AI in Healthcare

• By Sandeep Gupta
• 23-08-2024
• Artificial Intelligence

According to Statista, the market size of artificial intelligence in healthcare can grow from 11 billion USD to 188 billion USD worldwide, rising at a compound annual growth rate (CAGR) of 37% from the forecasting period of 2022 to 2030.

The reason behind this immense growth potential of AI in the healthcare sector is simply its ability to streamline diagnostic processes, allowing healthcare providers to screen more patients in less time and thus reducing patient waiting time. By incorporating AI in hospitals, the patients' hassle can be turned into an easy and smooth experience. Moreover, AI would be the right hand for healthcare management and assist them in keeping the patient's sensitive data, including their personal and treatment details, intact and readily accessible to the doctors for effective treatment.

In this blog, we will go through the role of AI in the healthcare industry, how it has transformed conventional healthcare operations, and what the future  holds.

How is AI revolutionizing healthcare?

To have a comprehensive understanding of the impact of AI in healthcare, let us distinguish between the conventional healthcare sector and AI-influenced healthcare sector through the table given below:

Areas

Appointment Management

• Appointments are set manually or using inefficient and complex software, leading to clashes and long waiting times.
• AI can optimize the scheduling process, reduce patient waiting time, and prioritize critical cases.

Patient Onboarding

• Manual form-filling processes delay patient onboarding and are prone to errors.
• AI software automates the form-filling process, which reduces the chances of errors and speeds up patient onboarding.

Initial Clinical Evaluation & Diagnosis.

• The medical practitioner will note the patient’s condition, strategize, and make a treatment plan.
• With AI data retrieval, EHRs, and data analysis, doctors can get insights into patients' past health records and make effective treatment plans using AI-recommended best-suited treatment options.

Care & Treatment Implementation

• It follows a “one-shoe-fits-all” treatment and has the potential for errors and negligence of key symptoms of the patients.
• AI monitors patient data and health 24/7 through wearables and suggests personalized treatment plans. Moreover, if the health is deteriorating, doctors are alerted for immediate intervention.

Patient Observation

• Resources for continuous monitoring are limited, and geographical barriers exist.
• With the help of IoMT, AI monitors the patient’s health 24/7. Also, AI telehealth increases the accessibility of health services to less privileged areas, thus reducing geographical barriers.

Financial Documentation & Revenue Collection

• Conventional healthcare systems have slow and complex billing processes and require manual intervention.
• AI automation helps the patients in doing billing quickly, and has less chances of errors, thus more accuracy.

Health Information Management

• Record management is mostly manual, and can be time-consuming, and may have errors.
• With AI-driven EHR systems, AI automated the record-keeping process, making it faster, more accurate, and hassle-free for hospital management.

Discharge, Follow-Up & Aftercare

• Most of the healthcare providers fail to provide proper aftercare to patients.
• AI tracks the patient's data and health all the time and thus can make predictions of the patient's recovery accurately.

Thus, AI is really bringing some amazing transformations in the field of health and is making the experience of both patients and healthcare management hassle-free.

Types of AI in healthcare.

The following are the key technologies by which AI is incorporated into healthcare systems in one manner or another.

Machine Learning

Machine learning refers to using statistical techniques to make the machines learn. In ML, machines are fed large amounts of relevant, high-quality datasets and are trained via algorithms to perform specific tasks like clinical decision-making. All the machine learning models improve their performance, efficiency, and accuracy with every use.

Neural Networks & Deep Learning

Neural networks are interconnected node networks that allow the machine to function like the human brain. They have three types of layers- inner, outer, and hidden layers. Most of the processing happens in the hidden layer, and when the number of hidden layers is two or more, it becomes a deep learning network. NN and DL networks allow machines to process complex information and provide results faster.

Natural Language Processing

Natural language processing (NLP) is an AI technique that makes machines interpret and respond to human languages. The input can be text or speech, and one can normally text or talk to the AI model naturally. Thus, NLP has transformed the way human beings interact. Natural language processing is widely used in different software applications, including generative AI models like virtual assistants and chatbots. If healthcare institutions partner with the best generative AI development company, like BuildFuture AI, they can enhance their patient experience by leveraging the benefits of generative AI.

Computer Vision

Similar to NLP is computer vision, which enables the AI models to process and understand visual inputs like images, videos, and so on. Computer vision is what sets the base for AI models that are used to streamline medical imaging processes. It allows medical imaging systems to rapidly detect any abnormalities, even minor ones, which may be overlooked by radiologists or lab staff.

Rule-Based Expert Systems

Expert systems are like human experts in a particular field. They have a bunch of “if-else” decision-making loops that help medical practitioners make fast and more informed clinical decisions. Expert systems are built with the assistance of human experts and knowledge engineers, and both of them set the rules of the algorithms. If the number of rules is quite large (over several thousand), rules may conflict with each other, and the system breaks. However, making changes to the rules can be a little time-consuming.

Internet of Medical Things (IoMT)

Having artificial intelligence embedded in hospital software applications, workflow systems, wearables, sensors, and medical devices would keep a 24/7 check on the patient’s health and data. It would record all the patient information, which can further be used to provide doctors with some amazing insights. IoT platforms can be used to provide electronic healthcare services to people.

