The healthcare industry is one of the most labor-intensive industries around the world. It requires the presence of humans to take care of people at various stages of their illnesses. There was a time when acute problem of staff scarcity was plaguing the healthcare industry in the United Kingdom but was aptly solved by creating artificial tabletop bots that would take care of elderly patients needs.The artificial tabletop bots remind elderly patients to take their pills, track their prescriptions, and track and suggest wake-up routines.
At the heart of Echo Alexa (as it is known) is the machine learning developed by the Amazon team using its cloud infrastructure Amazon Web Services (AWS). At the heart of Alexa is the Python machine learning code that helps it to perform tasks and learn from them through a feedback mechanism. The wonderful part of this service is that Echo Alexa is available to a common Python developer to use and develop their own programs and products based on Amazon’s infrastructure.
If you see the IBM Watson for healthcare services, you'll see that developers built their own applications on top of this base analytics engine. IBM has proven to use its analytics engine in applications such as testing genetic results, drug discovery, oncology, and care management, to name just a few.
One more area where not just IBM but other analytics engines are making headway is in diagnosing disease using imaging. In healthcare imaging, such as X-ray images or CAT scan images, all have traditionally been interpreted by humans. However, there are some reasons why we need machines to do this work more efficiently:
1. High volume of imaging data with increased patients.
2. Stress on doctors due to high volumes makes them more error-prone. Machines can handle large sets of imaging data with a lower error rate.
3. Inability of healthcare professionals to link and see the big picture from imaging data. Machines can help them by assessing large numbers of image datasets and determine whether there are any patterns or any connections among groups of patients or groups of localities, for example.
4. Replace doctors or specialist at times of their absence. This is a key operation that a machine can do—when a specialist is not available, it can replace the human specialist and provide diagnosis in even critical cases. In my opinion this function of a machine will be used more and more, and the day is not far when the entire area of image diagnosis will be done by machines with no human intervention.
5. Drug discovery is a very key area for the healthcare industry. Research in the pharmaceutical companies for diseases like cancer or HIV is continuously happening. Machine learning is helping speed up drug discovery by analyzing medicinal data and providing prediction models on drug reactions even before they are injected into subjects in a controlled environment. This saves both time and money, as the simulation of drug reactions gives an estimate on likely cure patterns and reactions to the drug.
6. Patient Research in difficult fields like Cancer, etc. There is a lot of data available in this field for both patient and clinical trials of medicines. Clinical trials are time-consuming and require collection of subject data on reactions in the body. This is either collected invasively, such as via a blood test, or non-invasively, such as through urine tests or putting probes on certain body parts of the subject.
Machine Learning Is Transforming Healthcare and there are reports that in China a Robot named
“Xiao Yi” has passed China’s National Medical Licensing Examination successfully and has achieved all the skills to practice medicine. Due to such reasons healthcare professionals fear that AI
will replace them. The machines may make their jobs redundant. Some say it is a clear sign that robots are going to rule the humans. However the truth is humans can't be completely replaced with machines but some of the trends that we are likely to see in the healthcare world as far as machines are concerned are:
1. Robots replace workers in low-paying jobs first, where humans do not want to do the mundane work, such as the case of Amazon’s Echo Alexa replacing elderly healthcare due to staff shortage.
2. Robots become assistants to senior specialists, like neurosurgeons, and learn the skills for diagnosis and surgery.
3. Robots will replace senior specialists in diagnosis, as it requires more analysis and processing. Humans can't process large information and spot patterns in big data sets. This is where robots will score significantly higher in accuracy of diagnosis than a human specialist.
4. Surgery will be done by humans with assistance from robots. This has already been demonstrated by the University of Oxford Surgeons . So it is possible as more and more robots are built to do precision operations on humans and are successful, they will work jointly with human specialists to carry out complex, precision-based surgeries. This trend will start to emerge in the coming years. They may be termed as Auto-doctors and Guided-doctors. Auto-doctors would use unsupervised learning techniques to treat a patient for new discovery diseases. Guided-doctors would use supervised learning techniques. They would work for known diseases on known lines of treatments.
The healthcare industry in particular deals with human beings and their lives. This is one of those industries where a simple judgmental error could cause death to a patient. However, when we talk about building prediction models based on machine learning (ML), which is the brain behind any robot, we know that no matter what algorithm is selected for predicting the outcome from any data set, there is going to be a percentage of errors in the final prediction by the model. In the case of human beings, a human being or a human doctor or a healthcare professional is also prone to errors.
This is something that we know as human error.
So if we were to build and create a replacement or a competitor for a human doctor, we know that it would have to do better than this error rate. It can only survive if it gives predictive diagnosis at a lower error rate than that of the human doctor. Since we are dealing with human life in the healthcare industry, we need a gradual and careful way of adopting technology, as a lot is at stake. The requirement is to build robust algorithms with prediction models with higher accuracy levels.
