27 Companies Changing Health Outcomes Through AI

Artificial intelligence in healthcare can help cut costs of on-going health operations and impact the quality of care for patients everywhere. And by diagnosing diseases early, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has huge potential in healthcare. In this comprehensive guide, we shall cover two primary use cases of artificial intelligence in healthcare: Clinical Decision Support and Information Management.

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How is artificial intelligence used in the medical field today?

Despite being years from having its greatest impact, Artificial Intelligence is already making major inroads in healthcare, influencing innovations and boosting efficiency in just about every facet of the industry. At the very core, AI is making healthcare facilities more efficient and improving the lives providers and patients by automating tasks for a fraction of the cost and time. Speaking of numbers, healthcare AI will undoubtedly become one of the most rapidly growing investments in the industry. While health AI industry valuation stood at $600 million back in 2014, it’s expected to hit a whopping $150 billion by 2026. Interestingly, AI applications in healthcare are eclectic, ranging from surgery-aide robots to faster discovery of drugs, and everything in between. What’s more, these AI applications in healthcare go beyond treatments and diagnostics, covering every bit of the industry, from service delivery to supply chain management. If you are interested in seeing how AI is transforming pharma, be sure to check this in-depth article from Healthcareweekly. There are plenty of benefits that come with embracing the role of artificial intelligence in healthcare – and investing in it. For starters, AI has proven to be quite effective and accurate. One study by Mayo Clinic discovered that incorporating AI into a widely available and cost-effective EKG (electrocardiogram) can enable doctors to detect early signs of asymptomatic left ventricular dysfunction with a high level of accuracy. This is a well-known precursor to cardiovascular conditions like heart failure. Another team of scientists at Moorfields Eye Hospital in London, in collaboration with researchers at UCL (a subsidiary of Alphabet’s DeepMind), has found that AI-driven software can sift through 3D scans to pinpoint up to 50 common eye diseases as accurately as the best doctors.

     Artificial Intelligence & clinical decision support

In the era of big data, artificial intelligence powered clinical decision support systems can be designed to enhance the quality and accuracy of the decision-making process in healthcare. In other words, AI-driven software engines can be used to leverage to improve diagnosis and help doctors make better, more informed decisions faster. The endgame here is to simply complex processes and make it easier to arrive at more conclusive and precise medical decisions.

     Artificial Intelligence & medical information management

Hospitals, clinical research centers, and other healthcare facilities gather and store a ton of patient, financial, and workforce data. By using AI to better manage information and knowledge in a healthcare environment, patients can get to doctors faster or receive top-notch consultation and care virtually. More importantly, doctors can learn through AI-driven educational modules. Before we delve into the nitty-gritty of these use cases, let’s take a quick look at the state of artificial intelligence in healthcare.

1. Artificial Intelligence (AI) in the healthcare market

As mentioned earlier, artificial intelligence in healthcare is one of the world’s fastest growing sectors. Fueled by increasing acquisition of artificial intelligence startups, the health AI market is poised to experience an explosive compound annual growth rate of around 40%, to reach $6.6 billion by 2022 from a mere $600 million in 2014. If you crunch the numbers, that means health AI sector will have experienced an 11x growth by 2022. And that’s just but the tip of the iceberg. According to an Accenture report, healthcare AI applications can potentially generate more than $150 billion in yearly savings for the US healthcare economy by 2026. Today, AI applications in the healthcare industry are a dime a dozen, but here are the top ones: Robot-assisted surgery is the most lucrative AI application in healthcare, valued at a staggering $40 billion. With groundbreaking innovations already in place, these robotic assistants are poised to transform not just surgery but also the entire healthcare spectrum. Next up is Virtual Nursing Assistants, an AI application that’s estimated to be worth more than $20 billion by 2026. Check out my podcast interview with Helpsy’s CEO Sangeeta Agarwal, a company which has developed the first virtual assistant for cancer patients if you’re interested in this topic. In addition, healthcare Administrative Workflow Assistance is forecasted to be valued at $18 billion in the next 7. Other AI applications that will present a huge opportunity in healthcare include Fraud Detection ($17 billion), Dosage Error Reduction ($16 billion), Connected Machines (Medical IoT, valued at $14 billion), Clinical Trial Participant Identifier ($13 billion), Preliminary Diagnosis ($5 billion), Automated Image Diagnosis ($3 billion), and Cybersecurity ($2 billion). As Accenture puts it, artificial intelligence is fast evolving into the “new OS in healthcare.” As it penetrates further into the veins of healthcare, there are 4 key areas that will reap more benefits of AI:

