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The emergence of AI in healthcare has been transforming, redefining the way we diagnose, monitor, and treat patients. This cutting-edge technology is driving massive innovation in the industry, facilitating rapid advancements in drug discovery and development while empowering healthcare professionals to provide enhanced and expedited care.
The outbreak of the COVID-19 pandemic has further pushed the healthcare industry to adopt AI. The pandemic supported the widespread adoption and advancements of AI-based healthcare, enabling their effective utilization in diagnosis, detection, patient care, clinical trials, claims settlement, and virtual assistants.
According to Statista, AI in healthcare was worth around $11 billion worldwide in 2021 and is estimated to reach $188 billion by 2030, increasing at a CAGR of 37% from 2022 to 2030.
AI in healthcare can immensely benefit providers and patients in different areas of operations, including improved patient care, chronic disease management, early risk identification, workflow automation, and optimization.
In this article, we will look into artificial intelligence at length to get an idea of how AI is used in healthcare and how the technology will reshape the industry in the future.
From making an accurate diagnosis to maximizing hospital efficiency, AI applications in healthcare have proven to be a boon for the industry. Here are a few ways AI is revolutionizing the healthcare industry and driving it toward digital transformation.
It is one of the most common applications of AI in healthcare. AI and collaborative robots have revolutionized surgical procedures, reducing the risks of blood loss, pain, and other side effects. Furthermore, with AI in healthcare, surgeons can perform complex surgeries with greater accuracy, leading to faster and smoother post-surgery recovery for patients.
For instance, Maastricht University Medical Center has been utilizing AI-powered robots to suture small blood vessels, some no thicker than 0.03 millimeters. Using AI in medicine and healthcare, surgeons can access real-time information and insights into a patient’s current health condition. This AI-backed information enables healthcare providers to make prompt, intelligent decisions before, during, and after procedures to ensure the best outcomes.
The US Justice Department states that 3% of healthcare claims in the country are fraudulent. It translates into a $100 billion loss annually. With AI-based healthcare, professionals can detect invalid claims and speed up the processing, approval, and payment of valid ones. Apart from detecting insurance fraud, AI in healthcare also prevents patient data from being stolen.
Leading healthcare service providers such as Harvard Pilgrim Health are embracing AI to root out healthcare fraud. They use AI-based fraud detection systems to identify claims and detect suspicious behavior.
AI and ML in healthcare are changing the way clinical providers make decisions. AI delivers data to assist healthcare professionals in diagnosing, treatment planning, and health management. The technology also supports decisions in data-intensive specialties like ophthalmology, radiology, and pathology. It may even make it possible to perform certain tasks autonomously using AI in the near future. With natural language processing, AI can also help translate clinical notes in EHRs, enabling clinicians to enter data once only once.
Healthcare is now moving towards the Cognitive Assistants world, which comes with the capabilities of reasoning, analytics, and a complete range of medical knowledge. The recently launched algorithm, Medical Sieve, has been stated qualified to assist in cardiology and radiology decisions. The cognitive health assistant analyzes the radiology images to detect issues faster and more reliably.
Medical Sieve is one of the best examples of AI in healthcare. However, there are other examples also, like Enlitic, which aims to mix deep learning with medical data to aid advanced diagnostics and improve patient care.
Babylon App is a real-world example of how AI applications in healthcare can change doctor consultations. The app offers online medical consultation and healthcare services, providing the best AI advice based on a patient’s medical history and available knowledge.
This AI-based app works in a way that the users only have to report the symptoms of their illness, and the app then checks the symptoms against the database of the diseases using the speech recognition method. After noting the patient’s history and circumstances, they offer a course of action that the patient should take.
The ever increasing demand for healthcare apps incorporating AI technology is evident from the fact that over 54% mHealth app users are willing to engage with AI and Robotics for their IT healthcare consulting needs. This AI trend highlights the growing popularity and acceptance of AI solutions in healthcare.
