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How Artificial Intelligence Can Help Healthcare Startups?

adminBy 12/07/2022December 15th, 2022No Comments

If artificial intelligence is to be used in the healthcare industry as a frame of reference, then the sky’s the limit. 

If artificial intelligence is being used to frame intelligence in the healthcare industry, the possibilities are truly endless. AI in the healthcare industry has been establishing standards for growth. As employing AI has increased corporate efficiency by 54%, 91.5% of high-scale organizations are investing in it.  

When it comes to healthcare businesses, AI companies have emerged. 

 Many healthcare companies are still in the dark about the amazing things artificial intelligence can achieve for their business. Given that, why don’t we discuss the state of AI in healthcare today?  

Everything about this idea will be covered in detail, including the changes it has brought, the difficulties in applying AI to healthcare, the advantages it has provided, and other important information. Let’s start off this discussion with a cup of freshly prepared coffee! 

What is AI in Healthcare?

We are all aware that the use of AI in healthcare is still very new. After deploying AI technologies, some healthcare startups have claimed to have noticed significant changes. However, we are assisting you in learning everything from scratch because we want all AI healthcare firms to use artificial intelligence for their next big plan of action. 

The use of AI in healthcare is referred to as “ML in healthcare,” which is an acronym for machine learning algorithms and other cognitive technologies. 

Simply described, artificial intelligence (AI) is the ability of computers and other technologies to mimic human cognition and to learn, think, decide, and act. 

Therefore, AI in healthcare refers to the use of computers to analyze and take action on medical data, frequently with the goal of foreseeing the outcome. 

The AI Healthcare Scenario 2022

Let’s look at some statistics describing the state of AI in the healthcare industry right now:  

  1. The market is anticipated to develop at a compound annual growth rate (CAGR) of 46.1% to reach USD 95.65 billion by 2028, up from USD 6.60 billion in 2021.
  1. From 2022 to 2028, the pharmaceutical and biotechnology startups segment is expected to increase at a rapid CAGR.
  1. The worldwide AI digital health market by key segment for certain years between 2015 and 2025, according to a Statista analysis. By 2025, it’s anticipated to be close to 190 billion dollars.
  1. Given the shift AI has ushered in for the healthcare sector, it is anticipated that AI applications will reduce US healthcare spending by $150 billion annually by 2026.
  2. Areas of AI in healthcare that are anticipated to advance between 2022 and 2028 include:
  • The Biotechnology & Pharmaceutical Startups 
  • Processing language naturally 
  • Identifier for people taking part in clinical trials 

These major figures therefore summarized the booming time of artificial intelligence-driven healthcare startups. 

What Kinds of AI are used in Healthcare?

Let’s explore the many categories of artificial intelligence in more detail. 

1. NLP – Natural Language Processing

Since it has been decades since artificial intelligence first appeared, its researchers have been working to precisely understand human language. The idea of natural language processing, or NLP, is useful for a variety of tasks, including text translation, text analysis, and speech recognition.  

There are two methods used for the process: statistical NLP and semantic NLP. However, because statistical NLP is based on deep learning neural networks and machine learning, which are quite accurate at text recognition and language recognition, it is frequently employed.  

To maximize the use of these insights, the generation, understanding, and classification of texts are among the core NLP functions. In addition, NLP serves as an analytical tool for unstructured patient data. In order to conduct conversational AI, it automatically generates reports about the diagnoses of patients and records their communications. 

2. Robotics Process Automation

In reality, robotic process automation has little to do with robots. They are server-based computer applications that employ automation technologies and are able to learn, simulate, and display rule-based business processes. 

RPA is relatively small compared to other artificial intelligence systems. It is also simple to programme and monitor because it lacks a clear user interface. 

Startups in the healthcare industry can use AI for routine operations like prior permission, billing, and updating patient information and records. The same technology can be combined with other technologies, such as image recognition, to extract data from, say, faxed photographs and feed it into transactional systems. 

3. Machine Learning

Here, we’re discussing machine learning, one of the most popular and widely applied types of artificial intelligence. ML is nothing more than a statistical approach that optimizes model fit to data to extract useful insights from the data. 