• Employees – AI has the potential to lighten the burden on the medical workforce and provide clinicians with powerful tools to do their jobs better.
• Consumers – Patients are eager to see what AI has in store for them. In fact, according to a recent survey, consumers are 6x more likely to see artificial intelligence as making a positive impact on communities than any other type of technology. More specifically, consumers expect that AI will improve health quality and make the patient experience holistic and seamless.
• Security – In the age where cyber attacks are commonplace, healthcare companies lose an average of $455 per breach for each individual record that is compromised. Of course, they don’t just lose money, they also suffer a consumer trust loss. If applied creatively, AI can bring greater organization, interoperability, transparency, and efficiency, the sum total of which leads to more focus on healthcare data security protection.
• Institutional Readiness – Healthcare organizations can integrate AI into their governance and structure. Specifically, they can enable an AI-smart workplace culture and workforce that will leverage the technology to improve service quality, patient outcomes, and overall efficiency.

2. AI in healthcare – known current limitations

True, artificial intelligence in healthcare is surging thanks to a number of enabling factors, including the availability of copious amounts of healthcare data, the patient shift towards consumerism, increased focus on delivery, and better digital storage capabilities. However, it’s not all rosy in healthcare AI market; artificial intelligence in healthcare does come with its fair share of hurdles and challenges.

• The Problem with Out-of-the-Box AI solutions – AI in healthcare usually delivers black box solutions. However, in the medical arena, getting the answer to “why” questions is of critical importance. Doctors, physicians, and other care providers want to know the reasoning behind a recommendation or diagnosis. This is a crucial step in setting up and long-term or effective treatment plan. After all, the final word rests with the doctor.
• Data Security Breaches and Privacy Issues – healthcare data is usually made of very sensitive patient information. And, therefore, appropriate security measures must be adopted by organizations to ensure that patient information is protected against cyber attacks and that only relevant people should have access to them. This can be a stumbling block for implementation of AI application in healthcare.
• Startup Issues – Most AI companies crafting solutions for the healthcare industry fall under the umbrella of startups. Oftentimes that means there’ll be adoption problems initially as use case studies are documented and large healthcare organizations start to ‘trust’ the AI. Before then, most stakeholders and investors will be hesitant to leverage artificial intelligence until they are furnished with feasibility and applicability studies.
• Compliance and Regulation Issues – Handling healthcare/patient data means that AI companies have to be compliant with several different laws and regulations like HIPAA, HITRUST, ISO, and so forth. The whole process of bringing their data collection, storage, and use practices up to these codes can be expensive and might take a huge chunk of crucial time they could have taken the product to market.

Proponents of Artificial intelligence in healthcare may also have to wrestle with stakeholder complexities, and have to play their cards right. They have to build AI products that are agreeable with every stakeholder, including hospitals, insurers, big pharma, healthcare workers, clinicians, and patients.

