consistently outperformed conventional risk prediction models. In an independent validation study, the AI correctly reclassified indeterminate nodules into high- and low-risk categories in more than a third of cancers and benign nodules, demonstrating the potential to speed up lung cancer diagnosis and reduce invasive biopsies and surgeries on patients without lung cancer.

Today’s AI systems utilize deep learning systems analyze real-world tumor examples and evaluate which constitute a problematic one, rather than looking for pre-defined features. The AI system is given a large data set of lung CT scan images, some with cancer and some without, and the machine learns what a lung cancer nodule looks in extraordinary detail. These systems can help radiologist overcome the limits of human vision, which can miss tiny malignant lesions that signify cancerous growth. Currently, up to 35% of lung nodules go unnoticed during initial screenings and AI systems can both discover hard to find lung spots as well as reduce the burden of overwhelming caseloads on busy specialists.

These developments promise to make lung-cancer screenings more precise and accessible to all, with AI should be leading the way for all.

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