Machine Learning In Medical Field
From the past 2 years the world has been in an era of pandemic because of Covid-19. Although now conditions have become better, still new variants of Covid are rising. Doctors are working 24x7 to tackle this problem. Besides Covid, there are other diseases for which doctors are needed, but can there be a solution, which can make the task of doctors easy ? Yes, there is Machine learning. But what is ML ? and how can it be used in the medical field ?
Simply put, machine learning is a type of artificial intelligence in which computers are programmed to learn information without human intervention. In machine learning, the development of the underlying algorithms is based on computational statistics. Data is provided to the computer, and the computer “learns” from this data. The data actually “teaches” the computer by revealing its complex patterns and the underlying algorithms. The larger the “machine” data sample, the more accurate the output of the machine.
Background Research
According to a survey conducted by Deloitte of 1,100 US companies that were using Artificial Intelligence, 63% were focusing on Machine Learning. With ML, you can enhance the organizational aspects of your industry. Regular US nurses spend 25% of their working hours on regulatory and administrative work. This technology makes it easy to take over these routine tasks such as billing, revenue cycle management, clinical documents and records management.
In another Harvard Business Review study, more than 300 medical and clinical executives said they had problems with patient involvement. More than 70% of the surveyed individuals said that less than half of the patients were deeply involved in the treatment process, and 42% of the surveyed subjects said that less than a quarter of the patients were highly involved. Increasing patient involvement will undoubtedly lead to better patient health outcomes. Machine learning can provide automated messaging alerts and relevant targeted content that triggers actions at critical moments. In general, there are many ways ML can personalize and improve the treatment process.
One of the best uses of machine learning for medical treatment is a bot system that greatly simplifies treatment time. The virtual patient nurse acts as a voice-controlled health assistant that provides information about many illnesses, health problems, and medications. AI assistants are very useful when patients need real-time advice and it is difficult to see a doctor. Data engineers work on solutions for all medical activities that cover not only general health monitoring, but also treatment and prevention of illness.
Machine learning in oncology health care technology searches for cancer cells with accuracy comparable to that of experienced physicians. The work of pathologists examining body fluids (blood, urine, tissues, etc.) from patients is supported by the ability of machine learning to analyse more appropriately and quickly. Human vision with a microscope cannot be analysed even at half the speed of an automated model. For this reason, hospitals and research centers can benefit from implementing CNN-based applications for patient diagnostics.
Applications of ml in medical field
Applications of Machine Learning in Medical field are endless out of which, few we have listed above. All of these applications are discussed in detail below.
Diagnosis and disease identification
Google is working with distribution networks to build predictive models from big data to warn doctors about high-risk conditions such as sepsis and heart failure. Google, Enlitic, and various other start-ups are developing AI-derived image interpretation algorithms. Jvion provides a “clinical success machine” that identifies the most vulnerable patients and those who are most likely to respond to treatment protocols. There are many types of cancer and hereditary diseases that are difficult to identify. However, ML was able to handle many of them in the early stages. IBM Watson Genomics is a good example of this. This project combines cognitive computing with genome-based tumour sequences to help with rapid diagnosis. PReDicT (Prediction of Responses to Depression Treatment) by P1vital seeks to create a practical way to bring AI to improve diagnosis and treatment in regular hospitals.
Making diagnoses via image analysis.
Microsoft is revolutionizing data analytics in healthcare with the InnerEye project. This start-up uses computer vision to process medical images and make diagnostics. As technology advances, InnerEye is making waves in healthcare analytics software. Machine learning will soon become more efficient, allowing you to analyse more data points and create automated diagnostics.
Drug Discovery and Manufacturing
One of the most important clinical applications of machine learning is early-stage drug discovery. This includes R & D technologies such as next-generation sequencing and precision medicine to help find alternatives to treat multifactorial diseases. Currently, machine learning techniques include unsupervised learning, which can recognize patterns in data without making predictions. The Hanover project, developed by Microsoft, uses ML-based technology for several initiatives, including the development of AI-based technology for the treatment of cancer and the personalization of drug combinations for AML (acute myeloid leukaemia)
Personalized Medicine
Machine learning in medicine has made great strides. IBM Watson Oncology is a leader in this field by providing a variety of treatment plans that begin with analysing the patient’s medical history. Things get even better when it comes to creating personalized treatment plans as advanced biosensors emerge in the mass market (which provide a lot of data for algorithms).
