Shaping the future of healthcare: AI, Big Data and smart health in the age of innovation

The combination of artificial intelligence (AI) and healthcare will permanently change the medical landscape. In our Sphere DIGITAL HEALTH, we will give you an insight into how technologies such as AI, Big Data and innovative digitized healthcare applications interact to collect large amounts of data in the healthcare sector and improve patient treatment on this basis. The integration of these technologies represents an essential step on the way to the digitized medical technology of the future.

AI in medicine refers to the use of algorithms and machine learning to analyze medical data, identify patterns and support decision-making. Smart health, on the other hand, refers to applications that record, monitor and analyze data, often in real time and via networked devices.

The integration of artificial intelligence into healthcare is hugely important. Through the automation of processes, more precise diagnoses, and personalized treatment plans, it enables higher quality care for patients and better follow-up care after illness. This development promises improvements by optimizing the use of resources in the healthcare sector.

The areas of application are diverse and range from medical imaging and disease prevention in healthy individuals to patient monitoring and the development of intelligent healthcare systems. The innovative use of data offers new ways to support medical decisions such as the administration of medication.

This combination will drive healthcare and patient care forward: By bringing together big data and cutting-edge analytical methods, new insights can be gained that were previously inaccessible. Scientists agree that the fusion of artificial intelligence and intelligent healthcare systems has the potential to take medical care to a new level.

Topics such as eHealth, mHealth and wearables are playing an increasingly important role in promoting a data-based approach to healthcare. The integration into robotics applications could in turn enable semi-autonomous or autonomous treatments in the future.

Algorithms and machine learning support doctors and medical professionals in a variety of ways.

1. Support in diagnosis and medical imaging: Data-based systems can analyze X-rays, CT and MRI images and identify abnormalities that may be overlooked by humans. This can enable a more precise diagnosis of diseases such as cancer, heart disease and neurological disorders. Find out more in our magazine:

• Topic of the Month: Artificial Intelligence & Big Data
• Video: The artificial eye – imaging and AI
• Video: Smart software for MRI examinations

2. Role of data analytics in personalized medicine: These systems analyze large amounts of patient data, including genetic profiles, medical histories, and clinical parameters, to develop personalized medicine approaches. By identifying individual risk factors, therapies can become more effective.
3. Support for treatment planning and therapy optimization: They support doctors' decisions. This enables treatment planning and therapy monitoring that is optimized for the individual needs and reactions of patients, for example by administering suitable medication.

Examples of successful applications in diagnostics include screening tools for eye diseases such as diabetic retinopathy, decision-making systems for emergency rooms and algorithms for the early detection of skin cancer by analyzing skin lesions.

The integration into medical processes has led to an increase in diagnostic accuracy and efficiency. You can find out what this can look like in cardiology in our interview "Is it a heart attack or something else? How artificial intelligence can support diagnostics".

Early detection of diseases, personalization of treatment and continuous monitoring can have a positive impact on the course of the disease and improve the quality of life of those affected.

It plays a critical role in propagating progress in healthcare by integrating technology into care:

1. Monitoring and managing health data: Through the use of wearables, sensors and other digital devices, data such as heart rate, blood pressure and activity levels are continuously recorded and transmitted in real time. The "Sports Hub" project, for example, makes use of this principle.
2. Support for care: It supports the care and aftercare of patients by providing personalized health information and recommendations. With the help of a digitized patient file, apps, and platforms, people can monitor their own health, obtain medical advice and implement preventative measures.

3. Increasing efficiency in hospitals: It helps to increase efficiency in hospital operations by automating processes and optimizing the exchange of information and use of resources. The integration of electronic patient records, appointment scheduling software and telemedicine solutions can streamline workflows and reduce waiting times. You can see examples in our video "AI in the hospital – Possibilities and limits". You can discover more background information on this topic in the multimedia report "Into the hospital of the future: data, digitization and artificial intelligence". Read more about an exciting use case in sterile processing here.
4. Improving the care situation: It facilitates access to medical care and increases the quality of treatment. Telemedicine applications enable patients to receive medical advice in their own homes, which is particularly beneficial for people in rural areas or with limited mobility. In addition, remote monitoring enables continuous care, for example when using an EEG.
5. importance for promoting patient autonomy and self-management: It promotes patient autonomy and self-management in healthcare by giving patients more control over their data and decisions. Through access to health information, self-help tools and online communities, patients are encouraged to actively manage their own health.

The use of artificial intelligence and smart health enables improved diagnosis, more precise therapies, and personalized medical approaches. Patient care is optimized, and medical processes become more efficient. In our MEDICA DEEP DIVE online talk titled "Al diagnostics - from development to practice", we discussed this topic with researchers and users.

Increased patient centricity and the increased use of telemedicine are helping to fundamentally change healthcare and make it more accessible and efficient. However, this also requires the involvement of healthcare workers. Our interview "How can you bring health professionals and algorithms together?" sheds light on how employees can be won over for the implementation of these applications.

The automation of routine tasks, the analysis of large amounts of data and the provision of precise decision support systems optimize medical processes and improve patient care. It is never a question of machines and programs replacing human decisions – these systems only ever work in a supportive capacity. An example for this is the application of the "Transpara" software in mammography screening.

In this field, numerous potentials and further developments can be identified. Advances in personalized medicine, the development of intelligent medical devices and the integration of wearables and sensor technology are already improving healthcare and increasing patients' quality of life.

In this context, good protection of data is essential. The extensive use of data creates increased risks of data breaches. This challenge must be taken into account when using and sharing non-anonymized personal data, such as in an generator for doctor's letters or when using surgical data at multiple locations.

Measures such as implementing robust security protocols, training medical staff in the handling of sensitive data and regularly reviewing data protection guidelines are crucial to ensure the security of stored data. This is the only way to establish patient trust in technologies based on algorithm.

In addition, technological solutions are required to ensure the security of data in these applications and during data exchange. This includes the encryption of data, the implementation of access controls and authentication mechanisms as well as the regular updating of security measures. Anonymization of data is essential for research purposes.

By consistently complying with data protection regulations and using appropriate technological solutions, healthcare facilities can ensure that the use of artificial intelligence and smart health is in line with the applicable data protection guidelines and that the security of sensitive data is guaranteed.