Artificial intelligence in healthcare has the potential to transform many aspects of patient care and administrative processes in healthcare. I think the role of artificial intelligence will be an asset to all healthcare professionals. The following article contains examples of artificial intelligence in healthcare and companies doing a great job at it.
Introduction – Artificial Intelligence in Healthcare.
Artificial intelligence (AI), Machine learning, NLP, Robotics, and Automation are increasingly prevalent in all aspects and are being applied to healthcare as well. These technologies have the potential to transform all aspects of health care from patient care to the development and production of new experimental drugs that can have a faster roll-out date than traditional methods.
There are numerous research studies suggesting that AI can outperform humans at key healthcare tasks, such as diagnosing ailments. Here is a great example, AI ‘outperforms’ doctors diagnosing breast cancer¹.
Artificial intelligence is a collection of technologies that come together form artificial intelligence. AI’s diverse range of technologies impacts a wide spectrum of healthcare.
Tech firms and startups are also working assiduously on the same issues. Google, for example, is collaborating with health delivery networks to build prediction models from big data to warn clinicians of high-risk conditions, such as sepsis and heart failure. Google, Enlitic, and a variety of other startups are developing AI-derived image interpretation algorithms. Jvion offers a ‘clinical success machine’ that identifies the patients most at risk as well as those most likely to respond to treatment protocols. Each of these could provide decision support to clinicians seeking to find the best diagnosis and treatment for patients.
You will find below some technologies that improve a specific area in healthcare with examples sourced from the internet with citations.
Machine learning
Machine learning is an application of artificial intelligence (AI)that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves².
There are majorly three types of Machine learning —
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In healthcare, the most common application of machine learning is predictive medicine — predicting what treatment alternatives are likely to work best on a patient based on various patient traits, history, the treatment situation, and protocols. The supervised learning model for predictive medicine applications requires a training dataset, like all supervised learning models. the difference here is that there may be a lot of variables.
Using neural networks it is now possible to also predict whether a patient will acquire a particular ailment or not based on a set of variables and conditions that can be fed into algorithms in the form of data.
One common application of deep learning and neural networks is the ability to detect ailments/issues in the radiology images. I think deep learning should be increasingly applied wherever clinically possible. This will allow doctors and radiologists to just supervise results and focus on other important aspects of their job. This combination promises better accuracy of finding ailments with limited human intervention or supervision.
Here are some organizations that are doing groundbreaking work in this area.
Organization: PathAI
Location: Cambridge, Massachusetts
How it’s using AI in healthcare: PathAI is developing machine learning technology to assist pathologists in making more accurate diagnoses. The company’s current goals include reducing errors in cancer diagnosis and developing methods for individualized medical treatment³.
Organization: Enlitic
Location: San Francisco, California
How it’s using AI in healthcare: Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company’s deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs³.
Also Read: Artificial Intelligence and Otolaryngology.
Natural language processing
In healthcare, most applications of NLP involve the creation, understanding, parsing, and classification of clinical documentation and published research. NLP can also be used to analyze clinical notes, prescriptions, help prepare reports, and possibly conversational AI. Few good examples of how NLP is currently being used.
- Parsing data realtime from coronavirus research that is being published globally. You can find more information about this in my article here.
- Project Meena by Google. More information can be found here.
Decision Tree
Decision trees require doctors and engineers to come up with an if-then-else decision flow chart that can help train machines to make decisions by building complex algorithms based on the finalized decision tree. This is critical and processes heavy software design, this enables the machines to take accurate decisions with human intervention. This will help save a ton of time for doctors and patients alike. This will enhance the capabilities of doctors to predict, analyze, and come up with a treatment plan for patient care.
This can also be used extensively in vaccine and treatment research provided the known variable is the ailment we are making the vaccine for and its pre-set conditions and protocols. This is an effective mechanism for pre-morbidity patients as well.
Robotics
Robots are becoming more intelligent, as other AI capabilities are being embedded in their OS. Other areas of improvements in AI have exponentially improved the capabilities of the robots and their ability to perform complex operations.