Here is what you need to know if we are explicitly discussing machine learning in the healthcare sector. Healthcare startups benefit most from machine learning’s accuracy in diagnosing illnesses, prescribing medications, and planning medical procedures based on a patient’s characteristics and medical history. 

But in order to get the desired results, machine learning and its use in precision medicine need training datasets; this method is known as supervised learning. 

Let’s examine the two main types of ML: Deep Learning and Artificial Neural Networks.  

Let’s start with an extremely broad and sophisticated type of machine learning that has been around for a while: artificial neural networks. 

Artificial neural networks, or ANNs, use a collection of algorithms to replicate the human brain. Four elements typically make up a neural network: inputs, weights, a bias or threshold, and an output. 

Deep learning is one of the most significant and difficult types of machine learning. Deep learning is merely a part of machine learning, though. Their methods for employing their algorithms to the data and for learning from it distinguish the two significantly.  

More of the feature extraction portion of the process is controlled by deep learning, reducing the need for manual human intervention. Large data sets may be used with it, which is why it has earned the moniker “scalable machine learning.” 

4. Physical Robots

You might be astonished to learn that every year, over 200,000 industrial robots are placed throughout the world. Industrial robots, as their name implies, are designed to carry out certain activities, such as repositioning, lifting, welding, assembling products, and so forth. 

Robots have become more proficient at pacing as the years go by. They are constructed and designed in such a way that training them to move or transition to another duty does not require a lot of work. 

Speaking of physical robots in healthcare AI, we have observed robots performing their duties effectively. They are working together with people in a humane manner. It’s all a result of AI’s powers. 

5. Rule-based Expert System

In AI, expert systems have been around for decades. Since a few decades ago, they have primarily been used in “clinical decision support” applications in the field of AI healthcare, with healthcare entrepreneurs currently making the best use of them. Numerous electronic health record (EHR) providers today include a set of regulations with their systems. 

These expert systems need the assistance of human specialists in order to incorporate a set of regulations pertaining to a certain knowledge domain. When there are many rules, the task gets more difficult since the rules often clash and finally fall out of harmony. 

Thus, machine learning algorithms are gradually replacing this sort of AI. 

The 10 Most Incredible AI-Based Healthcare Innovations

We have discussed how companies are using AI to achieve their goals and how it is relatively delivering excellence and transformation to the healthcare sector. Now let’s discuss the main advancements that AI in healthcare has made: 

The Advancement Of AI In Drug Development And Diagnosis

The COVID-19 era has left an enduring legacy of artificial intelligence. The discovery of new drugs and the effectiveness of the diagnostic process have benefited most from machine learning, if we are talking about what AI has accomplished in healthcare startups beyond information processing and decision making.  

Particularly for people who have already received treatment for the symptoms of COVID-19, AI has aided in the use of CT scans to identify any pneumonia symptoms. 

After the introduction of their AI tool for radiotherapy, Microsoft also hits another milestone with Project InnerEye 

Artificial Intelligence In Mental Health

When it comes to using AI in healthcare, it’s not just about physical health; mental health has benefited greatly from the technology. Machine learning algorithms have been made available by Harvard University academics to monitor traits and symptoms related to mental health. 

As the prevalence of mental illness has been increasing daily, AI models are now able to assess material on suicidality, sadness, and devastation. 

AI can also be used to identify chemical alterations in the human brain that indicate a certain sort of mental disorder. The most prevalent disease among all the symptoms is dementia. 

There are numerous different types of dementia recognised by medical research, with Alzheimer’s being the most prevalent. Our memory and communication are primarily affected by Alzheimer’s disease.  

Examining the soundness of human speech has become possible thanks to deep learning and AI for audio processing. AI systems are trained to distinguish between a healthy speaker, or rather, a speech, and one exhibiting dementia signs. 

Cloud Hosting And Storage Of Data

We already know that cloud hosting and cloud computing services are far more secure than other conventional services. When it comes to functionality and coherence, HIPAA compliant cloud hosting is a pretty good choice for any healthcare company that needs electronic health records (EVR). 