Examples of Artificial Intelligence in healthcare

     1. Clinical Decision Support

Making sure that AI solutions make correct diagnoses is a crucial step in healthcare. However, medical diagnosis in and of itself is an intricate and highly collaborative process that’s prone to errors and inconsistencies. In fact, delayed or inaccurate diagnoses (summarily called diagnostic errors) have been rampant in just about every setting in healthcare and continue to cause harm to an unfortunately high number of patients. For instance, postmortem reviews which have been conducted over the years have revealed that diagnostic errors lead to around 10 percent of all deaths in the US. That means an estimated 40000 to 80000 people die in America each year because of diagnostic errors. As if that isn’t worrying enough, 5 percent of adult Americans who visit the doctor’s office annually experience a diagnostic error. Even worse, more than 60 percent of all medical errors are deemed to be diagnostic errors, accounting for 6-17 percent of all adverse events in healthcare. The ramifications for diagnostic errors are dire, and affect nearly everyone in healthcare, from the patient to the hospital itself. For patients, a diagnostic error puts their health and well-being in jeopardy because it delays or prevents proper treatment, and can result in emotional, physical, mental, and financial repercussions. Diagnostic errors also result in the most common healthcare malpractice claims, which is not surprising considering that these errors are 2x more likely to cause patient’s death in comparison to other types of claims. All in all, diagnostic error claims also account for the largest percentage of total malpractice settlements. Given the gravity of diagnostic errors, the potential of improving the whole diagnostic process is one of the most exhilarating application areas where artificial intelligence is expected to cause a seismic shift in healthcare. From diagnosing early diabetics to cancer, here are the top examples that depict how artificial intelligence is helping physicians and clinicians reduce diagnostic errors and deliver better treatments and care plans.

     2. IDx: Diabetic Retinopathy with AI

Location: Coralville, IA How IDx is using artificial intelligence in healthcare: The Coralville-based firm has developed an AI-powered diagnostic system that allows clinicians to detect diabetic retinopathy by simply studying a batch of images of the retina. The company’s flagship AI-powered product, IDx-DR, achieves an incredible 90% specificity, 87% sensitivity, and 95% imageability. In only one minute, the diagnostic tool detects either the absence or presence of the eye condition that’s caused when high blood sugar destroys the blood vessels in the back of the eye. The artificial intelligence-based software has received FDA approval to be used in the US and is already in use in one big healthcare facility – University of Iowa Hospitals and Clinics.

     3. Apple: Atrial Fibrillation (Afib) Detection

Location: Cupertino, CA How Apple is using artificial intelligence in healthcare: The company’s AI sensor built into their flagship wearable, Apple Watch Series, leverages the power of artificial intelligence to detect atrial fibrillation, a cardiovascular condition that’s characterized by irregular heartbeats. Apple’s ECG app enables users to take an electrocardiogram, detecting any skipped, rapid or errant heartbeats. By monitoring your heartbeat in the background, the app can notify you if the abnormal heartbeat rhythms seem to be a sign of Afib, allowing you to seek medical attention early. Apple’s Afib detection AI-driven app is quite useful because the condition usually goes unnoticed, especially in the younger population. The Center for Disease Control estimates atrial fibrillation can affect close to 2 percent of this group, and up 9 percent of Americans older than 65. If it goes untreated, Afib can eventually lead to a stroke or heart attack. Currently, this FDA-approved software is geared towards users aged 55 and under, becoming one of the best and the most accessible FDA-approved personal ECG takers on the market.

     4. Aidoc: CT Brain Bleed Diagnosis

Location: Tel Aviv, Israel

How Aidoc is using artificial intelligence in healthcare: Aidoc has developed an artificial intelligence powered brain solution that sifts through Computed Tomography (CT) scans to help flag cases of internal brain bleeding. The clinical decision support system leverages the power of deep learning and AI to help radiologists triage patients with potential cases of acute intracranial hemorrhage (ICH). While it received the Food & Drug Administration nod in August of 2018, the workflow AI solution for radiology has been commercialized in other countries since 2017. By detecting internal bleeding more precisely and quickly, this is a godsend solution that can help doctors diagnose and treat brain hemorrhages much faster and more effectively.

