Artificial intelligence (AI) in healthcare
In healthcare in particular, there is an enormous amount of relevant data that can contribute to better medical care for patients. However, an analysis of these health data is extensive and complex. Therefore, it becomes almost impossible to examine them exclusively by selected professionals (1). This is where artificial intelligence (AI) comes in. Deep, continuous analysis using AI and analytics tools can eliminate this existing discrepancy. Thus, there is an opportunity to sustainably improve services in healthcare and to advance research even further (1).
Opportunities and threats of artificial intelligence
AI-based technologies have the potential to fundamentally change the healthcare system. Stakeholders such as physicians, patients and insurance companies can benefit from these forward-looking developments. However, for successful implementation, it must be ensured that the interests of all stakeholders are represented (2). It is therefore advisable to take a closer look at the opportunities and risks arising from the use of artificial intelligence (3; 4):
Opportunities from Artificial Intelligence
- Personalized medicine
- Strengthening the rights of patients
- Support in diagnosis
- Cross-sector processes and networked treatment
- Social participation and inclusion
- Increased knowledge and access to knowledge
- Process and workflow optimization
- Support in everyday life
- Counteracting underuse
- Promoting communication between
patients - Reduction of side effects in treatment
Opportunities from Artificial Intelligence
- Erroneous and discriminatory decisions by AI systems.
- Risks to quality of care
- Ethical issues: Danger due to dependencies and increasing cost pressure in healthcare
- Data protection
This list shows that, overall, there are more opportunities than threats arising from the use of artificial intelligence. However, the main concerns to be taken into account are those relating to data security. The use of AI-based technologies is also covered by the General Data Protection Regulation (GDPR). In the case of further processing of data, for example, Art. 6 (4) DSGVO applies, which only permits further processing if the new purpose is compatible with the original purpose of the data collection (6).
What areas of application already exist for AI?
AI has long since found its way into industry. The collection and analysis of data with the help of AI applications are used, for example, for the quality assurance of goods. This allows them to be examined quickly and efficiently for possible defects or damage. AI-based analyses in energy management help predict continuous energy consumption and distribution in production facilities. As a result, both production and energy costs can be optimized. With the help of scheduled data analyses, needs-based maintenance of machines and systems is made possible, which significantly reduces the number of downtimes.
Administrative processes in areas such as sales, customer service or marketing can also be improved by AI methods (7). For example, “chatbots” are used on websites to facilitate customer service. In this way, various inquiries are answered automatically and without direct human intervention. Artificial intelligence is also of great importance for strategic marketing. Advertising measures in online marketing can be controlled for specific target groups in order to avoid wastage. On the one hand, only the relevant target group is addressed, and on the other hand, costs can be saved.
How is AI being used in medicine?
In medicine, too, AI methods are already being used in the field of health research and care. This includes diagnosis and therapy, monitoring and follow-up care of patients, and the control of medical processes. Intelligent computer programs can predict courses of disease and therapy on an individual basis. For example, genetic examinations and image data can be used to calculate the aggressiveness of tumors, which favors the planning of further treatment options. Furthermore, operations can be facilitated by AI-supported systems. For example, surgeons can obtain information during an operation using voice or gesture control. This information then appears in the eyepiece of the surgical microscope. This avoids interruptions during the procedure (8).
There are also already applications in the field of self-care that make use of AI-based technologies. The app “Lindera” uses a 3D image of gait movement to provide a mobility analysis of patients at risk of falling. Artificial intelligence is used here to digitally translate the diagnostic eye of doctors and nurses. Digitizing the analysis of risk factors and action planning is intended to prevent falls in old age, systematize care documentation, minimize costs, and ultimately relieve relatives and caregivers (9). The app “Kata” helps with the correct use of inhalation devices in order to avoid application errors and thus negative consequences for patients. Users are guided step by step through the inflation process. They also receive a reminder to inhale and an assessment of whether they have completed each step of the inhalation process correctly. Thus, AI methods help sufferers to improve the handling of all common inhalation devices (10). It can be seen that many application areas of AI already exist in medicine. In general, one can speak of a positive added value, since the healthcare system is relieved by the use of digital technologies.
What does Big Data in AI mean for the healthcare sector?
