The use of artificial intelligence in the healthcare industry has been a hot topic in recent months, and there's no evidence of this technology's growth slowing down anytime soon. AI in healthcare has enormous and far-reaching promise, with everything from remote coaching solutions to drug development falling under the umbrella of what machine learning can achieve. Having said that, many healthcare executives are hesitant to experiment with AI due to privacy concerns, data integrity problems, or the unfortunate prevalence of multiple organizational silos that make data exchange nearly impossible. However, the futures of healthcare and machine learning and artificial intelligence are inextricably linked.
Artificial intelligence is a technology that mimics natural intellect, typically for learning, problem-solving, or decision-making. In healthcare, AI may perform duties that are previously performed by a clinician or other healthcare professional, such as assessing clinical data, providing insights to physicians, or operating medical systems. The word "artificial intelligence" refers to a wide range of technologies, including enhanced intelligence, robotic automation, natural language processing, and machine learning. Machine learning is a sort of AI that employs algorithms and data to mimic how people learn, synthesizing or improving a computer's "knowledge" of a subject. Healthcare workers can more quickly design pharmaceuticals, identify and cure diseases, and handle administrative operations with the help of machine learning (ML).
At the most basic level, below are some current technology applications of AI in healthcare that you should be aware of:
Several health and pharmaceutical businesses are currently utilizing artificial intelligence to assist with drug discovery and to improve the lengthy timelines and processes associated with developing and bringing medications to market.
This is still a developing area of interest in healthcare. As it turns out, by combining virtual reality and artificial intelligence, clinicians can construct simulated realities that can divert patients from the source of their pain and even aid in the fight against the opioid epidemic.
Clinical trials are, alas, a complete shambles. The majority of clinical trials are managed offline, with no integrated systems in place to track progress, data collection, and drug trial outcomes. However, Artificial Intelligence is reshaping clinical trials by placing a significant impetus on cost savings. In fact, according to Adobe, clinical health applications may save the US economy more than $150 billion per year, and more than 60% of businesses are already using AI in their innovation strategy.
Patients' outcomes can be improved using a wide range of artificial intelligence-driven tactics and outcomes. To begin with, voice assistants like Alexa and chatbots that aid patients at every stage of their patient experience have transformed the healthcare sector.
AI is currently on the minds of healthcare decision-makers, governments, investors, innovators, and the European Union. Countries as diverse as Germany, Finland, the United Kingdom, Israel, China, and the United States, have laid out objectives for AI in healthcare, and many are investing considerably in AI-related research. The private sector continues to play an important role, with $8.5 billion in venture capital (VC) funding for the top 50 enterprises in healthcare-related AI, including huge tech firms, startups, pharmaceutical, and medical-device firms, all engaged with the embryonic AI healthcare ecosystem.
Although, We are still in the early phases of acknowledging AI and its full potential in healthcare, particularly the influence of AI on personalization. Nonetheless, we may anticipate seeing three roles of AI in healthcare over time.
AI is expected to reduce the load on routine, repetitive, and primarily administrative tasks that take up a significant amount of time for doctors and nurses, optimizing healthcare operations and promoting adoption. In this initial phase, AI imaging applications may also be used, which are already in use in fields such as radiology, pathology, and ophthalmology.
More AI technologies, like remote monitoring, AI-powered warning systems, or virtual assistants, are projected to facilitate the shift from hospital-based to home-based care as patients take greater control of their care. This phase may also include a greater usage of NLP solutions in the clinics and at home. It may also facilitate increased use of AI across a broader range of disciplines, such as oncology, cardiology, or neurology, where improvements are already being made.
According to a survey conducted by Insider Intelligence, 23.4 million U.S. patients used remote patient care services and tools in 2020, with the figure predicted to rise to 30 million by 2024. Furthermore, 80% of Americans support remote patient monitoring, with nearly half favoring its incorporation into medical care. This comes as no surprise, given that the University of Pittsburgh Medical Center recently announced that their patient satisfaction scores increased to more than 90% after providing patients with remote patient monitoring equipment and tablets.
We expect to see more AI solutions in clinical trials based on clinical research evidence, with a greater impetus on enhanced and scaled clinical decision-support (CDS) tools in an industry that has learnt from previous attempts to inaugurate such tools into clinical practice and has adjusted its mindset, culture, and skills. Finally, respondents expect AI to be an intrinsic component of the healthcare value chain, from how we learn, research, and give care to how we enhance population health.
AI's penetration into healthcare will substantially impact human wellness, one of the most important components for any society. In particular, AI will be able to help medical experts such as doctors, nurses, and medical personnel recognize early signs of disease and empower them to give even more benefits to their patients. According to a report by MarketsandMarkets, the artificial intelligence in healthcare market will increase from USD 6.9 billion to USD 67.4 billion by 2027, with a compound annual growth rate of 46.2% from 2021 to 2027.
Medicinal discovery and development is an enormously long, expensive, and complex process that can typically take more than ten years, from identifying molecular targets to approving and marketing a drug product. Any failure throughout this process has a significant financial impact, and most drug candidates fail at some point during development and never reach the market. However, one important area in which AI will and is already changing healthcare is drug discovery. Much of the traditional method of identifying new medications is costly guesswork for biotechnology companies. However, a new wave of drug development platforms powered by artificial intelligence is enabling companies to exploit massive data sets to swiftly identify patient response markers and generate viable drug targets in a more cost-effective and efficient manner.
The outcomes could be transformative not only for medical providers and patients suffering from difficult-to-treat diseases but also for the biotech sector. It is believed that modest improvements in early-stage drug development success rates enabled by the use of artificial intelligence and machine learning could lead to the development of an additional 50 novel therapies over a 10-year period, representing a more than $50 billion opportunity.
Assuming modest annual gains in AI expenditure within biopharma research and development budgets, an AI drug development platform might deliver significant revenue growth through partnerships.
Another important area in which AI will and is already revolutionizing healthcare is early diagnosis prediction. This field entails utilizing Machine Learning models to anticipate disease onset or even before it manifests itself. This is accomplished by training ML models with vast amounts of labeled data, allowing the model to determine the link between those features that best predict such disease, and then deploying the model "in the wild." These models clearly rely on huge datasets to capture the underlying correlations between specific patient characteristics, such as age and pre-existing conditions, and the development of a target disease. Nearly 80% of hospitals and medical companies are considering or have already implemented medical AI applications, and more than 75% expect such applications to become popular.
Finally, by utilizing these ML-based models, numerous companies are beginning to develop solutions that allow patients to receive care comparable to that of a hospital without leaving their house.
Companies such as Babylon Health have created AI Chatbots that can deliver medical advice to patients. These chatbots receive messages from people expressing their current health difficulties, take that information as input, and then identify the most likely condition that would cause such symptoms. This method, known as "Triage" in medical contexts, has been described to be more effective than when administered by doctors or nurses.
Another feature of telemedicine is remote diagnosis delivered by a doctor rather than a "bot." It essentially comprises a standard doctor visit with the exception that the doctor or nurse is distant and in contact via a video-streaming device, such as a smartphone. While this treatment may have seemed strange and ineffectual a few years ago, the enormous changes that occurred in recent years changed a lot.
To summarize, healthcare is quickly becoming one of the key industries that AI is positioned to transform. Its trillion-dollar economy clearly demonstrates the magnitude of AI's impact, particularly along three trends: Drug development and discovery, Medical Diagnosis Prediction, and Telemedicine.
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