Our healthcare system is undergoing a digital transformation: the key word is “smart health.” Big data, 3D processes, and new diagnostic methods allow us to collect and process more data than ever. This technical progress makes it possible, among other things, to offer therapies that are much better tailored to the individual patient.
Update For Health Care: This Is What Digital Intelligent Health Technologies Are Already Doing Today
Definition: Smart Health stands for the digitization of healthcare. For example, processes from preventive and aftercare to support and care are supported or wholly taken over using digital technologies such as smartphones, software, or apps.
Artificial intelligence, digitization, and automation are finding their way into many areas of our lives. Our healthcare system is also undergoing a digital transformation: the key word is “smart health.” Big data, 3D processes, and new diagnostic methods allow us to collect and process more data than ever. This technical progress makes it possible, among other things, to offer therapies that are much better tailored to the respective patient (personalized medicine). Digitization also helps with another central topic in the healthcare sector: one way of combating the shortage of skilled workers is the increased implementation of supporting robotics in hospitals and nursing homes. For example, robots can already process patient files and help people in need of care to live independently for as long as possible. This way, the specialist staff is relieved and has more time for direct patient interactions.
AI In Healthcare
Artificial intelligence (AI), in particular, will play a key role in healthcare. It already supports physicians and hospitals by helping, among other things, to identify diseases at an early stage, make precise diagnoses, find suitable treatment methods and increase their effectiveness. For example, if a patient reports specific symptoms, an AI can make initial diagnostic suggestions to the doctor. AI is also becoming increasingly relevant in developing new drugs: Thanks to machine learning, many analytical processes in drug development can be made much more efficient, which, at best, can save years of work and vast amounts of investment money.
One hurdle that still needs to be overcome, however, is legal questions about data protection. This problem is currently preventing many decision-makers from taking appropriate measures for the introduction of AI.
AI In Prevention
One branch that is becoming increasingly important for medicine is prevention. The objective is here
- Recognize risks and previous stresses through regular check-ups, take countermeasures at an early stage if necessary and thus reduce the risk of a possible illness to a minimum.
- Diagnosing diseases early, gaining valuable time and thus significantly increasing the chances of successful treatment/therapy.
With the help of data analysis carried out by AI, it is possible to gain new insights into diseases and their spread, as well as risk factors. This way, people with an increased risk of infection can be identified more quickly. This can be done, for example, by analyzing the genetic material, certain microorganisms (microbiome), or the external appearance. People at increased risk can then be monitored, and their lifestyles adjusted accordingly.
Of course, AI, and especially the so-called “Supervised Machine Learning,” is beneficial in areas where the digitization of the diagnostic information determined by physicians is already well advanced.
For example:
- In CT scan-based detection of stroke or lung cancer
- In dermatology, specifically in the field of classification of skin lesions in skin images
- Finding specific indicators in eye images that indicate diabetic retinopathy (a disease of the retina that can lead to blindness at worst)
- When assessing the risk of a heart attack, sudden cardiac death, or another heart disease
A Practical Example From Cardiac Care
An excellent example from practice is the further development of cardiac care. Until now, there has been a lack of reliable methods in medicine for early and reliable detection of the risk of reduced blood flow in the heart using simple, non-invasive means. The traditional ECG, which has not been superseded for more than 100 years and is still used as the standard screening method, is uncomplicated and safe to use but does not offer the required reliability and precision when it comes to a precise diagnosis. A heart CT or a heart catheter examination is too time-consuming and too stressful for the patient to be carried out without concrete suspicion.
The young healthcare company Cardisio has now managed to eliminate this dilemma and to develop an “early warning system” for heart diseases based on artificial intelligence, which is easy to use and interpret, takes only a few minutes, and is also non-invasive. The new screening method is a “3D vector ECG,” in which the heart is measured in three dimensions, and the data obtained in this way is evaluated with the help of a computer algorithm. This gives physicians a precise overview of the patient’s heart attack risk within a few minutes.
Increasingly Precise Forecasts Thanks To Supervised Machine Learning
Artificial intelligence enables the algorithm to optimize the accuracy of the prediction continuously. This is done using Supervised Machine Learning, a machine learning method in which an algorithm is trained on a dataset whose target variable is already known. This way, the algorithm can learn connections and dependencies in the data that specify this target variable. An assessment of the quality of the prediction follows the training. This way, it is possible to apply the learned patterns to previously unknown data. The benefit of supervised machine learning is that the learning experience can flow into the process, enabling the creation of increasingly accurate predictions and forecasts.
Conclusion
The digitization of the health care system, or health prevention, offers enormous opportunities for doctors, nurses, and patients by improving diagnostic options, relieving the burden on nursing staff using supporting robotics, and defusing the shortage of skilled workers. Automation and digitization should not replace nursing staff and doctors with algorithms. Instead, the potential of digitization lies in developing and implementing methods that relieve people in healthcare and thus give them space again for what makes them unique and what patients need: emotions and empathy.
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