How AI & Deep Learning is helping the Healthcare Industry to Resolve Their Major Challenges?

Did you know that the Artificial Intelligence (AI) revolution has been one of the main pillars of success in many organizations for the last twenty years? With the introduction of machine learning and deep learning, each industry has started booming with results and adding AI as their top priority resource for quicker results. AI and deep learning are acknowledged everywhere and utilized across domains for various purposes. Organizations can now analyze and draw insights into customer data for better customer experience and allow users to operate devices anytime using AI. Like using Alexa, Google Assistant, or Apple's Siri as a voice bot to switch off the lights, listen to music, etc. That is how advanced the world has grown in recent times.

The impact of AI in Healthcare across domains is huge with 42% of companies exploring AI for its implementation in the future.

• Fortune Business Insights report forecasts that the global Artificial Intelligence market is projected to grow from USD 515.31 Billion in 2023 to USD 2,025.12 billion by 2030.
• According to projections, AI in healthcare market worldwide is expected to reach nearly $188 billion by 2030, with a compound annual growth rate of 37 percent from 2022 to 2030.

In this blog :

AI & Deep Learning in Healthcare - A Market Overview

As per market reports, the healthcare sector has made exceptional advancements in recent years using AI technology. The Healthcare industry has warmly embraced the adoption of AI and deep learning technologies to provide superior healthcare services that best serve the needs of their patients. To fully comprehend the significance of the impact of artificial intelligence (AI) on these advancements, it is crucial to understand the scale at which AI in Healthcare is utilizing data to analyze, predict, and provide the appropriate recommendations.

In 2021, the value of AI in healthcare statistics was around 11 billion U.S. dollars worldwide. As per the market forecast, the global healthcare AI market will be worth almost 188 billion U.S. dollars by 2030, increasing at a compound annual growth rate of 37 percent from 2022 to 2030.

• Data Segmentation - Every day healthcare organizations deal with huge volumes of data as millions of patients get treated across all departments. Yet, there has not been any significant process to organize data, as the manual segregation of data is very time and cost-consuming. AI in Healthcare plays a vital role in data segmentation and analysis, subsequently allowing quality monitoring, data analysis, and dissemination of insights on respective processes.
• Deep Learning Predictions - AI-powered tools have experienced a remarkable expansion in their capacity to detect diseases in patients before any symptoms arise. This advancement has enabled early treatment and increased chances of survival with reduced patient discomfort. AI in healthcare integration in diagnostic technology and imaging tools such as MRI, CT scans, and PET scans have streamlined the overall processes to measure and analyze symptoms faster than manual analysis.
• Drug Discovery - AI, machine learning, and deep learning in the healthcare industry can analyze clinical trial data and medical research to uncover previously unknown drug side effects. By leveraging AI & machine learning in clinical trials, organizations improved patient care, accelerated the drug discovery process, and improved the safety and efficacy of medical procedures.
• Digital Empowerment - With AI and machine learning algorithms in the healthcare industry, organizations are adapting digital support systems such as AI chatbots, web and app-based support, online bookings, and virtual consultations to increase the patient experience and make the overall system agile and cost-effective.

Challenges Faced by Healthcare Organizations

With the COVID-19 crisis leading to a series of benefits of AI in healthcare discussions and debates, AI has risen to the rescue with several factors which have helped ease patient care. Most patients start their healthcare journey through a Google search for their basic queries. As per a report by The Telegraph, David Feinberg, MD, Vice President of Google Health has stated that approximately 7% of Google's daily searches pertain to health-related topics. Here are some healthcare challenges that AI can resolve using machine learning and deep learning algorithms.

