Applications Of Machine Learning In Healthcare
Applications Of Machine Learning In Healthcare
Healthcare is a significant industry that provides value-based care to millions of people while also ranking among the top industries in terms of revenue generation for many nations. The US healthcare sector alone generates $1.668 trillion in revenue today. In comparison to the majority of other developed or developing countries, the US also spends more on healthcare per person. The phrases “quality,” “value,” and “outcome” are frequently used in the healthcare industry and offer great promise. Today, healthcare professionals and stakeholders all around the world are searching for novel approaches to fulfill this promise. Technology-enabled smart healthcare is no longer just a pipe dream since Internet-connected medical gadgets are keeping the current healthcare system from collapsing under the weight of the growing population.
Technology & Healthcare industry
Technology is now enabling healthcare professionals to build alternative staffing models, IP capitalization, deliver smart healthcare, and reduce administrative and supply expenses, in addition to playing a crucial role in patient care, billing, and medical records. One such subject that is gradually gaining traction in the healthcare sector is machine learning. To detect dangerous tumors in mammograms, Google recently built a machine-learning system. Stanford University researchers are now applying deep learning to detect skin cancer. Numerous more applications of ML in healthcare include providing exact resource allocation, timely risk assessments, and analysis of hundreds of distinct data points to suggest outcomes.
Top 10 Pharma and Medical Uses of Machine Learning
The growing number of machine learning applications in healthcare gives us a glimpse of a time in the future when data, analysis, and innovation will work together to benefit countless patients without their knowledge. The use of real-time patient data from various healthcare systems across numerous nations will soon be rather prevalent in ML-based applications, enhancing the effectiveness of previously inaccessible novel treatment alternatives.
The top 10 uses of machine learning in healthcare are as follows:
1. Recognizing Illnesses and Making Diagnoses
Identification and diagnosis of illnesses that are typically thought of as difficult to diagnose are one of the main uses of ML in healthcare. This can range from various hereditary illnesses to tumors that are difficult to detect in their early stages. A good illustration of how combining cognitive computing with genome-based tumor sequencing might aid in reaching a quick diagnosis is IBM Watson Genomics. Berg, the industry behemoth in biopharma, is using AI to create therapeutic medicines in fields like oncology. The PReDicT (Predicting Response to Depression Treatment) project by P1vital intends to create a method for diagnosing and treating common clinical illnesses that is also commercially viable.
2. Drug Discovery and Manufacturing
Early-stage drug development is one of the main clinical uses of machine learning. Additionally, this includes research and development (R&D) technologies like next-generation sequencing and precision medicine, which can aid in identifying alternative treatment modalities for complex disorders. Currently, unsupervised learning is used in machine learning approaches to find patterns in data without making predictions. Microsoft’s Project Hanover is utilizing ML-based technologies for several efforts, such as creating AI-based cancer treatment technology and tailoring medicine combinations for AML (Acute Myeloid Leukemia).
3. Diagnostic Medical Imaging
Both machine learning and deep learning enable the revolutionary field of computer vision. This has been acknowledged by the Microsoft InnerEye project, which creates picture diagnostic tools for image analysis. Expect to see more medical imaging data sources integrated into an AI-driven diagnostic process as machine learning becomes more widely available and as its capacity for explanation increases.
4. Individualized Healthcare
By combining individual health with predictive analytics, personalized treatments can not only be more successful but are also ripe for further study and improved disease assessment. Now, doctors are only able to select from a restricted number of diagnoses or gauge the patient’s risk based on his symptom history and the genetic data that is currently accessible. However, machine learning in medicine is advancing rapidly, and IBM Watson Oncology is leading this trend by using patient medical histories to provide a variety of treatment options. Additional gadgets and biosensors with advanced health measuring capabilities will enter the market in the upcoming years, making more data easily accessible for such cutting-edge ML-based healthcare systems.
5. Behavioral modification based on machine learning
Since the widespread use of machine learning in healthcare, a plethora of companies have sprung up in the areas of patient treatment, cancer prevention and detection, and behavioral modification, among other areas. A B2B2C data analytics business called Somatix has developed an app that uses machine learning to identify motions we make regularly so that we can better understand our unconscious behavior and make the required adjustments.
6. Smart Health Records – using machine learning in healthcare
Maintaining current health records is a laborious procedure, and while technology has contributed to making data entry easier, the bulk of activities still take a long time to complete. Machine learning’s primary function in the healthcare industry is to streamline procedures to save time, effort, and money. Techniques for document classification based on vector machines and ML-based OCR recognition, like Google’s Cloud Vision API and MATLAB’s machine learning-based handwriting recognition tool, are progressively gaining traction. The next generation of intelligent, smart health records is now being developed at the forefront by MIT. These records will be built from the ground up using ML-based tools to assist with diagnosis, clinical therapy recommendations, etc.
7. Clinical research and trials
There are several potential uses for machine learning in the world of research and clinical trials. Clinical studies can take years to complete, cost a lot of money and effort, and are quite labor-intensive, as anyone in the pharmaceutical sector will attest. Researchers can create a pool of possible clinical trial participants by using ML-based predictive analytics to discover individuals from a wide range of data sources, including prior doctor visits, social media, etc. Machine learning has also been used to determine the appropriate sample size to test, ensure real-time monitoring and data access for trial participants, and harness the power of electronic records to minimize data-based errors.
8. Crowdsourced Data Gathering
Nowadays, crowdsourcing is huge in the medical industry since it gives researchers and practitioners access to tonnes of data that people contribute with their permission. The future perception of medicine is greatly affected by this real-time health data. Users can use interactive apps that use ML-based facial recognition to attempt and treat Asperger’s and Parkinson’s disease through Apple’s ResearchKit platform. In a recent collaboration with Medtronic, IBM analyzed, gathered, and made accessible diabetes and insulin data in real time using crowdsourced data. With the IoT’s technological developments,
9. Better Radiotherapy
Radiology is one of the fields in healthcare where machine learning is most in demand. Numerous discrete variables that may appear at any given time are present in medical picture analysis. Complex equations cannot be used to accurately simulate all lesions, cancer foci, etc. It is simpler to diagnose and identify the factors since ML-based algorithms learn from the wide variety of varied samples that are currently available. The classification of objects, such as lesions, into categories like normal or abnormal, lesion or non-lesion, etc. is one of the most often used applications of machine learning in medical image analysis. DeepMind Health, a division of Google, actively assists UCLH academics in the creation of algorithms. which can distinguish between malignant and healthy tissue and enhance radiation therapy for the latter.
10. Outbreak Prognosis
Today, monitoring and forecasting epidemics on a global scale are also being done using AI-based technologies and machine learning. Today, scientists have access to a vast amount of information gathered by satellites, in real-time on social media, on websites, etc. To compile this data and forecast everything from malaria outbreaks to serious chronic infectious diseases, artificial neural networks are used. Because these nations lack essential medical infrastructure and educational institutions, forecasting these outbreaks is especially helpful in developing nations. The ProMED-mail, an Internet-based reporting network that tracks developing and emerging diseases and sends epidemic reports in real time, serves as a prime example of this.