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Almost all of human history is the history of technology. And now, finally, the time has come to expand the capabilities of human intelligence. There are plenty of examples where artificial intelligence (AI) is successfully applied in planning, science, and industry.
Now let’s think a little and dream up how artificial intelligence can transform healthcare in the not-too-distant future. So, in this article, we try to answer the question: AI in healthcare, what the future brings?
What Is AI in healthcare Today?
There are several basic types of AI of relevance to healthcare and other industries:
- The sorting of big medical data from the healthcare industry;
- Drawing up various scientific models;
- Computerized forecasting of future scenarios;
- AI Robots replacing or supplementing humans.
As for the sorting of data, AI works with loads of information and finds predefined elements. In the case of AI in healthcare, that will be the patient medical data. A good example of computerized data analysis is the interpretation of radiology images. Long-term planning of the future of healthcare requires the development and analysis of various scenarios. These studies use models based on tons of statistical data.
The most difficult thing is to build scientific models of poorly understood processes observed in the healthcare industry. And here, artificial intelligence is at its best, including self-learning neural networks, fuzzy logic, and other techniques of machine learning. AI solutions are absolutely indispensable for predictive analytics.
AI Technologies in Healthcare – What Are the Possible Scenarios for Future Years?
It’s impossible to imagine even one field of life sciences where AI is completely fruitless. Healthcare systems are developing at a particularly rapid pace. The growth of the population, as well as life expectancy, leads to problems with healthy nutrition.
In turn, these problems cause a number of diseases that are difficult to control, even with the entire healthcare system. This medical data can be useful in predicting the rate of death from common diseases, as well as in developing the healthcare systems needed to combat them.
This includes the:
- development of the healthcare system, including new healthcare organizations,
- the training of doctors,
- and the creation of new medications.
Solving these problems without AI solutions is almost impossible. In view of the exceptional complexity of these tasks, the future of the healthcare industry will use all the above AI models. Some other ideas that come to mind are much more exciting. Let’s mention just a few examples of AI in healthcare.
Identifying Cardiovascular Diseases
According to the WHO, cardiovascular diseases are the leading cause of death globally. One of the problems surrounding this topic is the early diagnosis of these diseases. This is where artificial intelligence comes in. AI can be used to help identify cardiovascular diseases in a number of ways, including:
Medical imaging analysis:
AI algorithms can be used to analyze medical images such as MRI, CT scans, and echocardiograms to detect early signs of cardiovascular diseases. This can include detecting the presence of plaque buildup in the arteries or abnormalities in the heart’s structure and function. This specially trained healthcare artificial intelligence would be incorporated in medical devices to help doctors give more accurate diagnoses.
ECG analysis:
AI algorithms can analyze electrocardiogram (ECG) recordings to detect abnormal heart rhythms and other signs of heart disease. This can include detecting arrhythmias, ST-segment changes, and other abnormalities that may be indicative of cardiovascular disease and rare diseases.
Risk assessment:
AI algorithms can analyze data such as medical history, family history, lifestyle factors, and other health metrics to identify individuals who are at high risk for developing cardiovascular diseases. This can help healthcare providers identify patients who may benefit from earlier screening, prevention, and treatment interventions.
Remote monitoring:
Camera-based tools such as Shen.AI that record heart rate and other vital signs can be used to monitor patients remotely, with AI algorithms analyzing the data to detect early signs of cardiovascular disease. This can include detecting changes in heart rate, blood pressure, and other metrics that may indicate the onset of cardiovascular disease.
AI has the potential to improve the accuracy and efficiency of cardiovascular disease detection and diagnosis, which can lead to earlier intervention and improved patient outcomes.
Remote Hypertension Recognition
AI has the potential to revolutionize healthcare, and remote hypertension recognition is one area where it can be particularly useful.
Hypertension, also known as high blood pressure, is a major risk factor for many health problems, including heart disease, stroke, and kidney failure. Early detection and treatment of hypertension is critical for preventing these conditions, but many people are not aware that they have high blood pressure.