     5. iCADL Breast Density Assessment via Mammography

Location: Nashua, NH

How iCADL is using artificial intelligence in healthcare: iCAD using a combination of Big Data and AI to help radiologists better assess breast density based on the standard range of categories of BI-RADS. The AI software can help detect breast cancer more accurately in women with dense breasts. How so? Given that mammography sensitivity dials down from normal 98 percent to around 48 percent in women with dense tissue in their breasts (the case for between 40 and 50 percent of American women), iCAD solution augments with artificial intelligence to recommend further screening tests, including breast tomosynthesis. “iCAD’s PowerLook Density Assessment 3.4 delivers automated, rapid and reproducible assessments of breast density to help identify patients that may experience reduced sensitivity to digital mammography due to dense breast tissue,” stated Ken Ferry, the CEO of iCAD. Aptly named iReveal, the solution has already received FDA clearance for AI assessment of breast density.

     6. QuantX: Detection of Breast Lesions

Location: Chicago, IL How QuantX is using artificial intelligence in healthcare: QuantX is a diagnostic platform that’s powered by machine learning, helping skilled physicians in characterization and detection of breast lesions. By combining case mining and AI, this tool helps radiologists and doctors better diagnose breast abnormalities and prescribe personalized treatments. QuantX’s AI is helping to address the diagnostic needs of healthcare administrators, physicians, and patients alike. It has already been given the green light by the FDA for use in US hospitals.

     7. Imagen (OsteoDetect): Probe Wrist Fractures in Adult Patients

Location: New York, NY

How Imagen is using artificial intelligence in healthcare: Imagen, the healthcare AI tech company, has developed an AI-powered diagnostic and detection software – called OsteoDetect – that uses machine learning to identify wrist fractures in adult patients. Through computer-assisted detection platform, doctors can quickly and more accurately spot distal radius fractures. Already cleared by the FDA, the diagnostic solution could benefit myriad healthcare settings, including urgent care facilities, primary care facilities, ERs, and so on. It is a great example of artificial intelligence in healthcare adjunct tools that will complement the expertise of radiologists.

     8. Mayo Clinic: AI Cervical Cancer Screenings at the Mayo Clinic

Location: Jacksonville, Florida How Mayo Clinic is using artificial intelligence in healthcare: Mayo Clinic is leveraging an AI computer algorithm developed by researchers at Global Good and the National Institutes of Health to recommend additional cervical cancer exams. The computer-aided diagnostic solution combs through more than thousands cervical images to detect if a case needs further medical attention. This FDA-approved solution is long overdue given that the number of women screened for cervical cancer can be much lower than reported. Researchers found that the AI-platform delivers 91% accurate detection of precarious cancer changes compared to 69% and 71% by physicians and traditional cytology respectively.

     9. Moorfields Eye Hospital: Early Detection of Signs of Eye Disease

Location: London, England How Moorfields Eye Hospital is using artificial intelligence in healthcare: Moorfields Eye Hospital in London, in conjunction with Google DeepMind Health, is using AI to offer faster and more accurate early diagnosis of eye disease and therefore prevent total loss of sight. Using patient data from close to 15000 individuals, the London-based hospital has trained the AI algorithm to identify signs of eye disease from OCT, an imaging method that employs light waves to render three-dimensional images of the retina. Although DeepMind’s AI platform is still seeking regulatory green-light in the UK, when it does, Moorfields will spread its use for free for the first 5 years across its network of thirty hospitals in England.

     10. Zebra Medical Vision: AI-Powered Coronary Calcium Scoring

Location: Shefayim, Israel

How Zebra Medical Vision is using AI in healthcare: Zebra Med uses AI powered computer algorithms for coronary calcium scoring. Its AI platform has already been used to catch early-stage cancers, misdiagnosed conditions, and other serious diseases. By leveraging millions of terabytes of HD quality scans, Zebra Medical Vision automatically detects and score calcium in patients with potential cardiovascular disease. The AI diagnostic tool by Zebra Med has already received FDA 510(k) clearance.