Physical Robots

Approximately two hundred thousand robots are being installed in various industries. Physical robots can perform hefty tasks like lifting, repositioning, welding, or assembling objects in their respective positions. However, as the use of robots is increasing day by day, they have also started entering the field of healthcare. Surgical robots are now being used in some parts of the world to perform precise surgeries with small incisions and so on. In the future, surgical robots will be seen in every hospital.

Robotic Process Automation (RPA)

Robotic process automation refers to bots that automate manual and repetitive tasks that are usually performed by human beings. These tasks are mostly less productive and time-consuming. RPA programs are inexpensive, simple to code, and transparent, and save important resources like time and money for any organization. Appointment booking, record management, data entry, patient billing, authorization, authentication, etc. are some tasks that RPA systems automate.

Use cases of AI in Healthcare

Let us now look into the areas where AI is used in the healthcare sector:

Electronic Health Records

Electronic health records are the base of modern healthcare operations and contain patient health information in every aspect, including patient’s medical history, diagnosis, immunization data, allergies, test reports, past medical images, treatment plans, medications, and so on. AI can automatically update these EHRs in real-time and provide fast and easy access to both patients and doctors whenever required.

Radiology

It is a fact that the ratio of the number of medical practitioners to the number of patients is lower. This difference leads to delays in providing people with timely treatment. By utilizing AI in the radiology department, radiologists can rapidly screen different medical images like MRs, CT scans, ultrasounds, and X-rays, which would bring an increase in the number of patients being screened each day. Moreover, when it comes to detecting complex cases, AI is a great help to radiologists.

Medical Data Analysis

For patients, AI can track their health through wearables and constantly monitor their health patterns. AI will alert doctors when their immediate intervention is required. Also, it would produce patient health insights, which will help the doctors customize the treatment plans according to the patient’s recovery pace. For researchers and the government, this data can be used to get statistics on population and public health trends, for example, which diseases are occurring more than often, which are getting diminished, etc.

Drug Discovery

The process of drug discovery is quite expensive and time-consuming. For drug discovery, researchers need to find various compounds with different amino acid patterns. Screening for relevant patterns in the drugs or living beings and then performing computational analysis with laboratory testing is a complex and demanding task. Now, since AI is a master of data analysis, combining bioinformatics with artificial intelligence can be a dynamic tool that can streamline drug discovery processes. AI would help research teams find the drugs and monitor all the data and performance to build insights that would speed the scientific analysis.

Robot-Assisted Surgery

Robots performing surgeries like stitching wounds, etc. are another great innovation that AI has brought to the healthcare industry. This can help doctors perform more surgeries rapidly and with more precision, thus reducing the waiting lists for patients. Moreover, with robots performing surgeries, the chances of human mistakes will be less.

Personalized Treatment

By monitoring the EHRs of each patient individually, AI unlocks the patients' health patterns and makes treatment plans that closely align with the patient’s past health history and body requirements. All the insights produced by AI for doctors help the faculty provide personalized treatment and care to all patients. This results in prolonged and fast recovery of their patients, which in turn makes sure that they need to visit the hospital premises less.

Virtual Chatbots

Having AI chatbots in the hospital’s mobile applications and websites would let people ask for any kind of query they have. However, in hours of emergency when doctors or nearby hospitals might not be available, AI chatbots can help people by providing guidance on first aid and home-based treatment for immediate pain relief. For healthcare management, AI chatbots can assist the public in appointment scheduling and administrative work. Therefore, AI chatbots would reduce the pressure on management and be a 24/7 virtual medical assistant for people.

AI Telehealth

AI telehealth services diminish geographical barriers all over the world and allow people to get remote healthcare services and medical advice from doctors specializing in different domains worldwide. This remote health approach minimizes hospital visits and saves time, money, and other resources while providing proactive care to patients.

Enhanced Patient Experience

With reduced waiting lists, 24/7 AI chatbot assistance, remote doctor consultations, personalized treatments, and faster medical imaging processes and diagnosis, AI brings a bag full of benefits for patients. This makes the patient’s health journey quite smooth, easy, and hassle-free. Thus, implementing artificial intelligence into healthcare systems would enhance the patient experience and improve the overall health of society.

Final Thoughts!

AI has successfully entered the field of healthcare and is already transforming its operations with automation, data analysis, and personalization. When it comes to imagining how big this role of AI in healthcare can get, the possibilities seem to be endless.

As we continue to uncover what else AI in healthcare can bring, researchers and healthcare providers must also consider the ethical aspects and risks that AI might bring. First of all, safety protocols and compliance must be ensured to protect sensitive patient’s personal and health details, thereby avoiding any kind of data breaches. Secondly, AI chatbots must be trained with high-quality datasets to ensure their accuracy. The public must be taught that the responses of AI chatbots in case of health emergency are just a medium to get initial assistance and not proper medical treatment. They would still need to visit a doctor afterward. Lastly, AI can assist but never replace, healthcare practitioners or scientists in treating patients or making drug discoveries. To overcome such challenges, it is better for healthcare institutions to collaborate with a reliable AI development company like Blocktech Brew that has the right expertise to build high-security, efficient, and user-friendly healthcare software applications.

However, AI still holds unrealistic potential that can change the healthcare industry for the better. Therefore, with careful and cautious usage, AI in the healthcare industry must continue to evolve and bring the best possible benefits to humankind.