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Revolutionizing Healthcare: The Adoption of AI for Advanced Treatment and Quality Services

adoption of ai in healthcare

Every year, roughly 400,000 hospitalized patients suffer preventable harm, with 100,000 deaths. In respect of that, the promise of improving the diagnostic process is one of Artificial Intelligence's (AI's) most exciting healthcare applications.  

It all started with IBM's Watson intelligence system. This intelligence system was developed to answer all questions accurately and quickly. IBM launched a healthcare-specific version of Watson 2011 focussed on natural language processing. The technology was used to understand and interpret human communication accurately. Today, apart from IBM, other tech giants such as Apple, Microsoft, and Amazon also invest in AI technologies for their use in healthcare.

AI and related technologies have started being applied to healthcare. With the potential to transform various aspects of patient care, AI is better than humans at key healthcare tasks such as diagnosing disease. Today, algorithms have already started outperforming radiologists at spotting malignant tumors. For many reasons, it could still be years before AI replaces humans for broad medical process domains.

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Rise of AI in the Healthcare Sector

The demand for healthcare services keeps on increasing, and many countries experience a shortage of healthcare practitioners, especially physicians. Healthcare institutions are also fighting to keep up with all the new technological developments and patients' high expectations.

The advances in wireless technology and smartphones have provided opportunities for on-demand healthcare services using health tracking apps and search platforms and have also enabled a new form of healthcare delivery via remote interactions, available anywhere and anytime. Such services are relevant for underserved regions and places lacking specialists and help reduce costs and prevent unnecessary exposure to contagious illnesses at the clinic. 

Telehealth technology is also relevant in developing countries where the healthcare system is expanding, and healthcare infrastructure can be designed to meet the current needs.

What are the Objectives of AI in Healthcare?

The objectives of applying AI to the healthcare sector include the following:

  • Increasing effectiveness of diagnostic processes: There is a possibility of human error in healthcare settings if there is a large caseload and a lack of proper medical history. AI, if implemented in healthcare, improves diagnostic accuracy. AI systems can easily diagnose and detect diseases quickly with minimum risk of errors.
  • Reducing overall healthcare costs: If AI makes diagnostic processes more efficient, it will also reduce the cost of healthcare procedures. Also, patients will receive faster and more effective care, further reducing the need for hospitalization and long waiting times.
  • Safer surgeries: AI is also establishing its position by offering effective and distinctive aid in surgical procedures. AI has the capacity to be precise during surgery while operating on delicate organs and tissues. AI's ability to perform less invasive surgeries provides a range of benefits to patients, including the following:
    • Reduced blood loss
    • Decreased risk of infection
    • Less pain after surgery
    • Reduced scarring 
    • Faster recovery
  • Easy information sharing: AI's algorithm analyzes a ton of data information quickly and precisely. AI can help healthcare professionals understand any disease through data via a real-time monitoring system.

Which Key AI Technologies Are Used in the Healthcare Sector?

The key AI technologies used in healthcare sector are mentioned below:

Machine Learning

machine learning

The data science behind machine learning can help healthcare professionals improve treatment and reduce costs. By altering the healthcare system, machine learning has enabled AI to be used in the diagnosis and treatment of diseases. 

The benefits of using machine learning include the following:

  • Quickly process large amounts of clinical documentation
  • Identify the patterns of medical outcomes with greater accuracy
  • Makes predictions about medical outcomes with precision

The use of machine learning can help doctors accurately diagnose illnesses and customize treatments according to patients' needs. Tasks such as disease diagnosis or drug discovery and development can be easily done with the help of machine learning.

Precision medicine is the most widespread utilization of traditional machine learning. It can help predict what treatment procedures are most likely to be successful with patients. This is based on the patient's genetic makeup and can be a huge leap forward for the data science of many healthcare organizations.

Natural Language Processing (NLP)

NLP is a form of AI that enables computers to interpret and use human language. In healthcare, NLP is used in a wide range of health data applications, such as:

  • Improving patient care through better diagnosis accuracy
  • Streamlining clinical processes
  • Providing more personalized services

The use of NLP in the healthcare sector includes:

  • It can be applied to medical records to accurately diagnose illnesses by extracting useful information from health data. 
  • It can be used to identify relevant treatments and medications for each patient.
  • It can predict potential health risks based on past health data. 
  • It provides clinicians with powerful tools for managing large amounts of complex data (which would take much longer to do manually).

NLP proves to be an invaluable tool in healthcare as it allows medical professionals to diagnose illnesses more accurately and provide better personalized treatments to patients.

Computer Vision

computer vision

Computer vision, a rapidly growing technology, leverages powerful AI algorithms with optical sensors and cameras. As a result, it can help doctors and medical professionals to:

  • Quickly identify diseases 
  • Provide an accurate diagnosis
  • Offer personalized treatments for patients
  • Monitor medication use
  • Predict health outcomes

Both doctors and patients may benefit from the use of computer vision in healthcare applications. It reduces the number of false positives and diagnostic errors. It does so by giving a second opinion on diagnostic conclusions and then detecting the most minute abnormalities that can be overlooked during manual observations.

Computer vision can be an invaluable aid in surgical procedures as it can: 

  • Help prepare for surgery
  • Keep track of surgical instruments before and after
  • Assist experienced specialists in training their younger colleagues

What are the Examples of Artificial Intelligence Applications in Healthcare?

