Machine Learning in Healthcare

INTRODUCTION:

Machine Learning (ML) is a field of study in artificial intelligence (AI) and computer science that develops machines, trained to automatically learn from their previous data and experiences without explicit instructions. These machines are designed to behave like human brains, making predictions and improving their decision-making ability without human intervention, allowing them to predict more accurate outcomes by analyzing the data.

Machine Learning is a subfield of AI that has made vast advancements in the field of science and technology, particularly in healthcare and medicine. It is one of the biggest sectors in the world that can benefit from machine learning technology.

Machine learning aims to enable physicians to improve their speed and reduce human errors. With the help of AI and machine learning, it is possible to perform even the smallest components of any operation with near perfection. Healthcare has greatly benefited from ML technology and has the potential to transform various aspects of the industry with implementation in the future.

USE OF MACHINE LEARNING IN HEALTHCARE:

Machine learning (ML) has emerged as a transformative force in the healthcare industry, providing innovative ways to improve patient care and the diagnosis of diseases. Examples include:

Prediction and Identification:

Machine learning models can assist in the detection of diseases like cancer at early stages through images produced in X-rays, Magnetic resonance imaging (MRIs), and CT scans. They can evaluate past medical records and diagnostic images of a particular patient to predict the risk of diseases and offer them proper preventive measures.

Electronic Health Records (EHR) Management:

An EHR is a digital version of stored health information for patients including their problems, medications, and test results. Machine learning algorithms can assist in analyzing large volumes of this electronic data to identify patterns, trends, and insights for healthcare professionals and offer patients proper treatment.

Personalized Treatment:

Machine learning models can help provide customized treatment by generating precise medical solutions for particular individuals. They can analyze a patient’s genetic information and historical medical records and suggest personalized diagnosis plans, making patient-tailored decisions. It is used to identify previously undetectable symptoms of an individual and diagnose those diseases at the earliest. ML algorithms also assist in drug discovery by identifying potential drug candidates and predicting successful drug molecules more accurately.

Chatbots:

ML-powered chatbots enhance patient engagement by handling simple inquiries with ease and offering initial treatment to them. By doing this, healthcare professionals can concentrate on patients who require greater attention, thereby saving time. Due to its 24/7 accessibility, patients can seek medical assistance anytime there is a medical crisis. Individuals prefer chatbots more because they are considered as ‘non- judgemental’, allowing individuals to seek professional help regarding issues like mental health, sexual problems, etc., more comfortably compared to a human assistant.

EXAMPLES:

Below are examples of some companies in the world that use machine learning in healthcare:

Microsoft:

Microsoft launched Project ‘InnerEye’ in September 2020, which uses 3D radiological images to distinguish between tumors and healthy anatomy, assisting medical experts in radiotherapy and surgical planning. With this AI-based approach, Microsoft aims to create medication that is specific to the needs of each individual.

Tebra:

Tebra’s Kareo product delivers a cloud-based clinical and business management platform to support the tech and business needs of independent practices. With the help of Kareo, organizations can transfer the health and financial data of patients over to Kareo’s billing platform for optimized billing operations and easy transactions. AI technology is implemented in Kareo to automate repetitive tasks, reducing time and labor.

Beta Bionics:

To reduce stress in the lives of diabetes patients, Beta Bionics has released a wearable “bionic” pancreas called iLet in 2023 for people aged six years and older. This device constantly monitors blood sugar levels in patients with Type 1 diabetes, so that patients don’t have to track their blood glucose levels every day.

Pfizer:

With the aid of Watson AI Technology by IBM, Pfizer utilizes machine learning and natural language processing for immuno-oncology research on how the immune the system can combat cancer. This partnership enables Pfizer to generate insights more quickly regarding how to produce even more impactful diagnoses by analyzing a large amount of patient data.

Orderly Health:

Orderly Health serves as a patient navigation platform that helps organizations with a B2C concierge chatbot that interacts via text, email, Slack, and video conferencing to answer everyday healthcare queries. The company aims to save time and money for organizations on medical and healthcare expenses by making it easier to understand benefits and identify the least expensive providers.

CHALLENGES OF ML IN HEALTHCARE:

The following are some challenges faced with the implementation of machine learning in the healthcare industry:

• Handling large volumes of patient data and extracting meaningful information from these datasets pose computational
• After applying any machine learning tool, the results that we receive depend on the information that we feed to the model. So, incomplete or inaccurate data can lead to biased or faulty
• ML models are not safe from cyber threats and attacks if not properly
• ML models require continuous learning, updating, and improvement in performance over time as they are exposed to new data.
• Not all people trust these algorithms, so the implementation of ML in healthcare is a challenge in It is difficult to guarantee patient engagement and their acceptance of the implementation of AI in medicine.

CONCLUSION:

Machine learning in the healthcare industry plays a significant and transformative role in our society by improving disease diagnosis, and operational efficiency, and making remarkable advancements in medical research. ML can analyze and process large amounts of data, identify medical complications from genetic information, and offer customized treatment plans, among other capabilities. Although there are some important issues regarding data privacy and security, as we progress with the development of technology, the collaboration of engineers and healthcare professionals becomes crucial for unlocking the full potential of ML. As long as the risks and challenges associated with this field are recognized and corrected, existing machine-learning algorithms can serve as a solid foundation for potential developments in the healthcare industry in the future.

REFERENCES:

• https://builtin.com/artificial-intelligence/machine-learning-healthcare