One such area of operation is robotic surgery. This enables surgeons to perform complex procedures with much greater precision and create precise, minimized, invasive incisions, and stitches. This is a game-changer in performing surgery, as long as human supervision exists.
Here are some examples of organizations using AI and Robotics
Organization: Vicarious Surgical
Location: Charlestown, Massachusetts
How it’s using AI in healthcare: Vicarious Surgical combines virtual reality with AI-enabled robots so surgeons can perform minimally invasive operations. Using the company’s technology, surgeons can virtually shrink and explore the inside of a patient’s body in much more detail³.
Organization: Auris Health
Location: Redwood City, California
How it’s using AI in healthcare: Auris Health develops a variety of robots designed to improve endoscopies by employing the latest in micro-instrumentation, endoscope design, data science and AI. Consequently, doctors get a clearer view of a patient’s illness from both physical and data perspective³.
Organization: Accuray
Location: Sunnyvale, California
How it’s using AI in healthcare: The Accuray CyberKnife System uses robotic arms to precisely treat cancerous tumors all over the body. Using the robot’s real-time tumor tracking capabilities, doctors and surgeons are able to treat only affected areas rather than the whole body. The Accuray CyberKnife robot uses 6D motion-sensing technology to aggressively track and attack cancerous tumors while saving healthy tissue³.
Organization: Intuitive
Location: San Francisco, California
How it’s using AI in healthcare: Intuitive’s da Vinci platforms have pioneered the robotic surgery industry. Being the first robotic surgery assistant approved by the FDA over 18 years ago, the surgical machines feature cameras, robotic arms, and surgical tools to aide in minimally invasive procedures.
The da Vinci platform is constantly taking in information and providing analytics to surgeons to improve future surgeries. So far, da Vinci has assisted in over five million operations³.
University: Carnegie Mellon University
Location: Pittsburgh, Pennsylvania
How it’s using AI in healthcare: The robotics department at Carnegie Mellon University developed Heartlander, a miniature mobile robot designed to facilitate therapy on the heart. Under a physician’s control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart, and administers therapy³.
Organization: MicroSure
Location: Eindhoven, The Netherlands
How it’s using AI in healthcare: MicroSure robots help surgeons overcome their human physical limitations. The company’s motion stabilizer system reportedly improves performance and precision during surgical procedures. Currently, eight of MicroSure’s micro-surgical operations are approved for lymphatic system procedures³.
Organization: Mazor Robotics
Location: Caesarea, Israel
How it’s using AI in healthcare: Surgeons use the Mazor Robotics’ 3D tools to visualize their surgical plans, read images with AI that recognizes anatomical features and perform a more stable and precise spinal operation³.
Also Read: How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?
Robotic process automation
Robotic process automation performs structured digital tasks for administrative purposes, ie those involving information systems, as if they were a human user following a script or rules. It relies on a combination of workflow, business rules, and ‘presentation layer’ integration with information systems to act like a semi-intelligent user of the systems. In healthcare, they are used for repetitive tasks like prior authorization, updating patient records, or billing. When combined with other technologies like image recognition, they can be used to extract data from, for example, faxed images in order to input it into transactional systems.
Mass personalization
Artificial Intelligence can help in mass personalization of patient care, treatments, procedures, vaccine research, and production. This along with human interaction can reduce costs and improve coverage across the board for healthcare.
AI can help with various aspects of patient care, like, charting the history of the patient, admission process, diagnosis, tests, treatment plans, and procedures. AI can help in every aspect of patient care including helping hospital administrators with managing massive amounts of patient data and patient care options.
AI can also help with patient adherence to treatment and management of the ailments or prophylactic care. This can help in reducing health care costs on the individual patient. if this is implemented on a large scale this can cut down or reduce healthcare costs. These savings can be re-invested in more advanced equipment, technology, better education for doctors, and healthcare professionals in a technologically shifting landscape.
Here are some examples of organizations working in this area.
Organization: Buoy Health
Location: Boston, Massachusetts
How it’s using AI in healthcare: Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. Here’s how it works: a chatbot listens to a patient’s symptoms and health concerns, then guides that patient to the correct care based on its diagnosis³.