Remote hypertension recognition refers to the use of AI to monitor blood pressure and detect hypertension in patients who are not physically present in a healthcare facility. This can be done using wearable devices that measure blood pressure and transmit the data to a healthcare provider or AI system for analysis.
Another way to detect hypertension early is to take regular measurements with camera-based measurement tools. Measuring pressure by smartphones and laptop cameras is the latest technological achievement.
Machine learning algorithms can identify patterns that indicate hypertension
There are a few ways that AI can be used in remote hypertension recognition. One approach is to use machine learning algorithms to analyze data from camera-based readings and identify patterns that indicate hypertension. For example, an AI system could analyze a patient’s blood pressure readings over time and detect if the readings consistently fall within the hypertensive range.
Another approach is to use AI to analyze other data that may be correlated with hypertension, such as physical activity levels or sleep quality. By looking for patterns in this data, an AI system may be able to detect hypertension even if blood pressure measurements are not available.
Overall, AI has the potential to improve hypertension recognition and management, particularly for patients who may not have easy access to healthcare facilities. However, it is important to continue studying and refining these tools to ensure that they are accurate, effective, and safe for patients.
Clinical Decision Support with AI
Artificial intelligence will support medical professionals in making clinical decisions. The process of making such a decision will be facilitated by AI access to a huge database (e.g., history of patients’ illnesses and symptoms in electronic health records). AI help will support doctors and allow for a more accurate assessment of the patient’s condition. Here are the areas where AI can have a significant impact on clinical decision support:
Predictive modeling:
Machine learning algorithms can be used to analyze large datasets and identify patterns that may not be immediately apparent to healthcare providers.
For example, predictive models can be developed to identify patients who are at high risk of developing a particular disease or condition, or who may experience complications during treatment. This can help clinicians make more informed decisions about diagnosis, treatment, and patient management.
Diagnostic support:
Machine learning can also be used to support clinical diagnosis by analyzing health data and identifying potential diagnoses.
For example, machine learning algorithms can be used to analyze medical images and detect early signs of disease that may be missed by human clinicians. With AI’s help, a doctor can compare healthcare data with other health information based on clinical documentation to help make a diagnosis.
Treatement optimization:
Machine learning algorithms can be used to analyze patient data and identify the most effective treatment options for individual patients.
This can include predicting which medications are likely to be most effective based on the patient’s medical history saved in the electronic health record, genetic makeup, and other factors.
Clinical decision support:
Machine learning and deep learning can be used to develop decision support tools that help clinicians make more informed decisions in real-time.
For example, a machine learning algorithm could analyze health data in real-time and provide recommendations for treatment options based on the patient’s specific medical history and symptoms.
Overall, machine learning has the potential to improve the quality of clinical decision-making and support more personalized, precise, and effective healthcare.
Upgrade Remote Patient Care with AI
Using AI can completely change patient interaction and disease management. Here are a few examples of digital health solutions that would upgrade caring for a sick person.
Remote monitoring for Patient Care
Smartphones, laptops or even smart mirrors that record vital signs and other health metrics can be used to monitor patients remotely, with AI algorithms analyzing the data to detect early signs of health issues. This can include detecting changes in heart rate, blood pressure, respiratory rate, and other metrics that may indicate the onset of health issues.
Chatbots and virtual assistants for constant touch
AI-powered chatbots and virtual assistants can be used to provide patients with personalized health advice and guidance. These tools can be used to answer patient questions, provide health education, and help patients manage chronic conditions.
Predictive modeling to avoid more damage
Machine learning algorithms can be used to analyze records in EHR and identify patients who are at high risk of developing certain health issues. This can help healthcare professionals proactively intervene and provide preventive care to avoid more serious health issues.
Telemedicine
AI can be used to support telemedicine by providing real-time translation services, speech-to-text, and other technologies that help healthcare providers communicate with patients more effectively.
