Publication date: December 02, 2025
Algorithms can be used in machine learning. A medical algorithm can help and guide the user on how to treat their patient and predict what condition they might potentially suffer from. This allows the system to provide health analysis based on the data provided. It is crucial to notice that algorithms can bring in more accuracy into medical diagnosis. This data can help a doctor to make recommendations on future steps in their patients’ treatment.
Machine learning that uses algorithms can be classified as medical devices. Those medical devices can be used for:
Algorithms can also improve the functioning of:
Therefore, it is clear to notice that algorithms can have a broad usage across different areas of healthcare. In simple terms, algorithms can be used to help the doctor to make diagnosis and pick out the best possible treatment for their patient. The doctor should put in the data they have on a patient such as their age, gender, and medical results in the algorithm. The doctor is then provided with information about other patients that had the same diagnosis, that were the same age, same gender and had the same genetic markers. This can allow to compare and decide on how to treat the patient, as they are provided with the medical history and outcomes different treatments had on patients with similar characteristics. It allows the doctor to make the best possible decision for further treatment of their patient. The medical algorithm does not make the decision for him, but it helps him make a decision.
Digital health devices with the use of algorithm can help to regulate contraception by the analysis of menstrual cycle. They can detect incorrect heart rhythm, large vessels that can lead to strokes, therefore algorithms are crucial in healthcare. However, healthcare appointments can also be made through those online apps.
Algorithms have several advantages such as:
This is the use of algorithms that create decisions and predictions. They do not give insights into how they work. This makes it difficult to understand how those conclusions were made. The explanation of how the prediction was made stays unknown to the user. Those models are produced directly from data. White box is its opposite. It can be easily analysed by the user. Therefore, it is a big problem faced in healthcare, as health is a field of study that should only allow accurate predictions. Black Box Algorithms disable the user from being able to make diagnosis and choose treatments accurately, as they don’t have any information that is backing up their form of treatment. This is a key problem faced in the medical field when it comes to algorithms. There is also a need to highlight that there is no actual requirement from the IVDR and MDR regulations, or from other standards that machine learning must be interpretable by a human. Therefore, they are consistent with regulations.
Manufacturers must make sure that their medical devices are developed accordingly to the legal regulations. The two key regulations are the IVDR and MDR in the United States, that make sure that general safety is reached. Clinical and performance evaluation needs to be reached. Some of the machine learning algorithms constantly retrain using streaming data. They are still obliged to comply with the regulations that are required.
Can algorithm be a type of software?
First of all, we should identify what software is. Software is used for data processing in a certain way. The outcome of it gives a certain effect. The algorithm allows a system to give analysis on the data provided. We can therefore notice that the outcomes of both software and algorithm are the same.
Regulations regarding medical products in the European Union:
TUV SUD is an organisation that controls products in terms of their safety. They have come out with something called a „White Book”. This gives guidelines about the opportunities and challenges that are faced by manufacturers that produce medical products that use algorithms.
The „ White Book” outlines all the different opportunities that algorithms give in the medical field. Algorithms can increase the quality of medical treatments and the reduction of costs, as the use of algorithms can process huge amounts of data. This can lead to a better adaptability to new conditions, and regulate how it works in the present time, as it goes.
IG-NB checklist
The German Notified Bodies Alliance (IG-NB) is an Alliance of notified bodies for medical devices. The IG-NB, with the help of TUV SUD produced a list on requirements in the „ White Book”.
Some of the requirements involve:
| Management of data The requirements that need to be reached when rating data used to evaluate algorithms AI. |
| Product development This relates to the tests, rating of efficiency etc. of the software |
| Produce of product This outlines all the criteria of production & distribution of medical produce that use algorithms. |
| Aim This helps to evaluate if the use of technology will give more benefits to healthcare, than non-technological choices would. |
| Software requirements How efficient and functional algorithms are that are being used in AI. |
| Model of AI algorithms The guidelines talk about the ability to receive the same algorithm test results, the influence of small changes in algorithms etc. |