Organization: Freenome
Location: San Francisco, California
How it’s using AI in healthcare: Freenome uses AI in screenings, diagnostic tests, and blood work to test for cancer. By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments³.
Organization: Beth Israel Deaconess Medical Center
Location: Boston, Massachusetts
How it’s using AI in healthcare: Harvard University’s teaching hospital, Beth Israel Deaconess Medical Center, is using artificial intelligence to diagnose potentially deadly blood diseases at a very early stage. Doctors are using AI-enhanced microscopes to scan for harmful bacterias (like E. coli and staphylococcus) in blood samples at a faster rate than is possible using manual scanning. The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95% accuracy³.
Organization: Olive AI
Location: Columbus, Ohio
How it’s using AI in healthcare: Olive’s AI platform is designed to automate the healthcare industry’ most repetitive tasks, freeing up administrators to work on higher-level ones. The platform automates everything from eligibility checks to un-adjudicated claims and data migrations so staffers can focus on providing better patient service.
Olive’s AI-as-a-Service easily integrates within a hospital’s existing software and tools, eliminating the need for costly integrations or downtimes³.
Organization: Cleveland Clinic
Location: Cleveland, Ohio
How it’s using AI in healthcare: The Cleveland Clinic teamed up with IBM to infuse its IT capabilities with artificial intelligence. The world-renowned hospital is using AI to gather information on trillions of administrative and health record data points to streamline the patient experience. This marriage of AI and data is helping the Cleveland Clinic personalize healthcare plans on an individual basis³.
Organization: Tempus
Location: Chicago, Illinois
How it’s using AI in healthcare: Tempus is using AI to sift through the world’s largest collection of clinical and molecular data in order to personalize healthcare treatments. The company is developing AI tools that collect and analyze data in everything from genetic sequencing to image recognition, which can give physicians better insights into treatments and cures. Tempus is currently using its AI-driven data to tackle cancer research and treatment³.
Hospital administration
There are also a great many administrative applications in healthcare. The use of AI is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies.
Using the patient profile AI can be used to predict the type of care, specialists, nursing staff, equipment, bed occupancy, tests, and medication requirements per patient, this will help hospital administration in better procurement procedures and reduce wastage. The advanced version could also automate the procurement and help with predictive ordering to avoid shortages at hospitals. This can also help in reducing the space required for storing all this equipment as well, as predictive ordering and robotic automation process can improve supply chain capabilities. This will help hospital administrators make efficient calls in terms of staffing hospitals with the right level of resources being maintained at all times.
AI can be used for a variety of applications in healthcare, including claims processing, clinical documentation, revenue cycle management, and medical records management etc. AI can also be used to improve payment processing systems, third party integration with insurance companies, labs.. etc. Reduce time for claims and improve the user experience of patients/families who are already distressed when visiting the hospital.
Here are some examples of organizations doing great work in these areas
Organization: Qventus
Location: Mountain View, California
How it’s using AI in healthcare: Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company’s automated platform prioritizes patient illness/injury, tracks hospital waiting times and can even chart the fastest ambulance routes.
CB Insights named Qventus one of its 100 Most Innovative AI Startups for 2019 based on the company’s work in automating and prioritizing patient safety³.
Organization: Babylon Health Care
Location: New York, New York
How it’s using AI in healthcare: Babylon uses AI to provide personalized and interactive healthcare, including anytime face-to-face appointments with doctors. The company’s AI-powered chatbot streamlines the review of a patient’s symptoms, then recommends either a virtual check-in or a face-to-face visit with a healthcare professional.
Babylon and Canada’s Telus Health teamed up to develop a Canada-specific AI app that scans a patient’s survey answers, then connects them via video with the right healthcare provider or professional³.
Organization: Cloud Med X Health
Location: San Francisco, California
How it’s using AI in healthcare: CloudMedX uses machine learning to generate insights for improving patient journeys throughout the healthcare system.
The company’s technology helps hospitals and clinics manage patient data, clinical history, and payment information by using predictive analytics to intervene at critical junctures in the patient care experience. Healthcare providers can use these insights to efficiently move patients through the system without any of the traditional confusion³.