In today’s age where there is a plethora of information and big data in healthcare strives to improve the current healthcare systems, machine learning can surely make a mark in its initiative to improve human health.
Here are five exciting applications of ML in healthcare:
1. ML IN HEALTHCARE FOR IMAGING AND DIAGNOSIS
With machine learning advancing at an astounding speed, machine learning is an active application in diagnosis of human diseases. Another important element of diagnosing an illness is medical imaging and its ability to show a more complete image of an illness. As machine learning operates on algorithms, healthcare specialists are aiming to leverage this technology in their field by actively developing algorithms and providing information to machines that can help them in imaging and analyze human bodies for abnormalities. By using smart machines machine on a human body, the machines can quickly scan through the body and can click images to detect diseases early on.
Deep learning is playing a key role in this regard as it is becoming more accessible thanks to richer data sources that can be used in the diagnostic process. The technology has some limits as it is incapable of explaining how it arrived at its predictions, although these ML applications are correct a lot of the time. Nevertheless, the technology, combined with healthcare professionals, can offer treatment solutions quicker with these advanced diagnosis tools by interpreting a result and deciding whether the machine’s treatment suggestions are correct or not.
One of the key components of a successful healthcare organization is its ability to identify a disease with speed and accuracy. With hundreds of drugs currently on clinical trial, scientists are entering the fray in high-need areas such as cancer identification and treatment. One such solution integrates cognitive computing with genomic tumor sequencing, while another uses ML to develop diagnostics and therapeutic treatments in multiple areas such as oncology. Another example is DeepMind Health, which is developing technology that can address macular degeneration in aging eyes.
2. ML IN HEALTHCARE FOR DATA COLLECTION AND FOLLOW-UPS
Personalization is what humans like when they go anywhere. As big data has several applications and gathers information from every possible source, leveraging the same to improve human life can be helpful for doctors to provide people with enhanced services. When ML can accommodate sufficient information about a user, doctors can personalize the treatment options. This personalization of services is possible with the help of machines providing insights about risks of a particular patient being susceptible to a specific disease. With accurate information and actionable insights, machines can also suggest users and doctors about remedies and precautionary measures with depending on a patient’s response to medications.
3. ML IN HEALTHCARE FOR RADIOLOGY AND RADIOTHERAPY
ML has proved its worth and capabilities to detect cancer in the past and is one of the most viable options for leading healthcare pioneers to identify any abnormalities. With such performance, ML is proving to be another strong option for radiology and radiotherapy. Doctors can use this technology to scan through the possibilities of a patient’s response to a specific input of radiations through their body. ML can also help doctors and surgeons in deciding what and how intense a radiation would be required depending on how well the patient responds to specific amounts of emissions.
4. ML IN HEALTHCARE FOR DRUG DISCOVERY AND EXPERIMENTS
Scientists strive to find ways of how they can discover newer ways to certain deadly diseases. With rigorous attempts at improving healthcare, they search for different drugs that can behave as advanced medicines and perform experiments that are focused solely on how these medications can help. Machine learning algorithms help scientists by providing them information about how to improve drug performance and behavior of the same on a test subject. The behavioral details that noted from a test subject and a dummy drug can be noted and ML algorithms can be used to determine how those medications perform on a human being.
ML has the capacity to discover new drugs that offers great economic value for pharmaceuticals, hospitals and new treatment avenues for patients. Some of the major tech players such as IBM and Google have created ML platforms designed to discover new routes of treatment for patients. Precision medicine is a key term in this topic as it consists of identifying mechanisms for multifactorial diseases and finding alternative paths for therapy. Institutions such as the MIT Clinical Machine Learning Group have been using precision medicine research to develop algorithms that can help doctors better understand disease processes and create effective treatments for diseases such as Type 2 diabetes.
5. ML IN HEALTHCARE FOR SURGERIES
We will always need human intervention for surgeries due to the high-risk nature of these procedures, but ML has been helping greatly in the robotic surgery space. One of the most popular developments in the field has been the da Vinci robot, which allows surgeons to manipulate robotic limbs in order to perform surgeries with great detail and in tight spaces. These hands are often steadier and more accurate than human hands. There are also tools that use computer vision aided by machine learning to identify the distances of specific body parts in order to adequately perform surgery on them. One example of this is the identification of hair follicles for hair transplantation surgery.
Current technological innovations continuously strive to improve the healthcare situation for patients and doctors. When machines focus on improving the performance of operations, they can help doctors by using surgical robots. These surgical robots prove to be of great help to doctors as they provide doctors with high definition imagery and extended flexibility to reach out in areas that are crucial for a doctor. Machine learning has several other applications in numerous fields that try to improve human life. As healthcare pioneers are working to improve the current scenario of their industry consistently, they can now search for ways in which their organization can leverage this technology and how they can benefit from the same.