     11. Bay Labs: Echocardiogram EF Determination

Location: San Francisco, California How Bay Labs is using artificial intelligence in healthcare: Based in San Francisco, Bay Labs is a medical tech firm that’s at the forefront of the application of AI in healthcare. The newly FDA-approved AI product by Bay Labs, EchoMed AutoEF, help cardiologists automatically determine left ventricular ejection fraction (EF) more accurately. As such, the diagnostic software helps doctors and cardiologists interpret echocardiograms and calculate EF with greater precision and efficiency. An HIPAA-compliant tech company, Bay Labs partners with several academic and clinical institutions to deliver better patient and research outcomes. They are currently working with Duke University School of Medicine, Northwestern Medicine, Minneapolis Heart Institute, Allina Health, and New York Presbyterian Hospital.

     12. Neural Analytics: Device for Paramedic Stroke Diagnosis

Location: Los Angeles, CA How Neural Analytics is using artificial intelligence in healthcare: the primary aim of Neural Analytics is to help paramedics detect early signs of acute ischemic stroke (AIC). According to the data presented by the medical tech company in Boston, its TCD platform and device can leverage AI to deliver stroke diagnosis at up to 94 percent accuracy. Soon, these devices will appear in EMT vehicles, allowing paramedics to stabilize and prep the patients accordingly long before they arrive at emergency rooms. Neural Analytics’s Transcranial Doppler device that’s powered by AI is designed to empower clinicians and first responders to diagnose cardiovascular diseases with greater accuracy and speed. The company received FDA approval in May 2018 for its TCD platform.

     13. Icometrix: MRI Brain Interpretation

Location: Chicago, IL

How Icometrix is using artificial intelligence in healthcare: Icometrix is a premier medical tech company that’s changing lives with the power of machine learning and AI. Its flagship AI solution, icobrain, uses deep learning algorithms to help doctors and radiologists interpret brain MRI and CT scans. What this means is that the platform allows clinicians to apply AI to help patients with traumatic brain injuries. Icobrain TBI platform makes it possible to detect a traumatic brain injury (TBI) early, a condition that affects more than 50 million people annually. Icobrain platform received FDA approval in April 2018.

     14. Viz.ai: CT Stroke Diagnosis

Location: Tel Aviv-Yafo, Israel, and San Francisco, CA How Viz.ai is leveraging artificial intelligence in healthcare: Viz.ai is using AI and deep learning to help doctors and cardiologists diagnose stroke faster and more accurately. Its artificial intelligence product, Viz LVO, has been praised for its capability to reduce door-to-groin time. With continued application, the new innovation will reduce time to treatment and greatly increase the number of stroke patients that receive intervention in time. This is an HIPAA-approved device with a mobile interface that allows for diagnosis on the move, making it easy for paramedics and doctors to take a handle on stroke cases.  Viz LVO received FDA clearance in February 2018 for AI CT stroke diagnosis. It enables doctors to recommend more advanced treatment options like thrombectomy and refer escalated cases to specialized stroke centers like the Southeast Regional Stroke Center at Erlanger, Tennessee.

     15. AliveCor: Atrial Fibrillation Detection via Apple Watch

Location: San Francisco, CA

How ALiveCor is leveraging artificial intelligence in healthcare: AliveCor uses AI and big data analytics to help consumers take a medical-grade EKG using their Apple Watches. In under a minute, KardiaMobile, a nifty smartphone app, helps tell you whether your heartbeat is normal or irregular, therefore uncovering any signs of atrial fibrillation (Afib). What’s even more interesting is the level of accuracy that the AI application delivers. The recently FDA-cleared platform helps users get an immediate and professional-grade analysis of their heart rates to detect Afib early enough. This way, patients can seek further medical diagnosis and proper treatment before it leads to stroke or heart attack.

     16. Tempus: Comprehensive Cardiac Disease Care

Location: Chicago, IL How Tempus uses AI in healthcare: Tempus is leveraging machine learning, big data, and deep learning to identify undiagnosed or undertreated cardiovascular disease. Tempus Next is an artificial intelligence software that screens routinely generated data for opportunities for proactive care and disease management. It uses multimodal data and AI-based algorithms to provide insights for care teams to evaluate and take meaningful action.

Medical knowledge management examples of Artificial Intelligence in healthcare

Here are several examples of smart medical tech companies that are leveraging artificial intelligence to spruce up medical knowledge management. They are developing the next wave of healthcare data management solutions for both patients and healthcare providers.