Leveraging crowdsourced medical data
Researchers today have access to the vast amount of data that patients themselves publish. This is the source of future improvements in machine learning in medicine. Why is data analysis so important in medicine? A partnership between Medtronic and IBM has made insulin information decrypted, collected and available in real time. With the advancement of the Internet of Things (IoT), there are even more options. In addition, public data improves the diagnostic process and drug prescribing issuance.
Medical research and clinical trial improvement
It is a well-known fact that clinical trials can take years to complete and require significant investment. ML can provide predictive analytics to identify the best clinical trial candidates based on factors such as medical history and social media activity. This technology may also reduce the number of database errors and suggest the optimal sample size for testing.
Health records improvement
Despite all of these technological advances, maintaining a health record is still a chore. Yes, it’s much faster today, but it still takes a lot of time. Data sets can be categorized by vector machines and ML-based OCR recognition technology. Typical examples of this are Google’s Cloud Vision API and MathWorks’ ML handwriting recognition technology.
Epidemic control
Speaking of data analytics, by 2020, professionals will have access to information from satellites, social media trends, news websites and video streams. Neural networks can handle all of this and draw conclusions about epidemics that have occurred around the world. Dangerous illnesses can get caught in buds before they actually do a lot of damage. This is very important as the countries of the Third World lack advanced medical systems. Perhaps the best example in this area is ProMEDmail, an internet-based reporting platform that monitors outbreak reports around the world. Artificial intelligence is also widely used for food safety and helps prevent epidemics on farms.
Challenges for ml in medical field
Data governance
Medical data is still personal and inaccessible. However, according to a survey by the Wellcome Foundation in the United Kingdom, only 17% of the average respondent opposes sharing medical information with third parties.
Transparent algorithms
The need for transparent algorithms is not only necessary to meet strict drug development regulations, but more generally, it is necessary to understand how algorithms generate inferences accurately.
Optimizing electronic records
Between different databases, there is still a lot of fragmented information that needs to be further structured. As this situation improves, it will lead to advances in personal treatment solutions.
Data science experts
Attracting more machine learning and data science professionals is critical to both the healthcare and pharmaceutical industries.
Future of ML in Medical Field
Today, many large organizations and start-ups, including Enlitic, MedAware, and Google, are working on large-scale projects focused on improving AI and ML and deploying them in healthcare systems such as Google`s DeepMind Health project and Avicenna IBM software. In addition, IBM’s Watson Health is working with Cleveland Clinic and Atrius Health to use cognitive computing in healthcare systems. Experts hope this will help doctors reduce burnout. More recently, ML algorithms including nearest neighbour methods, naive and semi-naive Bayes, look-ahead feature building, neural backpropagation networks, etc. are currently being tested and developed. Artificial intelligence and machine learning are undoubtedly the future, as sophisticated automation of data collection and the replacement of work in all industries with machine learning systems are inevitable. Scientists and researchers need to focus on developing effective, efficient and innovative algorithms while ensuring that their functions and models do not endanger the human labor market. Both Elon Musk and Stephen Hawking consider AI and ML to be both economically and physically dangerous. Nonetheless, it is imperative that we continue to strive to transform the quality of our overall care and health system through machine learning, science and technology that will revolutionize the world in all areas of our lives in the coming decades. The benefits of machine learning outweigh these theoretical nightmares.
Conclusion
Machine learning has optimized tasks like personal assistance, record keeping, etc. Also it has helped in research for new viruses and vaccines. Although there are a lot of advantages of ML in the medical field, there are some challenges which need to be addressed. AI& ML is the future in medical field and it has proven its worth during this pandemic era.
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