In the next post we'll continue our discussion and discuss the Real-World Benefits of Machine Learning in Healthcare . So till we meet again keep learning!
At the heart of Echo Alexa (as it is known) is the machine learning developed by the Amazon team using its cloud infrastructure Amazon Web Services (AWS). At the heart of Alexa is the Python machine learning code that helps it to perform tasks and learn from them through a feedback mechanism. The wonderful part of this service is that Echo Alexa is available to a common Python developer to use and develop their own programs and products based on Amazon’s infrastructure.
If you see the IBM Watson for healthcare services, you'll see that developers built their own applications on top of this base analytics engine. IBM has proven to use its analytics engine in applications such as testing genetic results, drug discovery, oncology, and care management, to name just a few.
One more area where not just IBM but other analytics engines are making headway is in diagnosing disease using imaging. In healthcare imaging, such as X-ray images or CAT scan images, all have traditionally been interpreted by humans. However, there are some reasons why we need machines to do this work more efficiently:
1. High volume of imaging data with increased patients.
2. Stress on doctors due to high volumes makes them more error-prone. Machines can handle large sets of imaging data with a lower error rate.
3. Inability of healthcare professionals to link and see the big picture from imaging data. Machines can help them by assessing large numbers of image datasets and determine whether there are any patterns or any connections among groups of patients or groups of localities, for example.
4. Replace doctors or specialist at times of their absence. This is a key operation that a machine can do—when a specialist is not available, it can replace the human specialist and provide diagnosis in even critical cases. In my opinion this function of a machine will be used more and more, and the day is not far when the entire area of image diagnosis will be done by machines with no human intervention.
5. Drug discovery is a very key area for the healthcare industry. Research in the pharmaceutical companies for diseases like cancer or HIV is continuously happening. Machine learning is helping speed up drug discovery by analyzing medicinal data and providing prediction models on drug reactions even before they are injected into subjects in a controlled environment. This saves both time and money, as the simulation of drug reactions gives an estimate on likely cure patterns and reactions to the drug.
6. Patient Research in difficult fields like Cancer, etc. There is a lot of data available in this field for both patient and clinical trials of medicines. Clinical trials are time-consuming and require collection of subject data on reactions in the body. This is either collected invasively, such as via a blood test, or non-invasively, such as through urine tests or putting probes on certain body parts of the subject.
Machine Learning Is Transforming Healthcare and there are reports that in China a Robot named
“Xiao Yi” has passed China’s National Medical Licensing Examination successfully and has achieved all the skills to practice medicine. Due to such reasons healthcare professionals fear that AI
will replace them. The machines may make their jobs redundant. Some say it is a clear sign that robots are going to rule the humans. However the truth is humans can't be completely replaced with machines but some of the trends that we are likely to see in the healthcare world as far as machines are concerned are:
1. Robots replace workers in low-paying jobs first, where humans do not want to do the mundane work, such as the case of Amazon’s Echo Alexa replacing elderly healthcare due to staff shortage.
2. Robots become assistants to senior specialists, like neurosurgeons, and learn the skills for diagnosis and surgery.
3. Robots will replace senior specialists in diagnosis, as it requires more analysis and processing. Humans can't process large information and spot patterns in big data sets. This is where robots will score significantly higher in accuracy of diagnosis than a human specialist.
4. Surgery will be done by humans with assistance from robots. This has already been demonstrated by the University of Oxford Surgeons . So it is possible as more and more robots are built to do precision operations on humans and are successful, they will work jointly with human specialists to carry out complex, precision-based surgeries. This trend will start to emerge in the coming years. They may be termed as Auto-doctors and Guided-doctors. Auto-doctors would use unsupervised learning techniques to treat a patient for new discovery diseases. Guided-doctors would use supervised learning techniques. They would work for known diseases on known lines of treatments.
The healthcare industry in particular deals with human beings and their lives. This is one of those industries where a simple judgmental error could cause death to a patient. However, when we talk about building prediction models based on machine learning (ML), which is the brain behind any robot, we know that no matter what algorithm is selected for predicting the outcome from any data set, there is going to be a percentage of errors in the final prediction by the model. In the case of human beings, a human being or a human doctor or a healthcare professional is also prone to errors.
This is something that we know as human error.
So if we were to build and create a replacement or a competitor for a human doctor, we know that it would have to do better than this error rate. It can only survive if it gives predictive diagnosis at a lower error rate than that of the human doctor. Since we are dealing with human life in the healthcare industry, we need a gradual and careful way of adopting technology, as a lot is at stake. The requirement is to build robust algorithms with prediction models with higher accuracy levels.
In today’s age where there is a plethora of information and big data in healthcare strives to improve the current healthcare systems, machine learning can surely make a mark in its initiative to improve human health.