     1. Johnson & Johnson Institute: AI-Powered VR Module for Training Doctors

Location: Cincinnati, Ohio How Johnson & Johnson is leveraging AI to help doctors: Johnson & Johnson Institute has developed a virtual reality platform that is powered by artificial intelligence. The J&J VR module is designed especially to train orthopedic nurses, surgeons, doctors, and even medical students how to conduct an array of surgical procedures. The deep learning tools embedded into the platform immerses clinicians, allowing them to learn and experience real-world case scenarios without having to be in physical training room. Needless to say, the platform helps doctors stay on top of their medical knowledge management without much hassle. On the patient side, the biggest use cases are health chatbots and self-assist apps. Take a look at 9 examples of how healthcare bots and self-assist mobile apps are transforming the lives of patients across the globe.

     2. Babylon Health: Medical Advice Dispensing Chatbots

Babylon Health is a state of the art telemedicine app, medical subscription service, and healthbot, all rolled into one. Established back in 2013, the platform now uses AI to deliver medical consultations to patients based on common medical knowledge and their own health history. Of course, Babylon Health also offers live video consultation with a live doctor if the patient requests one. The NHS in the UK has been testing Babylon Health since 2017. During the trial period, NHS used the AI-powered chatbots to help give medical consultation to patients on their network. More specifically, the chatbots carefully diagnosed the patients, and either give them a medical prescription or refer them to a specialist.

     3. SkinVision AI-Powered App That Enables One to Self-Check for Skin Cancer

Given that 20 percent of Americans get skin cancer, self-check mobile apps can come in especially handy. That’s exactly what SkinVision does. It’s an AI-powered iOS and Android app that allows users to perform routine self-checks for skin cancer using their smartphones.

     4. GYANT: Facebook Messenger/Alexa Chatbot for Medical Referrals

GYANT is smart telemedicine chatbot that collects symptoms from the patient and relays the information to clinicians who can offer diagnosis and prescribe treatments. They refer the patients to proper medical attention in real time through Alexa and Facebook Messenger. It’s currently geared towards English speaking patients, but it can communicate with Spanish, German, and Portuguese speakers.

     5. Buoy Health: Health Diagnostic Chatbot

Crafted by a team of scientists and clinicians through the Harvard Innovation Laboratory, Buoy Health’s AI algorithm perform medical queries on data sets from 5 million patients, 18000 medical paper entries, and can diagnose up to 1700 medical issues.

     6. Izzy: Menstrual and Sexual Health Chatbot

Izzy is a Facebook Messenger chatbot that allows women to stay on top of their menstrual cycle dates, sexual issues, and period problems like pain. It collects menstrual cycle data from the user, uses artificial intelligence and big data, and eventually helps users learn more about their sexual and menstrual health issues.

     7. Safedrugbot: Health Chat Messaging Service for Getting Drug Information Related to Breastfeeding

Safedrugbot is a robust healthbot service that provides assistant-esque support to doctors, physicians, nurses, and other healthcare providers who are looking for proper data about the use of medication during breastfeeding. Furthermore, the AI-powered chat messaging services offers healthcare professionals info about active agents/compounds in drugs and other alternative medications during breastfeeding.

     8. Your.MD: AI Health Information Platform

Your.MD is what it sounds like – it’s a free digital health platform that uses AI and Big Data analytics to offer actionable medical advice to patients based on incredibly accurate healthcare sources. It’s available as an Android and iOS app, as well as a chatbot for Telegram, KIK, Slack and Facebook Messenger. It’s an award-winning AI medical chatbot service that covers it all, from recommending mental health resources to finding you the closest doctor’s office.

     9. Sensely: Self-Care Chatbot Service

At the heart of Sensely chatbot service is a female medical assistant called Molly. She can examine your symptoms through images, video, text or speech, and recommend a diagnosis based on the patient’s gathered data and information already embedded in the algorithm. Eventually, Sensely’s Molly uses triage’s standard colors to decide whether patient’s medical case is an emergency or not.