AI Will Not Replace Caregivers, but It Creates More Care Possibilities
As outlined above, the healthcare industry recognizes excellent potential in AI models in patient contact, rapid response to health changes, and telemedicine. Using AI in these areas is an improvement that will create a new quality in digital health.
AI Speech recognition in Telemedicine
Another area that AI will step into is human speech recognition and processing – this is a big step in the health field because doctors can use this solution in the following ways:
Improved Accuracy:
AI speech recognition technology can accurately transcribe patient conversations with medical professionals, which can help improve the accuracy and completeness of patient records. This can save time and reduce the risk of errors in the documentation of patient care.
Streamlined workflows:
With AI natural language processing, doctors can quickly and easily generate notes and documentation, reducing the need for manual documentation and administrative tasks. This can help streamline workflows and allow providers to focus more on patient care.
Real-time translation:
AI speech-to-text technology can be used to provide real-time translation services, allowing healthcare providers to communicate more effectively with patients who speak different languages.
This can help improve access to care for patients who may have difficulty communicating with their healthcare provider due to language barriers.
Improved patient engagement:
By transcribing patient conversations and notes in real time, AI speech recognition and deep learning can provide patients with greater transparency into their care, helping to improve patient engagement and communication.
Data analysis:
Artificial Intelligence speech-to-text technology can be used to analyze large amounts of health data, including audio recordings of patient conversations, to identify trends and patterns that may be indicative of health issues. This can help medical professionals identify patients who may be at risk for certain health issues and provide proactive care.
Understanding human speech by AI in healthcare – a summary
The wide range of use of such a function in healthcare makes human speech recognition a priority in developing artificial intelligence.
The main improvements where we will observe understanding speech by AI are improved accuracy, streamlined workflows, real-time translation, improved patient engagement, and data analysis.
AI for Digital Therapeutics Apps
Digital therapeutics (DTx) apps are software-based interventions that can be used to prevent, manage, or treat medical conditions. These apps can be used alone or in combination with traditional medical treatments. AI can benefit digital therapeutics apps in several ways, including:
Personalization:
AI has the potential to revolutionize healthcare by personalizing digital therapeutics apps to meet the individual needs of patients. By analyzing data about a user’s behavior, symptoms, and preferences, AI algorithms and deep learning can develop a personalized approach to treatment that takes into account their unique circumstances.
For example, an AI-powered digital therapeutics app may collect data on a user’s exercise habits, diet, sleep patterns, and other health-related behaviors. The app can then use this data to provide tailored recommendations and interventions that are specifically designed to help the user improve their health outcomes.
This personalized approach to healthcare has the potential to significantly improve patient engagement and adherence to treatment regimens. By providing patients with targeted interventions and personalized feedback, digital therapeutics apps can empower patients to take an active role in their own healthcare, leading to better health outcomes and improved quality of life.
Overall, AI-powered digital therapeutics apps have the potential to transform the way we approach healthcare by providing personalized, targeted interventions that are designed to meet the unique needs of each patient.
While there are still many challenges to be addressed in the development and implementation of these apps, the potential benefits for patients are significant, and are likely to drive continued innovation in the field of digital therapeutics.
Decision support:
AI can be used to provide decision support for healthcare providers and patients using digital therapeutics apps. For example, Artificial Intelligence algorithms and deep learning can help medical professionals identify which patients are most likely to benefit from a particular digital therapeutic intervention.
Remote monitoring:
AI can be used to monitor patients remotely, allowing healthcare workers to track patient progress and identify potential problems early on. For example, AI algorithms can analyze patient data collected through digital therapeutics apps to identify changes in symptoms or other signs of deterioration.
Data analysis:
AI can be used to analyze large amounts of health data collected through digital therapeutics apps. This can help medical professionals identify trends and patterns in patient behavior and symptoms that may be indicative of health issues. Data analysis will be easily accessible thanks to diagrams and graphs. Access to health data by user consent will be available to various professionals. Thanks to this, doctors will access patients’ health information from multiple fields, and the treatment will be more comprehensive and fitted to the individual’s condition.