Here are five exciting applications of ML in healthcare:
1. ML IN HEALTHCARE FOR IMAGING AND DIAGNOSIS
With machine learning advancing at an astounding speed, machine learning is an active application in diagnosis of human diseases. Another important element of diagnosing an illness is medical imaging and its ability to show a more complete image of an illness. As machine learning operates on algorithms, healthcare specialists are aiming to leverage this technology in their field by actively developing algorithms and providing information to machines that can help them in imaging and analyze human bodies for abnormalities. By using smart machines machine on a human body, the machines can quickly scan through the body and can click images to detect diseases early on.
Deep learning is playing a key role in this regard as it is becoming more accessible thanks to richer data sources that can be used in the diagnostic process. The technology has some limits as it is incapable of explaining how it arrived at its predictions, although these ML applications are correct a lot of the time. Nevertheless, the technology, combined with healthcare professionals, can offer treatment solutions quicker with these advanced diagnosis tools by interpreting a result and deciding whether the machine’s treatment suggestions are correct or not.
One of the key components of a successful healthcare organization is its ability to identify a disease with speed and accuracy. With hundreds of drugs currently on clinical trial, scientists are entering the fray in high-need areas such as cancer identification and treatment. One such solution integrates cognitive computing with genomic tumor sequencing, while another uses ML to develop diagnostics and therapeutic treatments in multiple areas such as oncology. Another example is DeepMind Health, which is developing technology that can address macular degeneration in aging eyes.
2. ML IN HEALTHCARE FOR DATA COLLECTION AND FOLLOW-UPS
Personalization is what humans like when they go anywhere. As big data has several applications and gathers information from every possible source, leveraging the same to improve human life can be helpful for doctors to provide people with enhanced services. When ML can accommodate sufficient information about a user, doctors can personalize the treatment options. This personalization of services is possible with the help of machines providing insights about risks of a particular patient being susceptible to a specific disease. With accurate information and actionable insights, machines can also suggest users and doctors about remedies and precautionary measures with depending on a patient’s response to medications.
3. ML IN HEALTHCARE FOR RADIOLOGY AND RADIOTHERAPY
ML has proved its worth and capabilities to detect cancer in the past and is one of the most viable options for leading healthcare pioneers to identify any abnormalities. With such performance, ML is proving to be another strong option for radiology and radiotherapy. Doctors can use this technology to scan through the possibilities of a patient’s response to a specific input of radiations through their body. ML can also help doctors and surgeons in deciding what and how intense a radiation would be required depending on how well the patient responds to specific amounts of emissions.
4. ML IN HEALTHCARE FOR DRUG DISCOVERY AND EXPERIMENTS
Scientists strive to find ways of how they can discover newer ways to certain deadly diseases. With rigorous attempts at improving healthcare, they search for different drugs that can behave as advanced medicines and perform experiments that are focused solely on how these medications can help. Machine learning algorithms help scientists by providing them information about how to improve drug performance and behavior of the same on a test subject. The behavioral details that noted from a test subject and a dummy drug can be noted and ML algorithms can be used to determine how those medications perform on a human being.
ML has the capacity to discover new drugs that offers great economic value for pharmaceuticals, hospitals and new treatment avenues for patients. Some of the major tech players such as IBM and Google have created ML platforms designed to discover new routes of treatment for patients. Precision medicine is a key term in this topic as it consists of identifying mechanisms for multifactorial diseases and finding alternative paths for therapy. Institutions such as the MIT Clinical Machine Learning Group have been using precision medicine research to develop algorithms that can help doctors better understand disease processes and create effective treatments for diseases such as Type 2 diabetes.
5. ML IN HEALTHCARE FOR SURGERIES
We will always need human intervention for surgeries due to the high-risk nature of these procedures, but ML has been helping greatly in the robotic surgery space. One of the most popular developments in the field has been the da Vinci robot, which allows surgeons to manipulate robotic limbs in order to perform surgeries with great detail and in tight spaces. These hands are often steadier and more accurate than human hands. There are also tools that use computer vision aided by machine learning to identify the distances of specific body parts in order to adequately perform surgery on them. One example of this is the identification of hair follicles for hair transplantation surgery.
Current technological innovations continuously strive to improve the healthcare situation for patients and doctors. When machines focus on improving the performance of operations, they can help doctors by using surgical robots. These surgical robots prove to be of great help to doctors as they provide doctors with high definition imagery and extended flexibility to reach out in areas that are crucial for a doctor. Machine learning has several other applications in numerous fields that try to improve human life. As healthcare pioneers are working to improve the current scenario of their industry consistently, they can now search for ways in which their organization can leverage this technology and how they can benefit from the same.
In the next post we'll continue our discussion and discuss the Real-World Benefits of Machine Learning in Healthcare . So till we meet again keep learning!
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