• Rising costs: Healthcare costs are increasing rapidly due to advances in medical technology, the aging population, and the growing prevalence of chronic diseases. Manual processing of patient data and paper costs are rising through the roof and are very time-consuming. In this scenario, AI use in healthcare is the right solution to segregate and structure data to save time and money.
• Access to healthcare: Millions of people around the world lack access to basic healthcare services, either due to financial barriers, geographical barriers, or other reasons. However, with digital empowerment, and online resources, patients can now access desired healthcare consultations and online medication information for the best treatments.
• Quality of care: Patients expect a quality treatment that is cost-effective and safe. But due to the current complexity of the healthcare delivery systems, patients are not getting the desired care. Patient experience is a vital part of a patient’s treatment from their first appointment to their last invoice processing. However, due to the intense manual processes, patients are not getting the desired individual attention. The lack of desired personalized care for the patients is causing a decline in the Patient Satisfaction Score.
• Regulatory compliance: The current healthcare industry is subject to a wide range of regulations and compliance requirements that are difficult to navigate and comply with. According to the American Health Association, healthcare providers are dedicating approximately $39 billion per year to comply with the administrative aspects of regulatory compliance in these domains. In the current healthcare industry, compliant regulations add up every year and interrupt the process of personalized patient care.
• Data management: The healthcare industry generates volumes of personal data every day. Without AI use in healthcare, It is a tough challenge for institutions to manage, analyze, and protect that data. With manual processes, institutions face challenges managing those patient data resulting in loss of money and time.
• Changing patient expectations: Patients expect personalized experiences, timely care, and better outcomes. With current manual processes, it is difficult for healthcare organizations to deliver the desired patient experience. Every minute clinics and hospitals are getting critical cases to attend to, and other patients do not get the desired individual attention. Implementing AI use in healthcare can lead to providing personalized services to every patient and improve the patient care experience  .
• Robotic Surgery: In an operating room, surgeons are in their best mindset with precise control and flexibility to take over the surgery. While the surgeons are skilled and trained for their jobs, robots act as assistants to help them maintain their control over the surgery. These robots come with increased skills of providing mechanical arms and a 3-dimensional camera view, which augments the surgical experience and provides minimal error during any operation.
  Robots use AI and deep learning data to guide surgeons by identifying the right tools, monitoring health data, and sending timely notifications. With AI use in healthcare, surgeons operate complex surgical procedures with ease.
  Many healthcare organizations have started using surgical robots for routine movements in the operation theatre. They are accurate and can go for long hours without showing any fatigue or malfunction, increasing the operation process and precision rate. With deep learning data, organizations integrate AI into these surgical robots for automated surgical actions for quicker treatment.

Opportunities Offered by AI to Healthcare Organizations

The global market scope for AI in Healthcare is beyond borders, and organizations like Oracle, IBM, and many more have been harnessing this technology for a couple of years now. As per market reports, these organizations received good responses, and many other organizations have started their venture into the AI revolution. With AI in healthcare, you can open up many opportunities and solutions to revolutionize and improve your business.

