There is nothing artificial about the impact AI is having on the medical industry. By 2025, artificial intelligence applications in healthcare will represent a $34 billion market.
Revenue in the lucrative AI healthcare space will be spread across a wide swath of applications, from data security to streamlined workflows.
The phenomenal growth of artificial intelligence in healthcare is due to the wealth of benefits AI offers care providers and their patients. AI can identify complex patterns within raw data. It can learn on its own and rewrite its own algorithms. And it can predict outcomes. Combined, these capabilities add up to a technology that will disrupt and transform an entire industry.
AI Applications in Healthcare
The medical industry lends itself to hundreds of applications for AI, if not more. In this article, we will explore 10 uses getting the most attention at the moment.
The AI healthcare applications we will cover are:
- AI-assisted robotic surgery
- Virtual nursing assistants
- AI-assisted medical diagnoses
- Medical image analysis
- Drug discovery
- Automated workflow assistance
- Fraud detection
- Medical data security
- Medical risk prediction
- Clinical trials
As the number of applications for artificial intelligence in medicine increases, so will the effectiveness of AI solutions. The ability of AI to learn and rewrite its own rules, through deep learning, offers not only benefits for today, but yet unseen capabilities tomorrow.
AI-Assisted Robotic Surgery
Surgical robots have been around for nearly 30 years. The Westinghouse PUMA 2000
was used to place a needle during a CT-guided brain biopsy. Since then, robots have worked alongside of surgeons, and served as their remote arms from halfway around the world via telesurgery.
Until recently, however, surgical robots were only extensions of the surgeon’s own hands. They were not capable of acting independently, and, indeed, had no mind to do so.
AI Empowers Surgical Robots
In usual practice, robots wield the surgical tools, which are guided by hand controls manipulated by the surgeon. AI changes the surgical robot from a slave device to an active partner in the surgical treatment.
While the AI robot is not quite ready to throw the surgeon out of the operating room, it does bring its own talents to the operating table. Namely:
- Reducing micro-trimmers that are present in surgeons’ hands, especially during neurosurgery.
- Reducing variations in surgical procedures that occur from surgeon to surgeon.
- Automating less-critical tasks such as suturing.
- Providing a more positive patient outcome.
- Collecting data from each surgery that can improve future procedures.
One day, robots guided by machine learning technology might be the only surgeon in the room. Till then, intelligent robots will help great doctors to ensure a great outcome for their patients.
Virtual Nursing Assistants
The bedside nurse is going nowhere soon. However, AI offers care providers a way to add a new layer of care and monitoring to the treatment they offer their patients.
You might consider virtual nursing assistants as intelligent chatbots, powered by AI and focused entirely on providing support for medical patients.
Benefits of AI Digital Nursing Assistants
One such product is Angel, an AI-driven virtual nursing assistant application that can listen, talk, make decisions, and give advice.
Applications such as Angel can follow a prescribed program designed by the doctor in order to provide proper care for the patient. The virtual nursing assistant can call patients, ask if they are taking their medications as prescribed, answer questions, and alert the doctor if the patient is having problems.
Advantages of AI-Powered Virtual Nursing Assistants
Virtual nursing assistants running on AI technology can provide a range of benefits, including these:
- Extending patient care outside of the clinical setting.
- Providing an improved patient experience.
- Monitoring patients to ensure they are following doctors’ instructions.
- Scaling care capacity without reducing care quality.
- Improving quality and medical outcomes.
In time, AI and it’s more intelligent subset, machine learning, will connect to more and more nodes within the clinical environment. Along with its increased connections will come an increased capacity for AI to offer care and comfort to the sick and ailing.
AI-Assisted Medical Diagnoses
Enough can’t be said about the promise AI offers for improved medical diagnoses. AI algorithms have been proven to outperform doctors in the speed and accuracy at which they can diagnose disease.
AI Already Diagnosing Disease
John Radcliffe Hospital in Oxford, England, has developed Ultromics — a diagnostic platform powered by AI. Preliminary tests suggest that Ultromics greatly outperforms surgeons in diagnosing heart disease.
Similarly, startup Optellum has developed another AI diagnostic platform. Optellum promises to diagnose 4,000 more cases of lung cancer per year than doctors, alone, can do.
AI diagnostics offers a multitude of advantages, including:
- Improved speed and accuracy in general disease diagnosis.
- Bringing diagnostics to remote locations where doctors are in short supply.