Ethical implications of artificial intelligence in healthcare.
Finally, there are also a variety of ethical implications around the use of AI in healthcare. Healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission, and privacy.
Perhaps the most difficult issue to address given today’s technologies is transparency. Many AI algorithms — particularly deep learning algorithms used for image analysis — are virtually impossible to interpret or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation.
Mistakes will undoubtedly be made by AI systems in patient diagnosis and treatment and it may be difficult to establish accountability for them. There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician. Machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting the greater likelihood of disease on the basis of gender or race when those are not actually causal factors.
We are likely to encounter many ethical, medical, occupational, and technological changes with AI in healthcare. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures to monitor key issues, react in a responsible manner, and establish governance mechanisms to limit negative implications. This is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years.
The future of artificial intelligence in healthcare
We believe that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care. Although early efforts at providing diagnosis and treatment recommendations have proven challenging, we expect that AI will ultimately master that domain as well. Given the rapid advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine. Speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase.
The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather to ensure their adoption in daily clinical practice. For widespread adoption to take place, AI systems must be approved by regulators, integrated with EHR systems, standardized to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organizations, and updated over time in the field. These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10.
It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion, and big-picture integration. Perhaps the only healthcare providers who will lose their jobs over time maybe those who refuse to work alongside artificial intelligence.
References
Fergus Walsh. AI ‘outperforms’ doctors diagnosing breast cancer. 2 January 2020. BBC. Website. 12 July 2020.
Expert System Team. What is Machine Learning? A definition. 6 May 2020. Expert System. Website. 12 July 2020.
Sam Daley. SURGICAL ROBOTS, NEW MEDICINES AND BETTER CARE: 32 EXAMPLES OF AI IN HEALTHCARE. 4 July 2019. https://builtin.com/. 25 March 2020. Website. 12 July 2020.
Artificial intelligence in healthcare has the potential to transform many aspects of patient care and administrative processes in healthcare. I think the role of artificial intelligence will be an asset to all healthcare professionals. The following article contains examples of artificial intelligence in healthcare and companies doing a great job at it.
Table of contents
Introduction – Artificial Intelligence in Healthcare.
Artificial intelligence (AI), Machine learning, NLP, Robotics, and Automation are increasingly prevalent in all aspects and are being applied to healthcare as well. These technologies have the potential to transform all aspects of health care from patient care to the development and production of new experimental drugs that can have a faster roll-out date than traditional methods.
There are numerous research studies suggesting that AI can outperform humans at key healthcare tasks, such as diagnosing ailments. Here is a great example, AI ‘outperforms’ doctors diagnosing breast cancer¹.
Artificial intelligence is a collection of technologies that come together form artificial intelligence. AI’s diverse range of technologies impacts a wide spectrum of healthcare.
Tech firms and startups are also working assiduously on the same issues. Google, for example, is collaborating with health delivery networks to build prediction models from big data to warn clinicians of high-risk conditions, such as sepsis and heart failure. Google, Enlitic, and a variety of other startups are developing AI-derived image interpretation algorithms. Jvion offers a ‘clinical success machine’ that identifies the patients most at risk as well as those most likely to respond to treatment protocols. Each of these could provide decision support to clinicians seeking to find the best diagnosis and treatment for patients.
You will find below some technologies that improve a specific area in healthcare with examples sourced from the internet with citations.
Machine learning
Machine learning is an application of artificial intelligence (AI)that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves².
There are majorly three types of Machine learning —
In healthcare, the most common application of machine learning is predictive medicine — predicting what treatment alternatives are likely to work best on a patient based on various patient traits, history, the treatment situation, and protocols. The supervised learning model for predictive medicine applications requires a training dataset, like all supervised learning models. the difference here is that there may be a lot of variables.
Using neural networks it is now possible to also predict whether a patient will acquire a particular ailment or not based on a set of variables and conditions that can be fed into algorithms in the form of data.
