Machine Learning In Healthcare: 10 Use Cases, Examples & Benefits

Unleash the power of ML

While the benefits of machine learning in healthcare are apparent and indisputable, its implementation requires significant resources and operational changes within medical organizations, which can't happen overnight. Still, while the demand for healthcare services is growing, banking on ML technology remains the best solution. For machine learning and artificial intelligence to solve healthcare legacy challenges, medical institutions must shift from tests and pilot projects to fully-functional machine learning solutions. And Itransition's ML experts are here to help.

According to a recent Goldman Sachs report, the healthcare industry is experiencing an innovative wave. This is due to the increasing use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare. While it takes over $2 billion and several years to create a drug, the use of AI and ML allows companies to create safe and effective drugs at a lower cost and reduced time. Some of the most common use cases of AI in healthcare include diagnostics, drug development, clinical trials, and healthcare analytics. Let’s understand the role of AI in healthcare in detail.

How Can Al in Healthcare Improve Patient Care?

Artificial intelligence has impacted the healthcare sector on a large scale. From detecting fatal diseases to helping people manage their diseases and lifestyle, healthcare organizations are using AI for the following purposes: 

• Detecting cancer

• Predicting heart transplant rejection rate

• Lifestyle and health monitoring by detecting sleep, heartbeat, and breathing patterns 

• Drug discovery

• Clinical trials

• Personalized treatment

What are the Key Benefits of Utilizing AI in Healthcare Diagnostics?

The world is witnessing the increasing use of AI and deep learning technologies to diagnose skin, liver, and heart diseases. Early and accurate diagnosis of such diseases helps in the timely treatment of patients. Some key benefits of using AI in healthcare for diagnosis are given below:

1. Enhanced Accuracy

AI algorithms can analyze vast amounts of medical data for accurate diagnosis of diseases at an early stage. It also helps reduce diagnostic errors. Technologies like AI and cloud computing systems allow healthcare professionals to process large amounts of data in comparison to on-premise tools. This helps improve accuracy.

2. Faster Turnaround Time

Another key benefit of using AI in healthcare: quick diagnosis of test results. AI tools analyze test results in a short span of time in comparison to manual diagnosis. Moreover, AI is also able to analyze genomic, metabolomic, and bioinformatic data of individuals in less time. It not only saves costs but also facilitates timely treatment.

3. Personalized Medicine

Disease processes and responses to treatments of humans vary as per their genetic, physiological, and behavioral conditions. Thus, it is essential to provide personalized treatments to all individuals. Furthermore, AI in healthcare can provide customized treatments and medicine plans for patients. This is done by doing a comprehensive analysis of patients’ underlying genetic information, clinical records, and treatment outcomes to identify patterns and correlations. AI can also be used to develop personalized medicines for specific health conditions during the clinical trial stage. 

4. Predictive Analytics

AI can also be used for predicting potential diseases based on the genetic conditions, health records, and lifestyles of patients. AI analyzes real-time electronic medical record (EMR) data of patients and predicts the outcome of treatments.

5. Clinical Guidance

Doctors and health experts can also harness AI to suggest suitable treatments for diseases and help doctors figure out the most favorable treatment.

ALSO READ: The Benefits of AI: How It Can Help Your Business

What are Some Notable AI Applications in Healthcare?

1. Medical Imaging and Diagnostics

AI is being widely used for diagnostic imaging, especially in radiation oncology. Health professionals use AI to analyze medical images such as MRI scans and X-rays for quick and accurate detection of diseases. It can accurately identify image abnormalities and extract image features that may not be visible to the human eye.

2. Lifestyle Management and Monitoring

AI-powered devices analyze sleeping patterns, physical activity, and other health-related activities of individuals. Such devices provide a complete analysis of the physical well-being of users and how they improve their health. AI devices can also help people manage chronic diseases such as diabetes which requires constant monitoring of blood sugar.

3. Drug Discovery

Healthcare research organizations are also using AI for drug discovery as it can significantly help in research, finding suitable drug formulas and drug designs.

4. Clinical Trials

AI in healthcare is also beneficial in clinical trials as it analyzes suitable patients for trials based on their medical records. Additionally, it also helps analyze patients’ responses to trials.

ALSO READ: How Top Applications of AI are Transforming Different Sectors

What Challenges and Limitations are Associated With AI Implementation in Healthcare?

While AI can certainly transform the healthcare sector, it does pose certain challenges, such as: 

1. Data Privacy

One of the biggest concerns regarding the use of AI in healthcare is gaining access to the personal data of patients or users. Medical records and other health-related data are considered sensitive personal data, and they require extra protection. AI and ML technologies work effectively based on large data sets; the nonavailability of data makes it difficult to create effective tools for accurate diagnosis. However, keeping sensitive personal data secure is also a major challenge for organizations using the same to train AI algorithms. Moreover, personal data protection laws impose strict liability on organizations dealing with such confidential data.

2. Uncertainties About the AI Algorithm

AI has, undoubtedly, brought a significant change in the healthcare sector by detecting diseases at the earliest stage. However, there is still apprehension about the accuracy of AI technology. This has led to skepticism related to the deployment of AI in standard care processes. 

3. Training Resources for AI Implementation

Another major challenge related to the use of AI in healthcare is the lack of technical training for stakeholders. Healthcare professionals must know how this AI technology is used in specific care units. Nevertheless, AI technology can be complex to understand. Thus, it is difficult to provide customized training to various resources.

4. Lack of Implementation Framework

While there is an abundance of research on how AI can transform the healthcare sector, there is a dearth of studies on how to implement AI in healthcare. This has created a knowledge gap for healthcare leaders, practitioners, and patients; a large number of people are unaware of how AI can fit into existing healthcare practices and processes. To tackle this, there needs to be a proper framework to enable healthcare leaders to effectively integrate AI in healthcare.

5. Ethical Challenges

The use of AI in healthcare also encompasses ethical challenges. Accountability plays a very crucial role in the medical sector. As AI technology trains itself based on the input data provided, we can hardly understand how the algorithm provides results. In case of an incorrect diagnosis leading to an unfortunate circumstance, it is difficult to ascertain accountability. Moreover, the absence of a standard ethical framework or guide regarding the use of AI in healthcare makes it difficult to define accountability.

ALSO READ: Top Challenges of AI in Healthcare: What Businesses Need to Resolve

Build a Career in AI and ML With Emeritus

AI is rapidly transforming several industries, including healthcare, manufacturing, finance, and agriculture. However, with the massive power it holds, it has become imperative for organizations to hire AI and ML leaders who can safely and efficiently integrate the use of AI into various processes. Consequently, the demand for AI and ML skilled professionals has increased multifold. According to a McKinsey survey, companies have already started to integrate AI in at least one of their functions. Hence, learning AI and ML has become crucial for all working professionals, irrespective of their domain. Enroll in any of Emeritus’ online artificial intelligence courses and machine learning courses and ensure your career is headed in the right direction. 

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