- Enhanced pathological results based on AI analysis of tissue.
- Enhanced oncological detection at accuracies equivalent to that of a trained doctor.
- Reduced cost for diagnostic procedures.
These are but a few of the benefits of using AI in medicine. Even so, it is clear that deep learning can make the difference between life and death for a growing number of patients.
Medical Image Analysis
AI is more than a number-crunching beast. It also likes to look at pictures. In healthcare, AI’s power to process images is saving lives for thousands.
AI Lends Powerful Analysis to Medical Imagery
Thanks to cloud computing and deep learning, AI-based medical image analysis platforms are now a reality. Platforms such as Arterys give doctors powerful tools for analyzing medical images.
The benefits of AI in medical image analysis would fill an entire article. A summary of those benefits include:
- Enhanced 2D and 3D imaging provides valuable information on the condition of the heart, liver, lungs, and other organs.
- Deep learning software reduces time required to complete medical imaging procedures.
- Cloud-based computing allows different AI software to analyze the same medical images for more detailed diagnoses.
- Diagnostic scoring of medical images suggests disease probabilities.
- Automated workflows reduce error and improve the efficiency of medical image diagnoses.
We’ve always heard that a picture is worth more than a thousand words. Now AI medical imaging technology proves it.
Drug Discovery
Drug discovery might not be the most exciting topic when discussing medical technology. It is generally the benefits dugs offer that gets the headlines. But AI is making the process of finding new medications anything but boring.
With 9 out of 10 potential medications never reaching market, AI brings welcomed insight to the challenges of drug research.
AI Aids in Discovery of New Medications
A number of companies are working alone or in partnerships to discover new drugs using AI technology. A few include:
- Pfizer and IBM partner to use the Watson AI platform to accelerate drug discovery for Immuno-oncology treatments.
- Genentech, a member of the Roche Group, has announced it is collaborating with Cambridge-based GNS Healthcare. The partnership will focus on finding new drug candidates.
- U.K.-based Rosalind Franklin Institute has invested £6m in drug discovery projects. The research will focus in using disruptive technologies such as AI to find new drugs and other treatments.
- Last year, GlaxoSmithKline and Exscientia partnered up in a £33 million project to discover drug-promising molecules using Exscientia’s AI platform.
- Novartis and Intel are working together to accelerate screening of cellular phenotypes, an early crucial step in the process of drug discovery.
The cost of discovering each successful drug is nearly $2.6 billion, and it takes an average of 12 years to get it to market. If AI can improve the success rates and speed up the drug discovery process, it is well worth whatever investment it requires.
Automated Workflow Assistance
No industry on earth processes more information on a daily basis than the healthcare industry. Keeping reports, test results, drug prescriptions, medical images, and insurance information moving is key to operational efficiency.
Before a hospital can even think about efficiency, it must think of accuracy. Data must go from the source to the proper destination without error, or efficiency goes out the window.
Managing the workflow of a large clinical environment is one of the greatest challenges care providers face. It’s one thing to have written processes that guide workflows. Ensuring that those processes are flexible and that they are followed is another challenge.
Once again, AI to the rescue.
AI Office Automation
AI provides powerful solutions for administration of a clinical environment. With a number of solutions on the market already, machine learning is proving its value.
Depending on the platform used, AI-based workflow software can offer the following benefits:
- Interpreting clinical documents and filing them according to content and purpose.
- Automating administrative tasks such as patient scheduling.
- Monitoring the efficiency of staff and in-office processes.
- Using predictive analysis to forecast workflow bottlenecks.
- Assisting with coordination between care providers or departments.
AI automation does not replace the receptionist, or secretary, or office administrator. It does make their jobs easier by automating certain tasks, while providing assistance with others.
Fraud Detection
Healthcare fraud is the intentional filing of false medical claims for profit. Fraud costs insurance companies more than $1 billion per year, with those cost ultimalet being passed on to the consumer.
AI Fraud Detection Solutions for Healthcare
Preventing healthcare fraud requires digital solutions that can identify unusual patterns, quickly process raw data, and “learn” as it goes. Only artificial intelligence with deep learning is capable of meeting these challenges.
Healthcare fraud comes in many forms. Here is just a partial list:
- Billing insurance for medical services that were not rendered.
- Providing unnecessary services to patients.
- Upcoding of services to receive higher payment for services provided.
- Filing of duplicate claims.
- Providing excessive services.