The examples of AI applications in healthcare sector include the following:

Diagnostic AI

Medical diagnosis can be improved with the evolution of AI. AI can revolutionize the field by improving the prediction accuracy, speed, and efficiency of the diagnostic process. The use of diagnostic AI in healthcare includes the following:

  • Can analyze medical images such as X-rays, MRIs, ultrasounds, CT scans, etc. 
  • Can assist in identifying and diagnosing diseases more accurately and quickly. 
  • Can analyze a large number of patient data, including vital signs (e.g., body temperature, pulse rate, respiration rate, and blood pressure), medical 2D/3D imaging, bio-signals (e.g., ECG, EEG, EMG, and EHR), demographic information, medical history, and laboratory test results. 
  • Can support decision making and provide accurate prediction results. 

Predictive Analytics

In healthcare, predictive analytics is a process of analyzing historical healthcare data. This is done to identify patterns and trends that may be predictive of future events. In healthcare, it can be used to predict the likelihood of particular clinical decisions, health conditions, trends, and even disease spread. It has the potential to improve the quality and efficiency of healthcare delivery.

The use of predictive analysis in healthcare includes the following:

  • Helps healthcare providers make more knowledgeable decisions about the treatments to be offered to the patient. 
  • Helps provide the best treatment tailored to individual needs.
  • Helps recognize patients who are more at risk of developing complications or relapse and provides interventions before problems occur. 

Robotic Surgery

robotic surgery

Surgical robotics is one of the best frontiers for optimizing AI. Robotic surgery can utilize AI to improve patient outcomes, elevate the level of care, and reshape the healthcare industry.

The use of robotic surgery in healthcare includes the following:

  • Since there is no constraint of time or memory, AI-based systems can be given recordings of a number of surgeries in seconds. It can remember the first procedure they observe with equal precision to the last.
  • AI can help surgeons introduce new methodologies to existing surgical procedures and subsequently standardize practices. 
  • With the help of data analytics from all over the world, AI can collect varying images, recognize microscopic differences, and introduce new trends.
  • AI-based systems can guide surgery and ensure a more streamlined process by highlighting tools, monitoring operations, and sending alerts.
  • AI-based systems, if paired with technologically advanced robotics, have the potential to ensure that patients can receive the proper surgical care they deserve.
  • Surgeons who only perform one type of procedure today can broaden their impact with the help of AI-based robotic surgery.



Telemedicine is the most suitable and sustainable approach to delivering healthcare services to patients by incorporating technological advancements in affordable and low-cost implementations. 

These technologies enable human medical specialists to save time by reducing the need for constant patient monitoring and facilitation of healthcare consultations and personalized visits. Cognitive-computing-powered systems effectively process and interpret incoming health-related signals while engaging in conversations with patients regarding minor or routine issues such as health self-assessments and checkups. 

AI offers a wide range of use cases in telemedicine solutions, empowering healthcare providers and improving patient outcomes. These use cases exemplify the potential of AI to transform the way telehealth is provided and experienced.

The use of AI in telemedicine includes the following:

  • Analyzes symptoms and patient data to prioritize cases based on urgency, ensuring timely care for patients with critical conditions.  
  • Facilitates reduced wait times and enhances patient satisfaction.
  • It facilitates early detection of deteriorating conditions, allowing for timely intervention. 
  • Reduces the need for frequent in-person visits, providing convenience for patients.
  • Analyzes medical images, such as MRIs, X-rays, and CT scans.
  • Assists radiologists in making accurate diagnoses by analyzing medical images in bulk.
  • Accelerates the diagnostic process, leading to faster treatment decisions.
  • Enhances the efficiency of radiology departments, enabling them to serve more patients.
  • Handles patient inquiries, provides basic medical advice, and assists with appointment scheduling.
  • Improves patient engagement and access to information, enhancing overall patient satisfaction.

What are the Challenges and Shortcomings of AI in the Healthcare Sector?

The deployment of AI-enabled solutions in clinical practice still has many limitations. Besides privacy challenges, AI also presents other technical and methodological shortcomings. 

The top challenges of AI in healthcare include the following: 

  1. Lack of quality medical data: The healthcare sector should concentrate on techniques that can standardize medical data to increase the amount of data available for testing AI systems. Collecting data from patient information and images to test AI algorithms can be challenging. This is due to two major obstacles:
    • Fragmentation of medical data across different electronic health records (EHRs) and software platforms.
    • Medical data from two different organizations may not be compatible.
  2. Clinically irrelevant performance metrics: The criteria used to measure the success of an AI model may not be transferable to clinical settings. The developers and clinicians need to collaborate to avoid this gap and to know how AI algorithms enhance patient care.
  3. Methodological research flaws: The majority of studies on AI in healthcare have been retrospective and based on historical patient medical records. To realize the true value of AI diagnosis, there is a need to study current patients over time, which means prospective research. For reliable prospective research, specialists need to monitor patient health by combining physical examinations with telehealth visits and remote monitoring technologies.

To Conclude

While there are several barriers and challenges, AI in healthcare is here to stay because of its huge benefits to the overall healthcare ecosystem. The use of AI in healthcare plays a crucial role in accelerating the pace of the healthcare ecosystem with better care management practices and timely detection of diseases. 

nishu Author Name

A microbiologist by education, Nishu Negi clearly understands scientific/healthcare concepts and ideas. She is a creative content writer passionate about innovative concepts and expresses these with clarity in her writing. She enjoys writing blog posts and articles in a way that the target audience understands them. 

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