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.
Where is Machine Learning in Healthcare Today?
Several organizations work on developing and applying AI and ML in the healthcare industry. They employ big data analytics, medical experts, and hire AI developers.
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.
How is Machine Learning Helping the Healthcare and Pharma Industry in Today’s World?
According to McKinsey, machine learning and big data in medicine and pharma should generate a value of up to $100 billion annually, based on optimized innovation, better decision-making, new tool creation for physicians, and improved efficiency of clinical/research trials.
Here are a few applications of machine learning in the pharma and healthcare industry.
Personalized Treatment and Behavioral Modification
The penetration rate of EHR in healthcare has risen from 40% to 67% between 2012 and 2017. It means it gives access to more individual patient health data. With personal medical data compilation using ML algorithms and applications, healthcare providers will be able to assess and detect health issues better.
Depending on supervised learning, medical professionals can easily predict threats and risks to an individual’s health as per the generic information and symptoms on their history.
This is what health tech company is using. Using patient medical history and information is helping healthcare professionals to come up with a better treatment plan based on an optimized selection of treatment options.
ML can improve the Research and Development process. From identifying to designing new molecules to target-based drug discoveries and validation, artificial intelligence can do it all.
As per MIT study, just 13.8% of drugs emerge successful in their clinical trials. To top that, a pharmaceutical company will have to pay about US$ 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 are using advanced Machine Learning systems for collecting, processing, and analyzing a large volume of healthcare data. Healthcare providers across the world are using Machine Learning technology for storing sensitive data safely and securely, in the cloud. This is electronic health records or EHR.
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.
Disease and Epidemic Prevention
Pharma companies are using artificial intelligence and machine learning for known diseases, such as Parkinson’s and Alzheimer’s. Usually, pharma companies don’t spend time and resources to find treatments for rare diseases as the ROI is low in comparison to the cost and time it takes to create drugs for rare treatments.
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.
Has Machine Learning Adoption Matured in the Healthcare Industry? What Will Help it Get Adopted Faster?
Artificial Intelligence and Machine Learning in healthcare are at the end of the beginning. It has been introduced, researched, proven to work, and is being used in a clinical setting.
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.
To ensure that Machine Learning gets adopted faster, here are a few things the healthcare industry should do.
- 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.
Where do we see Machine Learning heading toward in the future?
Machine learning has already proven to be useful in addressing some of the problems for hospitals as well as pharma companies during the current global pandemic. According to the HealthITAnalytics report, a deep-learning tool might help in predicting COVID-19 surges in the United States counties with almost 65% accuracy.
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.
How does ResolveData Help Healthcare Industry with ML?
ResolveData is a platform that has been designed to help you address the challenges by combining ML insights with robust automated actions built-in complexity of modern-day environments. With insights from ResolveData, you can achieve the agility needed for the healthcare industry to be at its best. Its machine learning algorithm and cross-domain automation work together across the cloud and virtualized infrastructure to predict future problems and automate proactive fixes before they can affect a healthcare organization or hospital.