One major opportunity presented by new technologies is personalized medicine. This will enable better therapies and treatments for patients by individually tailoring certain clinical pictures to the persons to be treated. However, such a development will only be feasible with the involvement of Big Data and AI. The creation of intelligent diagnostics and treatment options requires the collection, processing as well as evaluation of existing data from as many people as possible. Particularly in the evaluation of drugs and therapies, the comparison of data from different patients plays a major role. This is because diseases often arise from a complex relationship between genetic aspects, environmental influences and lifestyle habits. Digital processes of Big Data and AI will provide deeper insights into the human blueprint through gene, protein and molecular analyses and will make it easier to understand the processes in our bodies. This will ultimately enable diseases to be detected more quickly, treated more effectively and monitored. In addition, optimized and also affordable care can be ensured at the same time (11).
Fitness Tracker und Wearables – Welche Rolle spielen die Fitness-Devices für die Zukunft?
Ist KI bereits in Digital Health angekommen?
Fitness devices such as pedometers, heart rate monitors and heart rate monitors accompany many people through their daily lives. The intelligent networking of such objects with the Internet has almost become the norm. A particularly large trend is emerging in the area of fitness & health devices. Around one third of people living in Germany use wearables to track their athletic performance or medical values. Fitness devices have the potential to contribute to health prevention. This is also shown in a study by the German Association for Information Technology, Telecommunications and New Media e.V. (Bitkom), in which 75 % of respondents stated that they would forward their health data to their doctor in the event of illness. This allows conclusions to be drawn about the patient’s condition and enables better treatment. However, the protection of the transmitted data must also be guaranteed here by the GDPR (12).
Many applications from the digital health sector are already making use of AI-based technologies. The trend is particularly evident in health apps. This is also shown by the previously mentioned examples of “Lindera” and “Kata”. However, the list of healthcare apps is much longer. Some even bear the designation Digital Health Apps (DiGA) and are thus officially part of standard patient care. DiGAs must demonstrate a medical benefit. If no clear scientific evidence of efficacy and efficiency is demonstrated after one year since introduction, the status of digital health app is dropped (13). The DiGA “somnio” helps users to optimize their sleep behavior. Through information and answers provided by a sleep diary, the app creates personalized training that helps achieve set sleep goals. The connection of a sleep tracker and “somnio” also enables automated recording of central sleep data (14). Since October 2020, the app and also others from the DiGA directory can be prescribed by physicians. This shows that AI-based technologies have long since found their way into digital healthcare.
Which startups (in healthcare) are already using AI and how?
Many start-ups in the field of digital health already offer innovative solutions based on AI-based technologies. PeakProfiling GmbH has developed a voice and sound analysis tool to help with the early detection or recognition of depression and ADHD. The AI technology used can detect emotional and physical states and find a suitable therapy. The expert team around Ada Health uses Artificial Intelligence for a health app, which enables patients to better understand their health condition. If a treatment can be considered, the app gives users a hint about it. Here, too, the focus is on disease prevention. In addition, costs in the area of healthcare are to be saved and medical professionals are to be relieved. The start-up Audatic aims to sustainably improve the quality of life of the hard of hearing and deaf. Using AI-supported deep-learning technology, audio signals are modified in such a way that unpleasant background noise can be filtered out via smartphone in conjunction with standard hearing aids.
How can AI impact YAS.life and the products in the future?
The use of artificial intelligence is also an enrichment for YAS.life – both on the part of the developers and from the perspective of the users. The use of AI-based technologies makes it possible to offer personalized recommendations within the app. However, this requires the evaluation of a large number of different data. The following list shows some examples:
- Amount of daily water intake
- Number of daily steps
- Visits to doctors
likes and dislikes based on past choices
individual challenges - Health goals
- etc.
On the part of the insurance companies, the health data obtained can be compared with the insurance premiums. This makes it possible to determine whether the number of medical consultations has decreased since the app was installed. It is also possible to draw conclusions about the motivation of the insured on the basis of app openings and activity. If the number of average steps increases, as well as the number of challenges completed, it can be concluded that there is still a willingness to use the app. Most crucially, however, if users keep themselves healthy by using the app, insurance companies and health insurers can save costs.
Sources
(7) BDVW. Künstliche Intelligenz. Anwendungsgebiete
(10) Vision Health. Digital gegen Asthma und COPD. Kata® macht Schluss mit Behandlungsfehlern
(12) Bundesministerium für Energie und Wirtschaft. de-hub. Wearables und das Internet der Dinge – Die Zukunft der smarten Gadgets.
(13) YAS.life. InsurTech Insights. Digitale Gesundheitsanwendungen
Über die Autorin
Freya
Content & Communications Managerin
Ich informiere über die neuesten Entwicklungen bei YAS.life und gebe interessante Einsichten in die Digital Health Branche. Meine Themen: Digital Health, Produkte bei YAS.life, der gesunde Lebensstil.
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