Continuous improvement:
AI can be used to continuously improve digital therapeutics apps over time. By analyzing user data, AI algorithms can identify areas where the app is not meeting user needs and suggest changes to improve the app’s effectiveness.
AI can help make digital therapeutics apps more effective and personalized, improving patient outcomes and making it easier for patients to access care.
Machine learning as one of the ways to eliminate the Covid-19 pandemy
Machine learning and deep learning is emerging as one of the potential ways to eliminate the COVID-19 pandemic. During the pandemic, researchers have identified various unique features and symptoms associated with the disease, including changes in voice patterns. By analyzing voice data, machine learning algorithms can help to detect the presence of COVID-19. This type of computer-aided detection could be achieved not only through the use of smartphone cameras but also through microphones.
This approach could enable patients to combine multiple measurements, including parameters related to blood circulation such as pulse and blood pressure, as well as respiratory measurements such as respiratory rate, cough, and voice timbre. By using machine learning algorithms to analyze this data, medical professionals may be able to identify COVID-19 infections more quickly and accurately, allowing for earlier intervention and treatment.
While this approach shows promise, it is important to note that more research is needed to fully understand the relationship between voice changes and COVID-19 infection, and to evaluate the accuracy and reliability of machine learning algorithms in this context. However, the potential for machine learning to aid in the fight against the COVID-19 pandemic demonstrates the importance of leveraging technology to develop innovative solutions to public health challenges.
In the context of the much larger amount of health data needed and the lack of special observations, heuristic approaches should help make a real breakthrough in the diagnosis of coronavirus and similar infections.
This is all the more relevant given that existing laboratory methods are time-consuming and inaccessible to many potential carriers of the virus due to the remoteness of diagnostic laboratories. Another factor is unnecessary hospital visits at the risk of spreading the infection.
Of course, this approach does not guarantee the detection of the virus itself, but a simultaneous sharp increase in temperature and respiration rate, as changes in blood pressure and voice are serious arguments, even when a patient’s experience is comfortable.
Don’t forget that the accuracy of express diagnostics for Covid-19 is rarely above 70-80%.
Robots: Another Example of AI in Healthcare
The phrase “artificial intelligence” for many people means human-like robots. And this is no accident. Artificial intelligence robots in medicine have long been a reality. One of the most evident options of these is patient care for sick and elderly people.
As a rule, they have to perform the same repetitive tasks: measure the patient’s basic health indicators, massage lying patients, and, if necessary, contact healthcare professionals, especially if the patient’s condition requires urgent action. Robots can be a support and relief for social workers, who often have a huge responsibility for the health and life of the patient, whose condition can deteriorate at any time. In particular, such assistance will be needed in the case of aging societies.
Surgical robots demonstrate high efficiency. However, their tasks are rather limited. Such robots are not capable of acting completely by Artificial Intelligence algorithms and autonomously from humans. It’s basically impossible to put the entire arsenal of medical information into a program.
However, robots have a huge advantage in precision when it comes to suturing tiny blood vessels and nerve endings. The smaller the surgical instruments are, the stronger the imperfection of the fine motor skills of the surgeon’s hands.
The robot, on the other hand, performs actions that are often limited in variety, but they do it with impeccable accuracy. At the same time, specialists control and direct the actions of the robot using optical observation systems and computer applications.
The areas of medicine in which surgical robots are particularly effective include ophthalmology and neurosurgery.
Computer-Aided Psychiatry – a Path to a Revolution in Healthcare
Computer-aided psychiatry is expected to change healthcare in the next few years. According to healthcare organizations, more than 12% of people in the world have signs of mental disorders. Moreover, this number grows every year.
Along with patients in psychiatric clinics who undergo regular examinations, there are many hidden patients who are not registered in medical records. This problem is too complex to be solved only with the help of medical examinations: there are simply not enough specialists to perform a patient diagnosis. The use of Artificial Intelligence solutions is one of the promising ways out.
