Artificial intelligence (AI) helps extract information from large amounts of data, analyze it, and use it in the best possible way. This opens up new opportunities to optimize processes in the life sciences sector, accelerate product developments, and promote innovations. This enables companies to strengthen their competitiveness and tap new potential sustainably.
The possibilities offered by AI solutions in the life sciences are diverse: Pharmaceutical companies hope to shorten the development times for new drugs significantly. They can also optimize their production processes and thus save costs. Doctors can use AI in diagnostics to improve diagnoses and initiate appropriate therapy offers for the patient earlier. In addition, AI can play a crucial role in identifying new market fields and developing research areas.
So far, many companies are still faced with the challenge of using AI correctly. The expectations are often high. But AI solutions are not off the shelf, and they cannot work miracles. Instead, the professional use of AI is a process that needs to be correctly planned and implemented. This requires careful preparatory work and a proper understanding of the existing data and its potential benefits.
First of all, it is a matter of ensuring good data quality. Even collecting the data poses some challenges since the decisive factor is not just the data collected. Above all, it is essential to understand and process the data so that the AI solution can use it correctly.
Integrate Data And Ensure Quality
Companies should first dissolve the boundaries between existing data silos. Often data is collected from a wide variety of sources and in different formats. These need to be standardized and consolidated in one place. Another essential prerequisite for the successful use of AI: The quality and integrity of the data must be guaranteed. This is the only way for the models and algorithms to access reliable data to evaluate large amounts of data. If the data quality is poor, the result will not be as expected either. The selection of the data suitable for the question is just as relevant as the cleansing of the data.
Understand And Extract Context
Data interpretation then plays an essential role in putting the data in the proper context. Only if metadata such as units and relations are added to the multitude of individual data records can they offer real added value. This turns a number into more than just a data point – it has a meaning. To do this, the metadata must be understood and extracted. Therefore, life sciences companies have to resort to technologies that can process metadata, especially from the scientific field.
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Asking The Right Questions And Choosing The Right AI Solution
Before an AI application can be used in a company, it must be clarified which questions should be answered. Because only if companies know in advance what they want to achieve can they choose the correct data and algorithms. This means that it should be carefully considered whether a data analysis with AI is necessary or whether a statistical evaluation of the information is sufficient. Only in this way can companies avoid unnecessary effort or false expectations and beneficially use AI.
To exploit the full potential of AI, it is also essential to choose the AI solution carefully. To do this, it should be selected based on the previously defined questions and the data to be used and adapted to the requirements of the company or research institution. Working with an experienced partner in the AI field helps you choose the technology and solution tailored to the needs and the respective specialist area and provides the desired answers.
In the life sciences sector, AI is one of the critical technologies of the future. It can make valuable contributions in diagnostics, product development, research innovation, clinical tests, and production.
In diagnostics, for example, AI makes it possible to detect breast cancer more precisely and is a helpful tool for doctors. The software can recognize whether a structure can be identified on the mammography that makes further diagnostics appear meaningful. Doctors can use it to improve the specificity and sensitivity of the screening and reduce false diagnoses. In a series of tests, false-positive results could be reduced by almost 6 percent. Breast cancer is thus detected more reliably, and unnecessary therapies are avoided.
A PwC study also shows that AI can help detect diseases more precisely and significantly earlier and thus reduce costs. AI can therefore reduce healthcare spending in Europe alone by a three-digit billion amount in the next ten years. To this end, PwC looked at three widespread clinical pictures that cause high costs and examined the savings potential through AI.
According to this, clinical studies show that the health data of two-year-olds can tell how high their risk of childhood obesity is. With early detection, targeted preventive measures could save around 90 billion euros over the next ten years.
In the early detection of dementia, AI enables an accuracy of 82 to 90 percent. According to the study, if the disease is detected early in preventive medical check-ups, around eight billion euros can be saved over the next ten years. In the case of breast cancer, AI enables not only early detection but also tailor-made therapy. In this way, AI can predict how a patient is likely to react to chemotherapy. This is done by linking patient data and the expected side effects of the therapy. Doctors can use it to adapt the use of the medication they need and thus curb unwanted sequelae. According to PwC, the potential savings here are estimated at 74 billion euros over the next ten years.
AI also supports natural intelligence so that scientists can focus more on the actual scientific work. For example, data from time-consuming analysis processes can be used for models, which also holds great potential for savings in the laboratory. Many tests no longer have to be carried out physically but can be carried out virtually based on existing experimental data. This means that AI can significantly increase the effectiveness in this area, too, helping to accelerate innovation and reduce research costs.
Uncover Unknown Connections
AI also has an exciting area of the application when looking at legacy data in a new way. In this way, connections can be developed today that were not possible a few years ago. For example, machine learning algorithms can detect minor deviations and abnormalities in the production line. Machine learning can thus help adapt the production process and optimize material consumption and the quality of the products.
In addition, AI can also give recommendations for action that may not have even been considered. It helps scientists to understand overarching interrelationships and to link knowledge. If further information, such as public publications, is integrated with internal company data, a comprehensive picture emerges that can reveal new possibilities. AI-based applications not only help researchers to understand individual data sets but also to build bridges to other research areas. This interdisciplinary approach enables new fields to be opened up and innovative medical products to be developed.
AI applications can already revolutionize the health sector. Diagnosis and treatment can thus be more precise and more targeted, impacting both optimal patient care and health expenditure. In addition, machine learning can enable new approaches in research and therefore make active ingredients and therapies available faster than ever before, save development and production costs or develop entirely new medical products.
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