The world is rapidly changing and evolving, bringing new possibilities, challenges and threats. Adapting to any condition is crucial to any business, and healthcare is not an exception. Any sphere where human labor is involved can suffer from human resources-related inefficiency in different periods and situations. However, this becomes especially critical if it comes down to human health or lives.

Awareness of timeliness is pivotal in health matters. Timely issue detection and rapid solving can reduce fatalities, while the latest technological developments can elevate healthcare work to a new level. There is a need to change the healthcare industry in many aspects. Helping medical professionals save time on routine tasks and dedicate it to providing more attentive and correct treatment can become one of those positive changes.

So, how computer vision and machine learning can improve the processes in the healthcare industry and bring those changes to the quality of service and effectiveness of business? How to promptly overcome challenges and help the patient get back to their daily routines? If you are seeking answers to these questions, we can help to find them.

Machine Learning potential in modern healthcare

Let’s start by evaluating the potential of ML application in healthcare management. It is expected that the implementation of AI will positively affect the healthcare system and grow from $2.1 billion to $36.1 billion by 2025.  Furthermore, the amount of medical data has already increased by 15 times since 2013. This kind of statistics shows excellent potential in optimization, enhancement, and automation of processes in the medical sphere. Machine learning in healthcare is based on well-trained and evolving models due to the growth of available medical data. Consequently, This leads to higher autonomy of the systems and superior healthcare consulting. 

The autonomy increases the ability of independent decision-making, which in turn facilitates operations of healthcare associates. For instance, the AI-driven system could provide better triage of patients during heavy load times. It allows less experienced staff to be involved in the decision-making process and reduces false-positive admissions without increasing patient risks.

The healthcare professional’s work can also be optimized this way, and the probability of delaying emergency medical care is minimized. The increasing amount of medical data gives us more considerable potential in ML models training, which also guarantees higher diagnostic precision in AI-powered processes.

Benefits of CV/ML application for healthcare business

The medical sphere demands diagnostic accuracy, speed of decision-making, and reliability of elements involved in making these often critical decisions. Healthcare business relies heavily on resource management. In this context, the developing AI systems can beneficially impact current hospitals’ performance. What exactly can CV/ML-based systems give to the healthcare sphere at the moment?

• Much higher reactivity in critical circumstances, prediction, and prevention of dangerous patients state during diagnosis, treatment, and surgical intervention.
  
• Better efficiency in resource management. Machine learning can be trained to look at images, identify anomalies, and point areas that need attention, improving the accuracy of all processes. In terms of optimizing the work of professionals, the system can fulfill a vast amount of work with less amount of human involvement. It is especially effective when routine manual work occurs, such as working with lab data or large image datasets: radiology, cardiology, histology, etc. A sophisticated system analyzes the data, assesses how it deviates from the norm or is close to it, and can make a fast and accurate decision before getting to a corresponding professional. Hence, this results in increased speed and precision and decreased costs. Automation of human-involved processes can also become crucial during heavy load periods, where human resources are scarce.
  
• Real-time 24/7 monitoring. Human attention level fluctuates over time. AI never gets tired as long as the machine is on. A CV/ML-based system can provide reliable hospital routine performance monitoring by ingesting the feed from multiple CCTV cameras. It may include constant supervision of patients, compliance with sanitary and quarantine standards by medical staff, analysis of visitors, etc.

Practical Computer Vision and Machine Learning applications

Optimization of the diagnostic process

Before getting the patient to the hospital, it is beneficial to be aware of their state and perform a preliminary diagnosis. Thus, the doctors could assess the complexity of the patient’s condition. Immediate diagnosis can be difficult, especially during heavy load periods. In this case, having an AI-driven system that accesses vast amounts of patient-related data allows us to evaluate the patient’s condition by all the necessary parameters to understand the priority of urgent care.

CV/ML processing can also help us collect data based on preliminary visual analysis of the patient. For instance, a stroke or neurological disease can be identified by detecting and automatically analyzing facial landmarks, even if the anomalies are barely recognizable visually.