One example of an AI-based healthcare security solution is KironAi. The cloud-based platform uses medical data to teach itself what patterns are normal. Unlike other technologies, KironAi can spot unusual activities that indicate fraud quickly — in some cases before the transaction even completes.
Medical Data Security
Did you know that your medical information is worth 10-40 times more than you credit card information to criminals? The reason is that a credit card is good for what can be charged on it. Medical records are good for much more, such as stealing your identity.
The value of medical records has resulted in major data breaches over the last few years. The recurring problem of medical record theft requires new solutions, if trust in the medical industry is to be maintained.
AI Medical Data Security Solutions
AI provides just the tools needed to stop medical record thieves in their tracks. Here are some of the ways AI gives care providers an edge in protecting your medical data:
- Traditional security solutions look for signatures of known malware. AI takes a proactive approach by identifying patterns that lead to previously-unknown malware.
- By spotting exceptions to normal network traffic, AI can identify intrusion attempts and shut the medical network down quickly.
- Since patient safety depends on the security of medical devices, such as heart monitors, securing these devices is crucial. AI addresses this problem by monitoring the hospital IoT infrastructure for malware.
- Responding to a cyberattack requires in-depth analysis of the vulnerability that allowed it to happen. When breaches do occur, no solution can match AI in determining the root cause.
- AI can be trusted to automate many tasks usually performed by humans. While a security engineer can monitor the security for one hospital, an AI security solution can monitor dozens.
More than 15,000 medical records are compromised every day. Turning the tide on cyber criminals requires not only AI, but machine learning and deep learning technologies.
Medical Risk Prediction
If there is one thing you would probably rather AI not do, it is to predict your death. Unfortunately, it can.
Google Healthcare AI
Google’s Medical Brain team has developed an artificial neural network that analyzes patient data and makes a number of subsequent predictions.
In one newsworthy case, Google pedictied a high likelihood of a patient dying in the hospital, when the hospital’s augmented Early Warning Rating (aEWR) indicated only a slight chance. Google won. The patient died within 24 hours.
Google studies also proved the power of AI in detecting and predicting disease using retinal scans. Using deep learning, Google AI provided early diagnosis of diabetic retinopathy and cardiovascular events. Early detection of each of these is crucial to prevention and effective treatment.
Fortunately, AI can do more than predict bad news, it can also predict positive outcomes and help make them happen.
AI-powered medical risks software, like the Google platform and others, can provide care providers with a host of stats, including the following:
- How long patients will stay in the hospital.
- The odds of patients being readmitted to the hospital.
- Probability of disease occurrence, in both short and long term.
- Success rate predictions for various treatment plans.
- Patient mortality probability.
The true value in AI predictive analytics in healthcare is its ability to identify persons of high risk for developing certain diseases. The ability to identify high risk patients leads to early detection and more positive outcomes.
AI in Clinical Trials
Clinical trials are all about data. And data doesn’t only become important once the trial has started. Even the selection of trial participants requires careful analysis of each individual to ensure they meet the criteria needed for the trial.
AI Benefits Clinical Trials
Know what else is all about data? AI. And it is transforming how medical trial candidates are selected, how trials are conducted, and how results are analyzed.
What makes AI useful in clinical trials comes down to three things: its ability to perform predictive analysis, its ability to adapt to changing criteria, and its ties to big data.
Here are some of the many advantages AI offers researchers when conducting clinical trials:
- The ability to access and process raw real world data (RWD).
- Predictive analytics, which can predict trial outcomes based on varied criteria.
- IoT connectivity, enabling participants to be monitored throughout the trial using wearable devices.
- Detection of non-compliance by participants.
- The ability to identify minute factors that can affect trial results, but which would escape human observation.
Clinical trials are an important part of medical research. Successful trials result in the release of new medications, new treatments, and a deeper understanding of how to care for patients.
Through the power of AI, clinical trials can now be conducted less expensively, more efficiently, and with more accurate results.
Why Ignite?
We have discussed a few uses of artificial intelligence in medicine. There are lots more. Each of them presents benefits to the patient and caregivers alike. Each also presents an opportunity for innovators to carve out a space in this lucrative market.
Problem is, developing AI solutions for the medical field requires unique expertise.
That’s why Ignite should matter to you.
If your verticals include AI in healthcare applications, you need a technology partner that knows the way.
Ignite provides world-class outsource development of cutting-edge solutions, including those you will need to tap the medical marketplace.
Why not contact us for a no-cost consultation?
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