One common application of deep learning and neural networks is the ability to detect ailments/issues in the radiology images. I think deep learning should be increasingly applied wherever clinically possible. This will allow doctors and radiologists to just supervise results and focus on other important aspects of their job. This combination promises better accuracy of finding ailments with limited human intervention or supervision.
Here are some organizations that are doing groundbreaking work in this area.
Organization: PathAI
Location: Cambridge, Massachusetts
How it’s using AI in healthcare: PathAI is developing machine learning technology to assist pathologists in making more accurate diagnoses. The company’s current goals include reducing errors in cancer diagnosis and developing methods for individualized medical treatment³.
Organization: Enlitic
Location: San Francisco, California
How it’s using AI in healthcare: Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company’s deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs³.
Also Read: Artificial Intelligence and Otolaryngology.
Natural language processing
In healthcare, most applications of NLP involve the creation, understanding, parsing, and classification of clinical documentation and published research. NLP can also be used to analyze clinical notes, prescriptions, help prepare reports, and possibly conversational AI. Few good examples of how NLP is currently being used.
Decision Tree
Decision trees require doctors and engineers to come up with an if-then-else decision flow chart that can help train machines to make decisions by building complex algorithms based on the finalized decision tree. This is critical and processes heavy software design, this enables the machines to take accurate decisions with human intervention. This will help save a ton of time for doctors and patients alike. This will enhance the capabilities of doctors to predict, analyze, and come up with a treatment plan for patient care.
This can also be used extensively in vaccine and treatment research provided the known variable is the ailment we are making the vaccine for and its pre-set conditions and protocols. This is an effective mechanism for pre-morbidity patients as well.
Robotics
Robots are becoming more intelligent, as other AI capabilities are being embedded in their OS. Other areas of improvements in AI have exponentially improved the capabilities of the robots and their ability to perform complex operations.
One such area of operation is robotic surgery. This enables surgeons to perform complex procedures with much greater precision and create precise, minimized, invasive incisions, and stitches. This is a game-changer in performing surgery, as long as human supervision exists.
Here are some examples of organizations using AI and Robotics
Organization: Vicarious Surgical
Location: Charlestown, Massachusetts
How it’s using AI in healthcare: Vicarious Surgical combines virtual reality with AI-enabled robots so surgeons can perform minimally invasive operations. Using the company’s technology, surgeons can virtually shrink and explore the inside of a patient’s body in much more detail³.
Organization: Auris Health
Location: Redwood City, California
How it’s using AI in healthcare: Auris Health develops a variety of robots designed to improve endoscopies by employing the latest in micro-instrumentation, endoscope design, data science and AI. Consequently, doctors get a clearer view of a patient’s illness from both physical and data perspective³.
Organization: Accuray
Location: Sunnyvale, California
How it’s using AI in healthcare: The Accuray CyberKnife System uses robotic arms to precisely treat cancerous tumors all over the body. Using the robot’s real-time tumor tracking capabilities, doctors and surgeons are able to treat only affected areas rather than the whole body. The Accuray CyberKnife robot uses 6D motion-sensing technology to aggressively track and attack cancerous tumors while saving healthy tissue³.
Organization: Intuitive
Location: San Francisco, California
How it’s using AI in healthcare: Intuitive’s da Vinci platforms have pioneered the robotic surgery industry. Being the first robotic surgery assistant approved by the FDA over 18 years ago, the surgical machines feature cameras, robotic arms, and surgical tools to aide in minimally invasive procedures.
The da Vinci platform is constantly taking in information and providing analytics to surgeons to improve future surgeries. So far, da Vinci has assisted in over five million operations³.
University: Carnegie Mellon University
Location: Pittsburgh, Pennsylvania
How it’s using AI in healthcare: The robotics department at Carnegie Mellon University developed Heartlander, a miniature mobile robot designed to facilitate therapy on the heart. Under a physician’s control, the tiny robot enters the chest through a small incision, navigates to certain locations of the heart by itself, adheres to the surface of the heart, and administers therapy³.
