What is the Role of Artificial Intelligence in Healthcare? Real-World Examples & Future Trends

Just as in many other industries, AI has brought about a significant transformation in the healthcare sector. The integration of AI in Healthcare simplifies the lives of patients, doctors, and hospital administrators by performing tasks in less time and at a fraction of the cost.

From diagnostic assistance to personalized treatment plans, AI-powered healthcare software promises to optimize efficiency, improve accuracy, and ultimately save lives.

For organizations and business owners navigating this dynamic field, understanding the role of AI in Healthcare is pivotal. 

Are you looking to adopt AI-powered solutions? Or integrate AI into existing healthcare software? This blog will provide insights into the role of artificial intelligence in healthcare, real-world examples, and future trends that are set to revolutionize the industry.

AI in Healthcare: An Overview

AI encompasses various technologies, including machine learning (ML), natural language processing (NLP), and computer vision, that enable machines to mimic human intelligence. These artificial intelligence technologies help to analyze complex medical data, improve diagnostic accuracy, and automate routine tasks. It ultimately leads to better patient care and operational efficiency. 

Market Stats of Artificial Intelligence in Healthcare

The increasing prevalence of chronic illnesses, widespread adoption of mHealth solutions, and governmental efforts to digitize Healthcare are anticipated to fuel the demand for innovative healthcare systems.

Here are other essential stats & facts about AI in Healthcare or the intelligent healthcare market:

In 2022, the Smart Healthcare Market reached a valuation of USD 184 billion and is projected to approach USD 541 billion by 2032. 

In 2021, the artificial intelligence (AI) in healthcare market was estimated at USD 11 billion worldwide. It was forecast that the global healthcare AI market will reach around USD 188 billion by 2030, increasing at a CAGR of 37% from 2022 to 2030.   

• AI-based diagnostics demonstrate 90% accuracy in identifying specific medical conditions. 
• AI has enhanced the efficiency of drug discovery processes by as much as 70%. 
• AI algorithms achieve a detection accuracy of around 90% in recognizing different types of cancer. 
• More than 70% of patients report satisfaction with AI-driven virtual health assistants for handling basic healthcare inquiries. 

On January 16, 2024, an MGMA Stat poll asked medical group leaders what will define AI in Healthcare in 2024. 58% pointed to positive outcomes—about 30% said innovation would be the defining trait of healthcare AI, while 28% said convenience would be what to expect. 

Even most healthcare organizations prefer to hire LLM developers for diagnosis and clinical care, administrative tasks, education, drug research and development, and patient-guided learning. 

The World Health Organization (WHO) released new guidelines on the ethics and governance of large language models (LLMs) in healthcare industry. 

Role of Artificial Intelligence in the Healthcare Industry

Here are some key roles and contributions of AI in Healthcare:

#1 - Cancer Diagnostic Assistance

One of artificial intelligence's most significant contributions to healthcare is its ability to diagnose and detect diseases like cancer early. 

The global AI in cancer diagnostics market size was estimated at USD 137.8 million in 2022 and is expected to grow at a CAGR of 26.3% from 2023 to 2030. 

AI-powered algorithms can analyze vast amounts of medical data, including imaging scans, pathology reports, and genetic information, with unprecedented speed and accuracy. It helps to improve the speed, accuracy, and reliability of some cancer screening and detection methods. For example: 

• The Food and Drug Administration has authorized AI-driven software to assist pathologists in detecting potential cancerous areas within prostate biopsy images. 
• NCI (National Cancer Institute) claims that AI imaging algorithms not only improve breast cancer detection on mammography but can also help predict the long-term risk of invasive breast cancers.
• Researchers supported by the NCI have created AI models using deep learning to predict commonly used drug response pathways, providing detailed maps.
• The use of large language models for electronic health record surveillance is aiding researchers in gaining insights into the social determinants of health that are crucial for cancer prevention, detection, and treatment.  

#2 - Personalized Treatment Plans

AI can analyze patient data, including medical records, genetic information, lifestyle factors, and treatment outcomes, to develop personalized treatment plans. It enables healthcare providers to customize therapies and interventions to individual patients, improving treatment efficacy and patient outcomes. 

For example, AI can predict how a patient might respond to a particular medication based on their genetic profile and medical history.

#3 - Drug Discovery

One of the most interesting roles of AI in drug discovery and healthcare is accelerating drug discovery processes by analyzing vast datasets and more efficiently identifying potential drug candidates.

The global market revenue of AI in drug discovery was USD 0.9 billion in 2023 and is predicted to reach USD 4.9 billion by 2028, growing at a CAGR of 40.2% during this forecast period.

Machine learning algorithms can predict the biological activity of molecules, simulate clinical trials, and identify patient populations most likely to benefit from new treatments. It not only speeds up the research and development cycle but also lowers costs associated with bringing new drugs to market.