We shouldn’t limit our discussion of cloud hosting and data storage to just teleconferencing and hosting data at this point. The security, appointment administration, secure messaging, location services, visit history, wearable integration, and other aspects of cloud hosting are also extremely good. These are some additional standout characteristics of the cloud. 

There are healthcare AI technologies like Google Health in addition to cloud hosting services. The goal of this AI healthcare tool is to improve everyone’s quality of life by providing goods and services that connect and contextualize health data. 

In order to enable care teams to deliver more connected care, it is creating technology solutions. Additionally, Google Health is investigating the use of AI to aid in the diagnosis of cancer, preventing blindness, and much more.. 

Healthcare's Augmented Reality and Mixed Reality Wonders

In AI healthcare businesses, augmented reality and mixed reality are pushing the envelope of traditional medical procedures. especially if you get to watch surgeons use mixed reality headsets like the Microsoft Hololens 2. 

These headsets can provide the surgeon with heads-up information while allowing them to operate with both hands free. 

These operations can not only have an effect on the head-up information, but they can also be quite helpful for training purposes. Doctors have the ability to quote their inputs if necessary thanks to a head-mounted camera view. 

The use of augmented reality is not limited to headgear and operating rooms. Additionally, the technology is usable  to allow nurses to find veins to draw blood from to come as a life savior. 

Smartwatches, such as wearables, are made to function dynamically. They are also striving to make it possible to measure a user’s blood pressure. Photoplethysmography (PPG) is an optical technique used to track changes in blood volume and composition. 

It is so small that it can operate on smartwatches and provides users with more information about their blood vitals than ever before. Startups in AI healthcare can use this information to accurately and more effectively diagnose their patients. 

Adaptive Drugs

The term “smart pills” refers to tiny electronic devices, sometimes shaped like pharmaceutical capsules, that are used to conduct very complex tasks like sensing, imaging, and drug delivery. They could be pH, chemical, or biosensors, as well as images or biosensors. 

These tiny pills explore your GI tract while gathering pressure, PH, and temperature information. After the capsule is consumed, the information gathered by the capsule is collected by a data receiver that is fastened to a belt. 

Organ Evaluation And Transport

Speaking about organ evaluation, Trans medics invention, the organ care system, provides ongoing assistance. The vital body organs like the heart, lungs, and liver can be retained outside the body for a few hours with this organ care system. They are receiving proper medical attention, as well as essential nourishment and care. 

 

According to specialists, however, this technology will have a bright future in the age of artificial intelligence since it will be able to operate on its own, without the need for a doctor’s intervention, and preserve the organ for a longer period of time. 

Bioprinting: The Process Of New Organ Creation

The idea of creating new organs is fairly enormous, therefore this is pretty astonishing. Even so, 3D-printed organs are rather realistic in appearance. Even though it is still in the development stage, the technology has already entered clinical testing. Body parts being tested in clinical settings for 3D bioprinting include bones, skin, ears, and corneas. 

Cancer Diagnosis

Years have passed since biopsy was thought to be the only accurate method of cancer diagnosis. The biopsy is the procedure used to remove tissue in order to diagnose the illness. 

 

However, as AI in healthcare has developed, the most sophisticated histopathology techniques now rely on digital scans of the specific region that can be impacted by cell mutations. Pathologists may now investigate far bigger sections of human beings at once thanks to the use of whole slide images, or WSI. 

Even though the WSN scans are highly thorough and insightful, the process is still laborious and painful because you must go through each and every aspect before the inspection can assist you with the outcome. 

However, this is no longer a concern because convolutional neural networks and computer vision may be used to handle AI applications. This method highlights the area of interest where probable cancer cells may be located, which helps shorten the time for diagnostics and eases the burden on medical workers. 

Medicine/ Drug Discovery

Clinical trials are still the standard way of developing medications, but they take a long time and cost a lot of money. The process of creating pharmaceuticals has sped up and become relatively inexpensive when AI drug production was introduced to the healthcare sector. 

Small-molecule drug development can benefit from artificial intelligence in four straightforward ways: access to new biology, improved or original chemistry, higher success rates, and speedier and less expensive discovery procedures. This specific technique can solve several problems and restrictions in conventional research and development.  

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