• Remote Patient Monitoring
  Remote patient monitoring (RPM) is a healthcare solution widely used to enable doctors to remotely monitor and track the health of patients with chronic or acute illnesses, elderly individuals receiving in-home care, and even hospitalized patients.
  There are currently numerous clinical-grade devices that patients purchase or utilize to measure and monitor their medical conditions, such as EKG, heart rate, heart rate variability, blood pressure, blood oxygen level, etc. We cannot manually analyze a thousand data points related to our health. But with these remote wearables, we can easily monitor our health, as they are developed with AI to track our complete body movements and alert an emergency when there’s a vital drop. Within the next 10 years, these RPM devices will be in huge demand since each patient will get personalized health updates on the spot while practices will have ample time to exercise remote patient care during medical emergencies.
• Drug Trials Development
  Drug development or discovery is no easy task as it takes more than ten years to develop an approved drug that can pass the safety regulations and have minimum side effects for its discovery. Sometimes these clinical trials can fail and set back an organization for months and years of effort. With AI use in healthcare, you can analyze and test protein mutation sequences and predict active protein structures that can reduce the time duration of drug discovery from 10 years to 5 years.
• Electronic Health Records
  EHR are sensitive patient details found everywhere within a hospital due to their manual process. These records are processed and managed manually, so it is very tough for the management to maintain and keep track of these patient records in physical files and folders. The healthcare management is very concerned about this manual process since there is no data privacy. With AI and deep learning technology, you can protect each data from getting misused. You can manage and segregate individual patient data in the system, and based on the patient's appointment, emergency level, schedule, medical visit, etc., you can utilize that data accordingly.
• NLP for Cancer Studies
  Natural language processing is the manipulation and interpretation of human-generated text or data. NLP helps in detailed diagnosis and analysis of all patients’ treatment, medical history, past illness, etc. With AI and NLP technology, you can extract the relevant information from unstructured clinical text data, such as electronic health records (EHRs), pathology reports, and radiology reports using NLP techniques. Natural language processing helps to get valuable insights into cancer treatment, prognosis, and outcomes.
• Patient Diagnosis
  In the Healthcare industry, AI and deep learning technologies act as valuable tools to help healthcare professionals make accurate predictions about their patients with quicker results, which aid in attentive patient care. The deep learning algorithm is used on large data sets of medical images, such as X-rays, MRIs, and CT scans, to accurately identify and classify abnormalities, such as tumors, lesions, etc., to get the desired information. This process can help radiologists and other healthcare professionals to make more accurate diagnoses and develop more effective treatment plans.
  Another important application of deep learning in patient diagnosis is genetic data analysis. By analyzing large datasets of genetic DNA information, deep learning algorithms can identify genetic markers associated with specific diseases or conditions. This methodology can help healthcare professionals identify patient diseases and take necessary medical actions.
• Improve Patient Experience with Healthcare Chatbots
  In this digital era, AI use in healthcare elevates the patient experience to the next level with the help of chatbots. With AI and deep learning, chatbots provide a guarantee of patient satisfaction. Healthcare chatbots respond to your patient’s concerns 24/7 and provide immediate solutions to all your queries. With AI in Healthcare, you can have minimum manual intervention and can cut down on several expenses with all-equipped AI tools. Healthcare organizations using AI chatbots improved their Patient Satisfaction Score by a good margin.
• Personalized Experience
  With personalization as the pillar of patient satisfaction, each patient is now receiving individual attention via AI chatbots and apps, which can offer personalized services and aid to individual patients. Improving the patient’s experience is strongly associated with positive patient outcomes, personalized virtual consultations, better health monitoring, and reduced hospital visits. By enhancing the patient experience, patients and health systems have improved their relationship to maintain a better client advocacy system.

Use Cases AI in Healthcare Organization

1. The high-tech hospital uses artificial intelligence in patient care
   Doctors at the University of Florida Health Center are using artificial intelligence to help monitor their patients.
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   The high-tech hospital uses artificial intelligence in patient care - YouTube
   
   Source- NBC News’ Dr. John Torres on the future of technology in healthcare.
   
   In this video, the patient is hooked to a monitor with cameras and sensors around the room detecting his every move. The AI attached to the PC processes more than 330 gigabytes of information regarding the patient’s movements and breathing patterns. The AI technology tracks their sitting and standing postures and provides a detailed behavior report of the patient. The system will analyze the findings using AI and deep learning algorithms and provide real-time healthcare recommendations.
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2. Companies Using AI for Vaccine Discovery
   Pharmaceutical companies have always leveraged disruptive technologies, including artificial intelligence (AI), machine learning, and deep learning to expedite drug discovery and medicine production processes. However, it started full-fledged when people and governments worldwide began seeking Covid-19 vaccines, and their efforts gained significant attention.
   
   How are Pharmaceutical Companies Using Artificial Intelligence? - YouTube
   
   - Astra Zeneca used knowledge graphs and image analysis to detect diseases 30% faster than human pathologists. The company also uses Machine Learning and chemistry Automation to shorten the lengthy drug discovery process.  
   
   - Pfizer, one of the World’s last pharmaceutical companies, used AI to discover one of the COVID-19 vaccines. Pfizer used AI to find the signal between thousands of data points to analyze the disease and progress toward making the vaccine.
   
   - Novartis collaborated with PathAI to incorporate AI into their medical research and implement it to decode cancer pathology images for quick and accurate detection. In 2016, they implemented machine learning to learn more about Breast cancer treatment, and in 2018, they conducted around 500 clinical trials with AI for accurate predictions.
   
   Many companies since then have collaborated with IBM Watson, Microsoft, Google, and many more tech giants to reap the benefits of AI in healthcare for accurate, quick, and innovative solutions.

Pros and Cons of AI in Healthcare

AI use in healthcare organizations is quite extensive, however there must always be a balance between excessive utilization of resources, whether AI or human. Here are a few benefits of AI in healthcare and some of the challenges associated with incorporating AI into healthcare.