Machine Learning plays  a vital  role in healthcare using artificial intelligence. Rapid evolution of healthcare technology and treatments can make it challenging for healthcare professionals to stay informed. Amidst the buzz, ML in healthcare has emerged as a game-changer. But what does it entail?

Machine learning in healthcare refers to the application of artificial intelligence to analyze and interpret complex patient data, uncovering patterns and insights that inform better decision-making.

Uses of Machine Learning in the Healthcare Industry 

Machine learning has enormous benefits to healthcare. Below are some of its uses and  benefits organizations are realizing by applying ML in healthcare:

Predictive Analytics to Forecast and Prevent Disease

Machine learning technology empowers healthcare professionals to develop personalized medicine solutions manufactured for patients’ unique characteristics. Also, machine learning models can predict patient reactions to treatments and as such enabling healthcare providers to anticipate and mitigate potential adverse reactions, optimize treatment plans for enhanced efficacy, improve patient outcomes through proactive care and enhance the overall quality of healthcare services.

Faster Data Collection

The healthcare industry is leveraging wearable technology and machine learning to accelerate data collection and analysis. Wearables provide real-time data, which machine learning algorithms can rapidly process and learn from as such enabling healthcare professionals to make timely and informed decisions.

Cost-Efficient Processes

Machine learning algorithms can quickly scan and analyze EHRs for specific patient data, schedule appointments with patients and automate a range of procedures. Health care workers  can be trained  to focus their attention on patients that need  more urgent medical attention

Drug Discovery and Treatment Innovation

Machine learning models can also be used by the healthcare sector and pharmaceutical companies to identify relevant information in data that could lead to discovery and manufacturing of new drugs by pharmaceutical companies to cure diseases. By applying ML to clinical trials data and medical research, these entities can uncover previously unknown drug side effects; enhance patient care and outcomes, optimize drug development processes; and improve the safety and efficacy of medical procedures

With the use of ML, healthcare providers can improve patient outcomes, enhance the quality of care, and make healthcare more accessible and efficient.

Examples of  Machine Learning in Healthcare

Strive Health

This hospital was founded in 2018. Strive Health is a healthtech company that leverages ML to enhance kidney disease care. Its CareMultiplier platform utilizes machine learning algorithms to analyze data and provide actionable insights, develop personalized treatment and management plans for kidney conditions and improve patient outcomes and quality of care. By harnessing the power of  ML, Strive Health aims to revolutionize kidney disease management and deliver more effective, data-driven care.

Grail

This innovative healthcare company is dedicated to developing smart solutions for early cancer detection, combining expertise in genomics and technology. Their groundbreaking Galleri test analyzes DNA fragments in blood samples, utilizing machine learning algorithms to identify the source of the DNA fragments to determine whether the cells are cancerous.

Microsoft

Microsoft’s Project InnerEye leverages machine learning and computer vision to enhance healthcare outcomes. By analyzing 3D radiological images, the platform uses AI to accurately distinguish between tumors and healthy anatomy, assisting medical experts in precise radiotherapy and surgical planning. Microsoft’s innovative approach harnesses the power of machine learning to deliver personalized medicine, tailoring treatments to individual patients’ unique needs and revolutionizing healthcare outcomes.

PathAI

PathAI utilizes machine learning to enhance diagnostic accuracy and speed for pathologists. The company’s AI solutions also streamline various tasks, including: compiling patient information, processing samples, facilitating clinical trials and drug development. Through its extensive network of partnerships with biopharma companies, laboratories, and clinicians, PathAI accesses a wealth of resources, enabling the development of more effective treatments and improved patient outcomes.

Ciox Health

The healthcare company was founded in 1976 with the office situated in Alpharetta, Georgia. Ciox Health’s platform uses machine learning to give healthcare professionals quick access to patient data. This helps them make informed decisions. The platform also allows organizations to customize data requests, keeps patient data private and secure and follows regulations to protect electronic health records.

Oncora Medical

This is a startup in Philadelphia. They make cancer research and treatment easier. They collect data from many sources to check the quality of care and find better treatment options. They develop machine learning models carefully, like a pharmaceutical company develops new medicines.

Deep Genomics

This healthcare uses artificial intelligence to speed up medical research. Their platform helps healthcare professionals find potential drugs for specific diseases. They use machine learning to find the causes of genetic diseases, understand how genetic changes lead to disease and quickly analyze millions of potential medicines and identify the most promising ones. This helps researchers develop new treatments faster.

Artificial Intelligence vs Machine Learning in Health Care

Artificial intelligence (AI) and machine learning (ML) are both transforming healthcare, but they have different strengths. AI is a broader technology that can perform tasks like language translation and user interface design, while ML is a type of AI that specializes in analyzing data and learning from it. In healthcare, AI can help with tasks like medical imaging and drug discovery, while ML can detect anomalies in medical data and improve patient outcomes over time.

Here are some key differences between AI and ML in healthcare:

•  AI can analyze medical images and diagnose diseases
•  AI can help with drug discovery and development
•  ML can detect anomalies in medical data, like potential breaches of patient data
•  ML can improve detection and prediction over time, making it useful for personalized medicine

Machine Learning: How Can We Help You

North South Tech assists healthcare companies in developing customized ML software and models by collecting and integrating diverse data, performing data preprocessing, and selecting suitable algorithms. We design and train models tailored to healthcare needs, optimize them, and ensure seamless deployment and integration into existing IT systems.

Our solutions comply with healthcare regulations, and we provide continuous performance monitoring and updates. By creating user-friendly applications and scalable solutions, we enable healthcare professionals to leverage ML for improved patient outcomes, operational efficiency, and innovation.