For example, 

AI and ML algorithms have the potential to classify new species of fungi, particularly mushrooms, and identify their natural compounds through image-based recognition. These technologies can explore the following properties of various mushroom and fungal species: 

• Anti-cancer, 
• Immune modulating,  
• Anti-neurodegenerative,  
• Anti-inflammatory, 
• Antioxidant properties. 

#4 - Neurological Disorders Treatment

The role of AI in Neurology is to improve comprehension of brain functions, neurological disorders, and cognitive processes through medical images, genomic information, and patient records.

The global Artificial Intelligence in Neurology and brain science market size was estimated to be worth USD 20.44 billion in 2022 and is expected to reach USD 134.65 billion by 2030, increasing at a CAGR of 23.9%.

AI, through ML, deep learning, and NLP, supports the creation of predictive models, diagnostic tools, and treatment recommendations for neurological conditions such as Alzheimer's disease, Parkinson's disease, epilepsy, and others.

The AI integration in business processes related to neurology and brain sciences has the potential to transform medical research and clinical procedures. It offers more personalized and practical approaches to enhance brain health and patient outcomes. 

#5 - AI Virtual Healthcare Assistants

AI-powered virtual health assistants (VHAs) provide patients with personalized support, answering questions and scheduling appointments. Additionally, AI healthcare assistants can reduce costs by automatic routine tasks. 

In 2022, the global market for health-intelligent virtual assistants reached USD 320.7 million and is projected to grow at a compound annual growth rate (CAGR) of 24.7% from 2023 to 2030.

Medical Virtual Assistants (MVA) can function via AI-enabled chatbots for customer management, voice interfaces, or integration into healthcare applications. Factors driving the Healthcare Virtual Assistants Market include: 

• Rising demand for remote healthcare services 
• Growing emphasis on optimizing healthcare operations 
• Increasing focus on enhancing patient engagement and satisfaction 
• Regulatory backing and compliance obligations 

Nuance Communications Inc. (US), Microsoft (US), and Amazon (US) are the major players in the Healthcare Virtual Assistants Market. 

So, if you have an online healthcare business, it’s essential to integrate AI chatbot into your website for 24x7 virtual healthcare assistants.

#6 - Predictive Analytics

Healthcare providers are using AI for predictive analytics to forecast potential health issues in patients. By analyzing data from wearable devices, electronic health records (EHRs), and other sources, AI can identify risk factors and alert healthcare professionals to intervene early. This proactive approach can prevent complications and hospitalizations, thus improving overall population health.

#7 - Remote Monitoring and Telehealth

With the rise of telehealth, AI plays a pivotal role in remote patient monitoring. Wearable devices equipped with AI algorithms can track vital signs and detect deviations in real time, alerting healthcare providers to intervene when necessary. This capability is precious for managing chronic conditions and ensuring continuity of care outside traditional clinical settings.

The AI in Remote Patient Monitoring Market is expected to reach USD 24 billion by 2033, increasing at a CAGR of 26.6% from USD 2.3 billion in 2023. Wearable devices lead the market, capturing a significant revenue share of 61.2% in 2023, facilitating real-time health monitoring.

The use of artificial intelligence in the telehealth is expected to expand rapidly, with a predicted market size of USD 1.73 billion by 2027.

From enhancing chronic disease management to improving patient scheduling efficiency and reducing healthcare costs, AI is revolutionizing the way Healthcare is delivered remotely. 

#8 - Improved Electronic Health Records (EHR)

AI-powered EHRs help analyze health records to improve diagnostic accuracy and predict complications. NLP, image recognition, and smart input suggestions enhance operational efficiency and streamline patient records management for physicians and nurses.

It also automates data entry, reducing errors, and uses Natural Language Processing (NLP) to organize information. AI provides real-time clinical decision support, predicts patient outcomes, and enables personalized treatment plans by analyzing large datasets.

It improves patient engagement through AI-powered chatbots and virtual assistants, optimizes workflows by streamlining administrative tasks, and ensures compliance with clinical guidelines.

AI systems streamline medical billing and coding processes, reducing errors and expediting insurance claims.

#9 - Ethical Considerations and Challenges

While AI offers immense potential in healthcare, it also raises ethical concerns regarding patient privacy, algorithm bias, and the impact on the doctor-patient relationship. Healthcare providers and policymakers must address these challenges to ensure responsible and equitable AI implementation in healthcare. It is seamlessly integrating AI technologies with current healthcare infrastructure and workflows.

#10 -   Robotics in Surgery

The global surgical robots market is projected to increase from USD 9.1 billion in 2024 to USD 22.9 billion by 2035, with a CAGR of 8.7% during the forecast period (2024 - 2035).

AI-powered surgical robots assist surgeons in performing complex procedures with high precision, reducing the risk of complications and improving patient outcomes. AI aids in designing personalized rehabilitation programs and monitoring progress, helping patients recover more effectively.