Organization: MicroSure
Location: Eindhoven, The Netherlands
How it’s using AI in healthcare: MicroSure robots help surgeons overcome their human physical limitations. The company’s motion stabilizer system reportedly improves performance and precision during surgical procedures. Currently, eight of MicroSure’s micro-surgical operations are approved for lymphatic system procedures³.
Organization: Mazor Robotics
Location: Caesarea, Israel
How it’s using AI in healthcare: Surgeons use the Mazor Robotics’ 3D tools to visualize their surgical plans, read images with AI that recognizes anatomical features and perform a more stable and precise spinal operation³.
Also Read: How can Artificial Intelligence help with the Coronavirus (Covid-19) vaccine search?
Robotic process automation
Robotic process automation performs structured digital tasks for administrative purposes, ie those involving information systems, as if they were a human user following a script or rules. It relies on a combination of workflow, business rules, and ‘presentation layer’ integration with information systems to act like a semi-intelligent user of the systems. In healthcare, they are used for repetitive tasks like prior authorization, updating patient records, or billing. When combined with other technologies like image recognition, they can be used to extract data from, for example, faxed images in order to input it into transactional systems.
Mass personalization
Artificial Intelligence can help in mass personalization of patient care, treatments, procedures, vaccine research, and production. This along with human interaction can reduce costs and improve coverage across the board for healthcare.
AI can help with various aspects of patient care, like, charting the history of the patient, admission process, diagnosis, tests, treatment plans, and procedures. AI can help in every aspect of patient care including helping hospital administrators with managing massive amounts of patient data and patient care options.
AI can also help with patient adherence to treatment and management of the ailments or prophylactic care. This can help in reducing health care costs on the individual patient. if this is implemented on a large scale this can cut down or reduce healthcare costs. These savings can be re-invested in more advanced equipment, technology, better education for doctors, and healthcare professionals in a technologically shifting landscape.
Here are some examples of organizations working in this area.
Organization: Buoy Health
Location: Boston, Massachusetts
How it’s using AI in healthcare: Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness. Here’s how it works: a chatbot listens to a patient’s symptoms and health concerns, then guides that patient to the correct care based on its diagnosis³.
Organization: Freenome
Location: San Francisco, California
How it’s using AI in healthcare: Freenome uses AI in screenings, diagnostic tests, and blood work to test for cancer. By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments³.
Organization: Beth Israel Deaconess Medical Center
Location: Boston, Massachusetts
How it’s using AI in healthcare: Harvard University’s teaching hospital, Beth Israel Deaconess Medical Center, is using artificial intelligence to diagnose potentially deadly blood diseases at a very early stage. Doctors are using AI-enhanced microscopes to scan for harmful bacterias (like E. coli and staphylococcus) in blood samples at a faster rate than is possible using manual scanning. The scientists used 25,000 images of blood samples to teach the machines how to search for bacteria. The machines then learned how to identify and predict harmful bacteria in blood with 95% accuracy³.
Organization: Olive AI
Location: Columbus, Ohio
How it’s using AI in healthcare: Olive’s AI platform is designed to automate the healthcare industry’ most repetitive tasks, freeing up administrators to work on higher-level ones. The platform automates everything from eligibility checks to un-adjudicated claims and data migrations so staffers can focus on providing better patient service.
Olive’s AI-as-a-Service easily integrates within a hospital’s existing software and tools, eliminating the need for costly integrations or downtimes³.
Organization: Cleveland Clinic
Location: Cleveland, Ohio
How it’s using AI in healthcare: The Cleveland Clinic teamed up with IBM to infuse its IT capabilities with artificial intelligence. The world-renowned hospital is using AI to gather information on trillions of administrative and health record data points to streamline the patient experience. This marriage of AI and data is helping the Cleveland Clinic personalize healthcare plans on an individual basis³.
Organization: Tempus
Location: Chicago, Illinois
How it’s using AI in healthcare: Tempus is using AI to sift through the world’s largest collection of clinical and molecular data in order to personalize healthcare treatments. The company is developing AI tools that collect and analyze data in everything from genetic sequencing to image recognition, which can give physicians better insights into treatments and cures. Tempus is currently using its AI-driven data to tackle cancer research and treatment³.
