Technology and innovations have always influenced medicine development. This is true for machine learning and artificial intelligence as well. Machine learning in medicine and healthcare is now used to better process large amounts of data. This is becoming more affordable and cost-effective since the development of computing power occurred.

Machine Learning application is widely appreciated since evidence-based medicine is of top standard. Nevertheless, it will give you access to technology-enabled healthcare improving treatments, creation of new tools, and efficacy of development or research. Machine learning applications in the healthcare industry combine the processing power of many human minds and reinvent fields, like medicine and diagnosis. It is changing the way we live.

Today machine learning in healthcare is being more widely used and is helping clinicians and patients in various ways. The most common use of machine learning is in clinical decision support, automating medical billing, and the development of clinical care guidelines. Notable concepts of ML and healthcare are being applied in medicine.

At MD Anderson, researchers have come up with the first medical machine learning algorithm for predicting serious toxicities in patients who are receiving radiation therapy for neck and head cancers. In the field of radiology, deep learning can identify complex patterns automatically and help radiologists make decisions reviewing the images, such as MRI, CT, and PET radiology reports. Unstructured healthcare for ML represents 80% of the details locked in the electronic health record system. These are documents that couldn’t be analyzed without humans reading through the material. Human language is complex and lacks uniformity. It contains vagueness and jargon. To convert the documents to analyzable and useful data, ML in healthcare often depends on Natural Language Processing programs.

National Language Processing programs can be used to decide the creditworthiness of a consumer. ForeSee Medical uses ML medical data to analyze the speech patterns of the physician end-users and understand the context of complicated medical terms.

Depending on supervised learning, medical professionals can easily predict threats and risks to an individual’s health as per the generic information and symptoms on their history.

This is what health tech company is using. Using patient medical history and information is helping healthcare professionals to come up with a better treatment plan based on an optimized selection of treatment options.

As per MIT study, just 13.8% of drugs emerge successful in their clinical trials. To top that, a pharmaceutical company will have to pay about US$ 161 million to US$2 billion for a drug to complete the process of clinical trial and get FDA approval.

Thus, pharma companies are adopting AI and ML to improve the success rates of their drugs and introduce more affordable drugs.

Doctors use these records when they have to understand the effect of a certain genetic trait on the health of a patient and find out how certain drugs treat a condition. ML uses data stored in EMRs for making real-time predictions for diagnosis and suggesting the right treatments.

As machine learning can process and analyze large amounts of data easily, it can quicken the process of diagnosis.

As per Global Genes, about 95% of rare diseases do not yet have FDA-approved treatments or cures. But thanks to ML’s innovative abilities, the scenario is changing rapidly for the better.

AI and ML are also being used by various pharmaceutical companies to forecast epidemic outbreaks. A good example is the malaria outbreak prediction model functioning as a warning tool.​

However, adoption is quite slow. Healthcare has been successful in exploring the tip of the iceberg. There is significant work to be done to improve patient care and machine learning in healthcare has started emerging out of its infancy.

Hospitals and healthcare organizations have moved beyond AI-based program pilots into adopting and developing systems that work best for them. There is considerable interest in predictive clinical analytics or processes to input historical patient data to build models for identifying and forecasting future events.

During the next 10 years, Health technology expects to see more developments in the areas of ML, decision support capabilities, and reasoning.

• Introduced specialized machine learning roles and using machine learning models.
• Use specific machine learning success metrics.
• Approach machine learning models in a different manner.
• Build a strong model-building checklist.

With the work in progress in AI and ML in healthcare, experts are aiming towards reduced costs, better outcomes, and improved accessibility. For better outcomes, you will see AI-powered tools empowering clinicians. There will also be tools that make it possible for clinicians to stay abreast of the rising amount of new medical science.

AI and ML will offer new ways for people to access and control their own healthcare. You can already see this happening with Healthbot by Microsoft.