The role of AI in healthcare is expected to grow as technology advances and more data becomes available. AI will likely continue to drive innovations in preventive care, early diagnosis, and personalized treatment, ultimately improving patient outcomes and healthcare delivery worldwide.

Top 5 Real-World Examples of Artificial Intelligence in Healthcare 

Here are the top five real-world examples of AI applications in the healthcare industry:

1. IBM Watson for Oncology

IBM Watson for Oncology leverages AI to help oncologists develop treatment plans. It analyzes large volumes of medical literature, clinical trials, and patient data to provide evidence-based treatment recommendations.

Features of IBM Watson for Oncology: 

• Improved Treatment Plans: Helps in formulating personalized treatment plans based on the latest research and patient-specific data. 
• Efficiency: It reduces the time oncologists spend researching and planning, allowing them to focus more on patient care. 

2. Google DeepMind and Moorfields Eye Hospital

Google DeepMind partnered with Moorfields Eye Hospital to develop AI algorithms capable of detecting eye diseases from retinal scans. The system uses deep learning to identify conditions like age-related macular degeneration and diabetic retinopathy. 

Features of DeepMind: 

• Early Detection: Enables early diagnosis of eye diseases, which is crucial for preventing vision loss. 
• Accuracy: Achieves diagnostic accuracy comparable to that of experienced ophthalmologists. 

3. PathAI 

PathAI uses machine learning to assist pathologists in diagnosing diseases from pathology slides. The AI system can identify patterns and anomalies that may indicate cancer or other diseases.

Features of PathAI: 

• Enhanced Diagnostics: Improves the accuracy of pathology reports, reducing the likelihood of misdiagnosis. 
• Speed: Accelerates the diagnostic process, allowing for quicker treatment decisions. 

4. Aidoc

Aidoc develops AI-powered radiology tools that help radiologists detect abnormalities in medical images, such as CT scans and X-rays. The AI system flags potential issues for further review by radiologists.

Features of Aidoc: 

• Efficiency: Increases the speed of image analysis, leading to faster diagnosis and treatment. 
• Accuracy: Enhances the detection of critical conditions like intracranial hemorrhages and pulmonary embolisms. 

5. Babylon Health

Babylon Health provides an AI-powered health consultation service through a mobile app. Users can input their symptoms, and the AI chatbot provides preliminary medical advice and recommendations.

Features of Babylon Health: 

• Accessibility: Offers convenient access to healthcare advice, especially for those in remote or underserved areas. 
• Cost-Effective: Reduces the need for in-person consultations for minor health issues, lowering healthcare costs. 

These examples highlight how AI is transforming Healthcare by improving diagnostic accuracy, personalizing treatment plans, increasing efficiency, and making Healthcare more accessible.

What is the Future of AI in Healthcare?

The future of AI in healthcare promises enhanced diagnostics, personalized treatments, streamlined administrative tasks, and improved patient outcomes. Innovations in AI will drive precision medicine, predictive analytics, and real-time data analysis. AI will make Healthcare more efficient, accessible, and effective in addressing complex medical challenges.

Future Trends of Artificial Intelligence in Healthcare

Telemedicine and IoMT for monitoring patients’ health

Telemedicine and the Internet of Medical Things (IoMT) are transforming Healthcare by enabling remote monitoring and management of patients' health. These technologies offer numerous benefits, including improved access to care, enhanced patient outcomes, and cost savings.

Based on research from RockHealth.org, approximately 80% of Americans utilized telemedicine for healthcare services, while 46% of those surveyed indicated they owned a wearable device for monitoring their health status.

Advancement of Computer vision to patient care

Computer vision algorithms are increasingly used to analyze medical images such as X-rays, MRIs, and CT scans, aiding in the early detection of conditions like tumors, fractures, and neurological disorders. Enhanced accuracy and speed in diagnosis can improve patient outcomes and reduce diagnostic errors.

AI-powered cameras, sensors, and wearable devices can monitor patients continuously, detecting changes in vital signs, movements, and even facial expressions to alert healthcare providers to potential issues.

Neural networks for research and diagnostics

Neural networks can analyze vast amounts of genetic, clinical, and lifestyle data to predict disease risk and tailor treatments to individual patients. This personalized approach enhances the effectiveness of interventions and minimizes side effects.

Neural networks can process complex genomic data, identifying mutations and patterns associated with diseases and aiding in both research and clinical diagnostics. 

Handwriting recognition for digitizing medical records

AI-powered handwriting recognition or EHR can convert handwritten notes into digital records, streamlining data entry processes and reducing administrative burdens on healthcare professionals.

Digitized records reduce the risk of errors associated with manual data entry and provide a more secure and searchable database for patient information.

Artificial Intelligence is not just a technological advancement; it’s a vital step towards a future where Healthcare is more efficient, effective, and unbiased. The integration of AI into Healthcare can lead to better patient outcomes, reduced costs, and a more efficient medical system.

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