Sense.ly, a medical startup, developed the world’s first digital nurse, Molly. The virtual nurse has a friendly face and pleasant voice, aiming to monitor the condition and treatment of patients. The mobile app uses machine learning to support patients with chronic conditions during the intervals between their medical appointments. The app provides tested, customized monitoring and follow-up care, focusing on chronic diseases. Being present to inform patients when to take medications and then monitoring if they did, the benefits of AI in healthcare are immensely valuable.
Creating pharmaceuticals using clinical trials can take over a decade and even cost billions. Introducing AI to drug creation doesn’t just make the process faster but also extremely cost-effective.
Atomwise is one such network that uses supercomputers, which roots out therapy from molecular structure databases. In 2015, Atomwise used its AI technology to discover the existing medicines in the market that could be redesigned for treating the Ebola virus. They found two drugs that could help solve the epidemic. The analysis that would have taken years happened in one day through Atomwise AI technology.
AI in medicine has a big impact on genomics and genetics. The technology aids in identifying patterns in massive data sets containing medical records and genetic information, helping find links to diseases and mutation. No wonder AI can tell doctors what happens in the cell when DNA is changed through genetic variation, whether therapeutically or naturally.
As healthcare invoices increasingly transition to digital formats, retrieving data about the doctor, treatment, and medical establishment becomes effortlessly accessible. After data mining, hospitals can generate reports on the mistakes they are continuously making in treating a certain type of condition to help improve and even avoid unnecessary hospitalizations of patients, wherever needed.
A company in the Netherlands, Zorgprisma Publiek, has been analyzing the invoices shared by the hospitals and using Watson technology to mine the collected data.
One of the most remarkable applications and benefits of AI in healthcare is the computer vision capabilities. Hospitals and clinics use AI to recognize abnormalities in different kinds of medical images as CT or radiology scans. Image recognition assists doctors in diagnosing tumors, kidney, and liver infections, improving cancer prognosis, and more.
The best example of AI-powered visual perception is the tool used at the UVA University Hospital. Utilizing AI and ML in healthcare, the tool analyzes children’s biopsy images to distinguish between environmental enteropathy and celiac disease, doing it as reliably as doctors do.
Now that we know the transformative use cases, real-world examples, and impact of AI on healthcare, let’s dive into the different types of AI technologies revolutionizing the healthcare industry.
AI for healthcare is a collection of many technologies, each serving specific purposes. While the industry greatly benefits from various AI technologies, the tasks and processes they support may vary. Let’s explore some of the most significant AI technology in healthcare:
Machine Learning (ML) is one of the common forms of AI in the healthcare industry. Machine learning uses data and algorithms to imitate how humans learn, gradually improving its accuracy.
Precision medicine is one of the most remarkable applications of ML in healthcare. It predicts what treatment procedures will best suit the patients based on various patient attributes and treatments. The majority of precision medicine applications rely on supervised learning, utilizing a training dataset with known outcomes.
The most complex form of machine learning is deep learning or neural network models with many levels of variables or features to predict outcomes. A common application of deep learning is the recognition of potentially cancerous lesions in radiology images.
Natural language processing (NLP) includes applications such as text analysis, speech recognition, and so on. A common use of NLP in healthcare involves creating and classifying clinical documentation and published research. NLP systems can analyze unstructured clinical notes on patients, giving incredible insight into improving methods, understanding quality, and better patient care.
RPA in healthcare uses automation technologies to learn, mimic, and execute rules-based business processes. Compared to other AI technology in healthcare, they are inexpensive, easy to program, and transparent in their actions. In healthcare, they help automate repetitive tasks like updating patient records or billing.
Related Article: The Handbook You Need to Understand Robotic Process Automation
A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem. The aim of the expert system is to take knowledge from a human expert and convert this into several hardcoded rules to apply to the input data.
In healthcare, they are widely employed for clinical decision-support purposes. These rule-based systems work well up to a point and are easy to understand. But when the number of rules increases, they begin to conflict with each other and break down. However, now they are being replaced in healthcare by more approaches based on data and machine learning algorithms.
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With the ever-evolving applications of AI in healthcare, there also comes a set of challenges that need to be addressed efficiently. These challenges range from data quality issues to security concerns, creating setbacks to harnessing the full potential of AI solutions in healthcare. Let’s delve deeper to understand the impact of these challenges on the industry.