Artificial Intelligence behavioral analysis refers to the use of machine learning algorithms and deep learning to analyze and interpret patterns in human behavior, such as speech, facial expressions, and movement. In the context of mental health, AI behavioral analysis is often used to help diagnose and treat psychiatric disorders.
For example, AI behavioral analysis can be used to analyze speech patterns and detect changes in tone or intonation that may indicate a mental well-being issue. These changes in speech may be indicative of depression, anxiety, or other disorders. AI algorithms can also analyze facial expressions to detect signs of stress, anxiety, or other mental disorders and illnesses.
A lot of data has already been gained in early recognition of external signs of serious mental illness, such as schizophrenia or epilepsy. For example, in the early stage of the development of schizophrenia, a specific pattern of facial expressions is characteristic: twitching of the facial muscles, especially the mouth, frequent blinking, the lack of focus of the look, etc.
At the same time, there is a steady decrease in respiratory rate, heart rate, and blood pressure. However, unlike other diseases, mental problems are more difficult to fix since such patients can be secretive. Therefore, it is necessary to develop AI technology that allows for collecting information about a risk group in the background.
Often the conclusion of psychiatrists is based not on objective criteria but on personal experience and intuition. This means that psychiatrists and psychologists will remain the backbone when diagnosing mental illnesses. The achievements of new technology, such as Artificial Intelligence, can help regularly examine patients’ physical and mental states.
AI behavioral analysis can also be used to develop predictive models that can identify patients who are at risk for developing mental illnesses. For example, machine learning algorithms can analyze social media data to identify patterns that may be associated with depression or anxiety.
One potential benefit of AI behavioral analysis is that it can help overcome some of the limitations of traditional methods for diagnosing and treating mental health disorders. For example, it can be difficult for clinicians to detect changes in behavior and mood in patients who are not regularly seeing a mental health professional. AI behavioral analysis can help fill this gap by providing continuous monitoring of patients.
How AI is changing psychiatry medical diagnosis?
The use of artificial intelligence can reduce the severity of this problem through patient data and impartial computer analysis. This is a case when complex heuristic analysis based on self-learning algorithms is needed.
As for the rest, we can take as a basis already proven technologies of photoplethysmography: the remote measurement of physiological indicators of health.
Accordingly, the main problem is not achieving the necessary photo and video data but what part of them to use for further image analysis. AI cannot replace a psychiatrist when diagnosing problems within a mental sphere, but it will help doctors to make a diagnosis in a shorter time.
Here, the long-term research and development of statistics are still ahead.
How Can Shen.AI Change Healthcare?
MX Labs implemented a rather recent AI technology for healthcare known as remote photoplethysmography, which made our company a game changer in innovative solutions for well-being.
Our artificial intelligence technology is now used in the healthcare industry, covering key issues faced by healthcare providers. Non-contact measurements of critical physiological health indicators such as heart rate and blood pressure will change the future of healthcare.
This data can be collected daily, even without a single contact with a person. Combined with other AI technologies, our capabilities are an important element of future healthcare. Humanity is moving rapidly toward new digital standards of health and well-being.
Learn the secrets of increasing the involvement of patients
It turns out that people who monitor their health are more willing to cooperate with the doctor and take medications regularly. The entire process of diagnosis and treatment is more efficient.
Regularly taking care of one’s health improves a person’s mental state and increases his or her involvement in the recovery process.
At MX Labs, we want to empower patients. Our tools are smartphone cameras, machine vision, deep learning models and artificial intelligence. And the power of the human mind uses this new technology to create a holistic health tool – Shen.AI.
With Shen.AI, you can measure your blood pressure, pulse, HRV, and physiological stress in real time and only with a smartphone camera. All results of measurements can be sent directly into Electronic Health Records. This solution is in the form of an SDK or a dedicated application. This new healthcare technology is the future of healthcare research and population health care in general.