Hospital administration
There are also a great many administrative applications in healthcare. The use of AI is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies.
Using the patient profile AI can be used to predict the type of care, specialists, nursing staff, equipment, bed occupancy, tests, and medication requirements per patient, this will help hospital administration in better procurement procedures and reduce wastage. The advanced version could also automate the procurement and help with predictive ordering to avoid shortages at hospitals. This can also help in reducing the space required for storing all this equipment as well, as predictive ordering and robotic automation process can improve supply chain capabilities. This will help hospital administrators make efficient calls in terms of staffing hospitals with the right level of resources being maintained at all times.
AI can be used for a variety of applications in healthcare, including claims processing, clinical documentation, revenue cycle management, and medical records management etc. AI can also be used to improve payment processing systems, third party integration with insurance companies, labs.. etc. Reduce time for claims and improve the user experience of patients/families who are already distressed when visiting the hospital.
Here are some examples of organizations doing great work in these areas
Organization: Qventus
Location: Mountain View, California
How it’s using AI in healthcare: Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company’s automated platform prioritizes patient illness/injury, tracks hospital waiting times and can even chart the fastest ambulance routes.
CB Insights named Qventus one of its 100 Most Innovative AI Startups for 2019 based on the company’s work in automating and prioritizing patient safety³.
Organization: Babylon Health Care
Location: New York, New York
How it’s using AI in healthcare: Babylon uses AI to provide personalized and interactive healthcare, including anytime face-to-face appointments with doctors. The company’s AI-powered chatbot streamlines the review of a patient’s symptoms, then recommends either a virtual check-in or a face-to-face visit with a healthcare professional.
Babylon and Canada’s Telus Health teamed up to develop a Canada-specific AI app that scans a patient’s survey answers, then connects them via video with the right healthcare provider or professional³.
Organization: Cloud Med X Health
Location: San Francisco, California
How it’s using AI in healthcare: CloudMedX uses machine learning to generate insights for improving patient journeys throughout the healthcare system.
The company’s technology helps hospitals and clinics manage patient data, clinical history, and payment information by using predictive analytics to intervene at critical junctures in the patient care experience. Healthcare providers can use these insights to efficiently move patients through the system without any of the traditional confusion³.
Ethical implications of artificial intelligence in healthcare.
Finally, there are also a variety of ethical implications around the use of AI in healthcare. Healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission, and privacy.
Perhaps the most difficult issue to address given today’s technologies is transparency. Many AI algorithms — particularly deep learning algorithms used for image analysis — are virtually impossible to interpret or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation.
Mistakes will undoubtedly be made by AI systems in patient diagnosis and treatment and it may be difficult to establish accountability for them. There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician. Machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting the greater likelihood of disease on the basis of gender or race when those are not actually causal factors.
We are likely to encounter many ethical, medical, occupational, and technological changes with AI in healthcare. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures to monitor key issues, react in a responsible manner, and establish governance mechanisms to limit negative implications. This is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years.
The future of artificial intelligence in healthcare
We believe that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care. Although early efforts at providing diagnosis and treatment recommendations have proven challenging, we expect that AI will ultimately master that domain as well. Given the rapid advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine. Speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase.
The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather to ensure their adoption in daily clinical practice. For widespread adoption to take place, AI systems must be approved by regulators, integrated with EHR systems, standardized to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organizations, and updated over time in the field. These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10.
It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion, and big-picture integration. Perhaps the only healthcare providers who will lose their jobs over time maybe those who refuse to work alongside artificial intelligence.
References
Fergus Walsh. AI ‘outperforms’ doctors diagnosing breast cancer. 2 January 2020. BBC. Website. 12 July 2020.
Expert System Team. What is Machine Learning? A definition. 6 May 2020. Expert System. Website. 12 July 2020.
Sam Daley. SURGICAL ROBOTS, NEW MEDICINES AND BETTER CARE: 32 EXAMPLES OF AI IN HEALTHCARE. 4 July 2019. https://builtin.com/. 25 March 2020. Website. 12 July 2020.
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