Artificial intelligence in healthcare

If we are talking about intelligence framing the reference of Artificial Intelligence in the healthcare sector, then definitely the sky is the limit. AI in healthcare has been setting benchmarks for itself to grow at a consistent pace. 91.5% of high-scale businesses are Investing in Artificial Intelligence, as using AI has boosted business productivity by 54%. Taking a look at healthcare startups, AI healthcare startups have boomed and how. Still, many healthcare startups are unaware of the fact what wonders artificial intelligence can do for their venture. So, that being said, why don’t we talk about the current scenario of AI in healthcare? We will be covering everything about this concept from scratch; the transformations it has brought, the challenges to implementing AI in healthcare, the benefits it has offered, and other significant stuff. Let’s brew this conversation with a cup of brewed coffee! What is AI in Healthcare? We all are aware of the fact AI in healthcare is being embraced relatively, especially AI. Many healthcare startups have claimed to witness major differences after implementing AI solutions. But since we want all the AI healthcare startups to avail artificial intelligence for their next big plan of action, we are helping you learn things from scratch. AI in healthcare is an umbrella term to present with the application of ML which is the acronym for machine learning algorithms and other cognitive technologies in medicine. To put it simply, AI is when computers and other machines simulate human cognition, and are eligible of learning, thinking, and deciding stuff or take action. AI in healthcare, then, is the use of machines to examine and act on medical data, often with the perspective of anticipating the outcome. The Scenario of AI in Healthcare 2022 Let’s take a look at statistics citing the current status of AI in the healthcare market: Market is projected to reach USD 95.65 Billion by 2028, up from USD 6.60 Billion in 2021, at a compound annual growth rate (CAGR) of 46.1%. Especially the pharmaceutical & biotechnology startups segment is projected to grow at a high-paced CAGR from 2022 to 2028. As per Statista’s report, the global AI digital health market by major segment for selected years between 2015 and 2025. It is estimated that it will reach nearly 190 billion U.S. dollars by 2025. Having seen the transformation AI has welcomed in the healthcare industry, it is projected that AI applications can cut annual US healthcare costs by USD 150 billion in 2026. Segments of AI in healthcare that are expected to make strides between 2022-2028: The pharmaceutical & biotechnology Startups Natural language processing The clinical trials participant identifier So, these were the key statistics that executed the flourishing period of healthcare startups brought by artificial intelligence. What Are The Types of AI in Healthcare? Let’s learn about the types of artificial intelligence here in detail: NLP – Natural Language Processing Artificial intelligence has been in existence for decades up until now; since then, AI researchers have been focusing on making the exact sense of human language. The concept of natural language processing, i.e., NLP helps with the recognition of speech, text analysis, text translation, and other stuff. The process is done in two ways which are statistical and semantic NLP. But statistical NLP is being used on a frequent basis as it is based on machine learning and deep learning neural networks, they are pretty precise at language recognition and text identification. When it comes to key functions of NLP, it’s majorly about the creation, comprehension, and segregation of documents in order to make the most of these insights. Apart from this, NLP functions as an analytical tool for unstructured data about patients. It automatically generates reports about patients’ diagnoses and transcribes patients’ communication to conduct conversational AI. Robotics Process Automation Robotics process automation has nothing to do with robots in actuality. They are computer programs on servers that use automation technology that is capable of learning, simulating, and presenting rule-based business processes. If compared to the other platforms that artificial intelligence has, RPA is pretty nominal, also it is quite easy to program and monitor as they are not lucid to be worked with. AI Healthcare startups can avail it for regular tasks like prior authorization, updating patient entries, and records, or billing. If you will merge the same with other technologies like image recognition, you can use it to extract data from, for example, faxed images to input it into transactional systems. Machine Learning Here we are talking about one of the most common and most used forms of artificial intelligence: machine learning. ML is nothing but a statistical technique that fits models to data and optimizes it to get valuable insights from the data. If we are specifically talking about machine learning in the healthcare industry, here’s what you need to know. Machine learning’s precision in diagnosing treatment and the course of medicine with respect to the attributes of the patient and his medical history is what helps healthcare startups the most. However, machine learning and its precision medicine application need training datasets to reach the end results, this process is called supervised learning. Let’s learn about two major forms of ML: Artificial neural networks and Deep Learning Beginning with a pretty vast and complex form of machine learning, which is artificial neural network – a technology that has been in existence for quite a long time. ANN, i.e., artificial neural networks simulate the human brain through a set of algorithms. Neural networks are generally run by 4 components: inputs, weights, a bias or threshold, and an output. Another most important and complex form of machine learning is deep learning. However, deep learning is just a subset of machine learning. The notable difference between the two is their approach to learning from the data and using their algorithms toward the data. Deep learning regulates more of the feature extraction piece of the process, pulling out some of the manual human intervention required. It