Based on what we’ve seen in 2020, it’s no surprise that healthcare is now one of the most significant and fastest-growing industries. Worldwide, the healthcare market was nearly $8,452 billion in 2018 and is expected to grow at an 8.9% CAGR to nearly $11,909 billion by 2022. Because of a worldwide pandemic, the CAGR percentage has seen a significant increase, making the push for digitalization in healthcare all the more urgent.
Since emerging technologies were creating their presence known long before 2020, last year’s seminal event provided the much-needed impetus for those on the fence and those ready to see how things change for others. Many business leaders are hesitant to embrace digital transformation because of the following concerns:
- Lack of clarity on how to begin and proceed
- Concerns about data security
- Concerns about investment returns
- Inadequate technical knowledge
It’s obvious that the concept of “digital transformation” can sound absurd at times. We know it’s important, but it’s also such a broad topic that people are frequently overwhelmed by the potential changes. True digital transformation requires a fundamental shift in how an organisation operates. However, there is always the option of starting small, changing one thing at a time, assessing results, and moving forward. One can examine a specific technology, consider the areas of operation that could benefit from its features, and begin with a small project. Using a series of byte-sized approaches like this can greatly simplify the process of implementing AI applications in healthcare.
When it comes to selecting a technology, artificial intelligence is all the rage in the healthcare industry. So much so that it has been dubbed the “new nervous system of the healthcare industry.” Artificial intelligence is significantly transforming and enhancing various key processes in healthcare, and its potential is even more diverse as well as amazing. AI can help providers understand ailments and better manage patients’ health by using chatbots and computer aided detection (CAD) for diagnosis and analysis, as well as training. The AI-powered healthcare market is expected to exceed $34 billion by 2025 due to its versatility.
Now let’s look at the numerous ways AI can drive healthcare innovation in the upcoming years.
Benefits Of AI In Healthcare:
- Accurate diagnosis: Human errors usually result from insufficient medical information, poor sequencing, and a high caseload. Advanced machine-learning algorithms, however, can make the correct diagnosis once records are fed into a computer, eliminating errors and considerably increasing the efficiency of medical institutions.
- Drug development that is expedited: The traditional methods of producing medications can frequently be prohibitively expensive and time-consuming. This presents a significant obstacle, particularly when the globe is in danger from a pandemic and action must be taken quickly. Treatments are typically tested in clinical settings at a cost of $2.6 billion, and only 10% of those drugs are subsequently sold. Robot Adam quickly combed through billions of data points in 2007, when scientists were investigating the diverse roles of yeast, to identify the 19 genes that make up yeast and forecast 9 new, precise ideas.
- Enhanced patient experience: Everyday life is chaotic due to overcrowded medical facilities, an increasing volume of reports, misunderstanding about insurance, and other factors. In these circumstances, artificial intelligence (AI) has proven to be a lifesaver by quickly sifting through data, preparing reports, and assisting patients in knowing precisely where to go and who to call on mobile devices. In the age of remote consulting, artificial intelligence (AI) forms the basis of some of the most complex digital solutions, enabling connection and communication as well as precise timing updates, report availability, appointment scheduling, and many other functions.
- Data security: safeguarding private patient information is crucial in the healthcare industry. Rapidly developing AI algorithms assist in encrypting patient data, clinical reports, diagnostic results, and other information to protect it from hacking and store it securely in the cloud for access by patients and professionals anywhere.
- Robot-assisted surgery: Extreme care, accuracy, and competence are required for complex and crucial surgeries. The number of successful surgeries using AI-enabled robots is rising. The robots have surgical equipment, mechanical arms, and cameras. They can be specifically designed to fit within the human body and offer a clear, magnified view of the surgery site that is far superior to what the human eye can provide. These procedures help patients recover more quickly and with less discomfort than traditional procedures.
- Remote monitoring: By utilising real-time monitoring of events like heart attacks and asthma attacks, connected devices can help save lives. IoT networks are used by remote monitoring devices to connect and follow bodily activity. Wearable technology or mobile applications can be used to access data, and AI can be used to make quick choices. By 2029, the market for wearable technology is anticipated to be worth $415.12 billion.
- Streamlined training: Training is made more efficient thanks to AI, which enables healthcare professionals to participate in simulations based on a sizable database of scenarios. These simulations help students make judgments and learn from prior responses to achieve training requirements.
- Risk prediction: Determining a patient’s likelihood of contracting a specific disease using pattern recognition. By offering insightful information, machine learning in healthcare promotes prompt decisions and actions.
- Smart health insurance: Health data can be collected through linked devices by insurance firms for use in underwriting, processing health claims, and managing risk. It eliminates bogus claims and offers transparency between clients and insurance.
- Location tracking and alerts: Smart health devices allow real-time alerting, tracking, and monitoring, allowing for hands-on treatments, better accuracy, prompt doctor intervention, and improved outcomes for the full spectrum of patient care. Sensors can track wheelchairs, scales, nebulizers, pumps, or monitoring equipment, making it simpler for personnel to keep track of them.
Despite these significant developments, AI usage in healthcare is still in its early stages. Continuous research keeps enhancing the technology with new capabilities, leading to bigger achievements in the upcoming years across many industries.
AI and ML have a lot to offer the crucial healthcare sector, which is currently undergoing one of the fastest shifts towards digital, and facilities have the potential to significantly improve customer experience, develop new digital business lines, and accelerate research goals, all of which will contribute to a better and safer world for all.