Thus, even during the transportation of a patient, the ambulance personnel would be able to submit information to the hospital. It can be further processed by an AI ​​system, which puts a patient into a queue, sorting patients by priority. Integration of healthcare information technology gives an ability to analyze data captured from different sources (such as patient’s gender, weight, height, age, ECG, current face image, pupil size, blood test, blood pressure, pulse, body temperature), and provide a diagnosis assumption.

Ergo, when the patient is transported to a hospital, the workforce is evenly distributed, and the medical care is delivered promptly, depending on the condition complexity.

 Visual data processing

 The previously described mechanism for diagnosing and sorting patients can be used in hospital facilities as well. It can optimize the decision-making process of specialists and provide an accurate diagnosis. Automated visual data processing facilitates work with X-Ray, histology, MRI, encephalograms, radiology tests, tissue volume determination, computer surgery, dermatology or oncology diagnostics, treatment planning, and detailed visual examination of the organism.

CV/ML-based object detection and segmentation provide better diagnosis accuracy and already outperform human professionals in certain situations, making medical images more meaningful and more straightforward to analyze. 

 
We should keep in mind that the more visual information is being ingested and processed, the bigger the database is, leading to gradual CV/ML models improvement and better results in the future.

Human pose estimation

In cases where the patient needs constant 24/7 supervision due to the surgery, severe condition, or unconscious state, AI-based pose estimation can cope with this task. The system monitors the patient’s position and analyzes body movements, assessing the adequacy of the state and preventing potential injury by producing the appropriate alert for personnel when a dangerous situation is about to happen. In case of danger, such as falling of a patient, failure of blood transfusion, convulsions, the system can notify medical staff immediately.

 AI-powered CCTV system for organizational hospital processes

As we’ve already mentioned above, one of the main advantages of using machine learning and computer vision is constant monitoring of everything happening in the hospital.

For example, the system can monitor queues in corridors and notify healthcare workers about crowds or non-compliance with the social distancing guidelines. The technology also tracks waiting time when patients are delivered by ambulance to optimize the internal processes further.

In addition, during the quarantine period, it is obligatory to monitor the availability of PPE and comply with all the sanitary norms on the hospital territory (ventilation of premises, disinfection, masks, and gloves on-time replacement by medical staff, etc.)

It is equally essential to track hospital visitors, monitor the overcrowding, and store the data to track visitors’ contacts if COVID-positive people were in the hospital.

 Complete solution for machine learning in medicine

Having extensive expertise in machine learning and computer vision, our company can provide a complete, reliable, accurate, and constantly evolving ML system for automating and optimizing healthcare processes.

The system ingests data from different sources and processes it with ML and CV models. This gives us the possibility to optimize and automate diagnostic decisions, monitor patients 24/7 for preventing critical situations, produce alerts for medical personnel and facilitate resource management by automatically sorting patients based on data from constantly growing databases and just out data produced by CV/ML models.

Having access to rapidly growing big data in healthcare gives us the following benefits:

• implementation of interoperability in healthcare
• continuous enhancement of ML models, resulting in constantly increasing accuracy and reliability
• the faster, better, and more autonomous decision-making process

This automated medical diagnosis system can provide the precision healthcare system needs for resource-effective and top-quality healthcare services.

Conclusion

To conclude, AI-powered advanced healthcare has an increasingly great potential to become prevalent as modern systems and extensive machine training possibilities evolve, minimizing risks for human life and optimizing the work process.

A crucial benefit of healthcare innovation lies in the extensive field of application. Any industry which works with people or may constitute even a minimal potential threat to their physical condition can reduce it to minimal values (1-2%).

Nextgen healthcare technologies can be applied in sports to monitor the condition of athletes before essential competitions, in logistics to monitor the condition of drivers during transportation, to check the condition of pilots, extreme sports enthusiasts, and others. Keep an eye on our blog to gain more insights into the most up-to-date advancements in the healthcare field.

If you want to learn more about how AI-driven innovations in healthcare technology can benefit your business, do not hesitate to contact us!

The DeepX team can become your reliable technology partner, combining many years of experience and an individual approach to satisfy the needs of any business.