     10. Cancer Chatbot: Facebook Messenger Chatbot for Seeking Cancer Information

As you might have already suspected, this is health chatbot that delivers cancer-related information and resources on Facebook Messenger. It provides family, friends, caregivers, and cancer patients with things like chemo tricks and tips as well as free resources.

     11. San: The Artificial Intelligence Nurse Chatbot

San, the proprietary AI-driven chatbot from Helpsy is one of a kind. San is a virtual assistant for cancer patients. It can be deployed directly in an interconnected fashion to connect all the people with a vested interest in the quality of life of a specific patient: hospitals, doctors, nurses, the patient and the patient’s immediate family. Once a diagnosis is entered into the system, the San chatbot can help patients navigate the complex web of requirements, treatment options, routines, patient adherence medical protocols and other aspects of cancer treatment.

Summary

AI potential in healthcare is huge. There are already several noteworthy AI applications making inroads in the sector. These include: Robot-Assisted Surgery – This leads the pack when it comes to valuation ($40 billion). They can help deliver better surgery outcomes with little or no errors in the process. Virtual Nursing Assistants – These AI-powered assistants examine the symptoms and readily available data and relay alerts to doctors only when patients need attention. Valued at $20 billion, this AI application can cut RN staffing costs by up to 20 percent, according to Accenture Analysis. Clinical Trial Participant Identifier – Given that most clinical trials fail due to wrong participants, AI can chip in, saving the healthcare industry up to $13 billion annually. Connected Medical Equipment – AI can help ship the idea of the Internet of Things into the world of healthcare, delivering savings up to $14 billion. Dosage Error Reduction – This AI application is expected to be worth over $16 billion by 2026. This entails the use of AI solutions to prevent dosage errors and deliver more accurate prescriptions. This will help providers to avoid unwanted malpractice lawsuits and save patients a lot of heartache and risk. Fraud Detection – In the age of increased security breaches, AI Fraud Detection solutions can save healthcare up to $17 billion in losses. Cybersecurity AI applications, on the other hand, is also huge delivering cost savings of up to $2 billion. Administrative Workflow Assistance – Medical workforce can also stand to benefit from the proliferation of AI in healthcare, bringing cost savings of up to $18 billion. Preliminary and Auto Image Diagnosis – Radiologists are the biggest winners in a healthcare system run by AI solutions. From detecting ailments from CT, MRI and other imaging technologies, AI can save more than $8 billion annually for healthcare organizations. * All figures were sourced from Accenture’s report – ARTIFICIAL INTELLIGENCE: Healthcare’s New Nervous System In total, the healthcare AI market is forecasted to reach $150 billion in valuation by 2026. The biggest winning areas in the race for AI adoption in healthcare are 4, namely: Institutional Readiness – AI is easy to include in any healthcare organization’s governance and structure, boosting the quality of service, workflow efficiency, and improving medical outcomes for patients. Medical Employees – Doctors, RN, caregivers, administrative personnel, executives, etc. Patients – Better manage their health, and seek medical attention at their convenience. Healthcare Security – AI solutions for cybersecurity, fraud detection, and preventing privacy breaches While several AI applications have already appeared in the healthcare realm, they are not without challenges. These include:

1. Data Security Breaches and Privacy Issues
2. Out-of-the-box Solutions Problem
3. Startup and Initial Stakeholder Issues
4. Compliance and Regulation hurdles

As we discussed in this article, there are many robust examples of how artificial intelligence is being employed in the healthcare space. We discussed them as use cases for two areas of healthcare: Clinical Decision Support, and Medical Information and Knowledge Management both for healthcare providers and for patients/consumers.

Do you need guidance with your digital transformation initiatives? Digital Authority Partners has worked with companies like Athenahealth, Omron Healthcare and Blue Cross Blue Shield on cutting-edge digital initiatives that improve patient outcomes and quality of care. Contact Digital Authority Partners at hello@digitalauthority.me or 312-820-9893.

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