One of the biggest barriers to leveraging AI trends in healthcare is that training them requires huge amounts of data from several sources, including electronic health records, pharmacy records, etc. Since the data is fragmented and patients often consult with different health providers, the data gets complicated and less comprehendible, resulting in errors and higher costs.
Another key challenge of utilizing AI for healthcare is the need for robust security measures to protect sensitive information in the vast amount of collected data. So, it’s vital to look for the right ML and Artificial intelligence development company that can offer a wide range of security options to safeguard customers’ data.
There are some instances where AI systems might get wrong in detecting potential risks or designing effective treatment plans. For instance, if an AI-based system suggests the wrong drug to a patient or makes an error in locating a tumor in a radiology scan, it could result in the patient’s injury or dire health-related consequences.
AI is already revolutionizing the healthcare industry, redefining medical practices, patient experience, and pharmaceutical operations. Medical units are using generative AI in healthcare to predict everything from designing effective treatment plans for women who develop breast cancer to forecasting emergency department volumes and beyond.
However, it is just the beginning of an exciting journey. In the future, AI will enable the next generation of radio tools that are precise and detailed enough to replace the need for tissue samples in some cases. It may help service providers to define the aggressiveness of cancers and target treatments more appropriately. AI is also enabling “virtual biopsies” and advancing the innovative field of radio mics.
Moreover, the utilization of electronic health data can help identify patients at risk and detect infection patterns before they show symptoms. By leveraging AI for healthcare, these analytical capabilities can deliver swift and precise alerts to healthcare providers. AI can also provide earlier warnings for conditions like seizures or sepsis, which typically necessitate intensive analysis of highly complex datasets.
Risk scoring, clinical decision support, and early alerting are some of the significant areas of development for this revolutionary tech trend in healthcare, propelling the healthcare industry into a new era of clinical excellence and remarkable advancements in patient care.
In simple terms, AI tools in healthcare are rapidly penetrating the industry and witnessing a significant surge in their widespread adoption. Consequently, it helps drive efficiency, improve patient care, optimize operations, and reduce the ever-increasing burden on the healthcare systems. The effective utilization of conversational AI in healthcare will create intelligent processes and workflows, making medical practices more effective, more equitable, and more personalized.
As we can see AI and healthcare go hand-in-hand because of the multiple benefits this technology offers to the industry. Artificial intelligence is revolutionizing healthcare by making it more intelligent, efficient, and patient-centric. The integration of AI in healthcare can enhance diagnostic accuracy, streamline administrative tasks, optimize resource allocation, and improve treatment outcomes.
However, some hurdles hinder the way of effective utilization of generative AI solutions in healthcare. By partnering with reliable software development companies like Appinventiv, one can easily overcome those challenges and produce more accurate diagnoses, drug discovery, and treatment plans.
Appinventiv, leading AI services and ML software development company, actively works with industry leaders like Vyrb, JobGet, KFC, etc., to build custom AI and ML-based models that help enhance customer experience, improve revenue, and reduce costs.
With a team of skilled professionals, we are well-equipped to create innovative AI-powered solutions that address the unique challenges and requirements of the healthcare industry. By partnering with us, you can harness the full potential of AI in healthcare and experience transformative changes.
Thus, all healthcare institutions must invest in healthcare software development services to streamline operations and improve patient care.
Get in touch with our AI experts now.
Q. What is AI in healthcare?
A. AI in healthcare uses machine learning, deep learning, natural language processing, chatbots, and other AI-enabled tools to streamline administrative tasks and improve the patient experience, including diagnosis, drug discovery, treatment, and outcomes.
Q. How can AI improve healthcare?
A. AI can improve healthcare in several ways, such as:
Q. How is AI used in healthcare?
AI in healthcare is used to improve various operational aspects, redefining how we diagnose, treat, and monitor patients. It helps analyze medical data, design personalized treatment plans, support drug discovery, and automate administrative tasks. Furthermore, AI chatbots in healthcare provide round-the-clock support, improving patient engagement and experience.