But first, here is a brief explanation of the concepts of precision medicine and personalized medicine, which are often used as synonyms, but actually differ slightly in focus. However, they are both used to describe the following: tailored care strategies that optimize treatment and prevention to achieve the best possible outcome.
Precision medicine is about using advanced tools and data (such as genetic information, biomarkers and lifestyle factors) to understand which treatments work best for specific groups of patients. The focus is on finding the right treatment for the right group based on scientific insights.
Personalized medicine emphasizes tailoring treatment to an individual patient, taking into account the person's unique biological conditions, lifestyle and personal preferences. Unlike precision medicine, personalized medicine does not always require advanced technology.
What is precision medicine and why is AI crucial?
Precision medicine, which by analyzing, for example, a patient's tumor and identifying this genetic marker, the drug can be specifically targeted to cancer cells with this mutation. This improves treatment outcome and reduces side effects compared to standard chemotherapy. Other applications include the treatment of rare diseases and pharmacogenomics, i.e. the genetic risk of drug side effects or under-treatment. Personalized care based on genetic information makes it possible to tailor treatments based on each patient's unique genetic profile, health status, age and environmental factors.
Unlike traditional care, where a 'one-size-fits-all' approach is often applied, precision medicine aims to optimize treatment for each individual.
Recent technological developments, such as whole genome sequencing and CRISPR technology, have opened up the possibility of using precision medicine in practice and for more applications. According to research, AI can revolutionize this field by processing the vast and complex data needed to predict how a patient might respond to a particular treatment.
The US National Institutes of Health (NIH) reports that precision medicine, supported by AI, can improve the accuracy of treatments and reduce side effects by identifying relevant genetic and biomarker patterns at an early stage. However, limitations need to be addressed, for example, the analyses depend on high-quality and representative datasets. Bias in data and AI models often lacking transparency (the so-called black box problem) also need to be considered.
Challenges in the implementation of AI for precision medicine
Despite the potential of AI-based precision medicine, there are significant obstacles to overcome. One important aspect is the safe and ethical handling of patient data. For AI to become an integral part of healthcare, a robust infrastructure is required that protects the privacy of individuals while enabling the necessary data exchange between healthcare providers and research entities.
How far can and should we go in this data exchange to maximize the potential of healthcare without compromising the individual's right to privacy?
The use of unique genetic information is of course very delicate and requires great caution, not least in terms of how it could fall into the wrong hands and then be used in harmful ways, either against the person associated with the information or by other parties exploiting the data for their own gain.
Will we be able to ensure that precision medicine is accessible to all or is there a risk that only a small proportion will benefit from these new medicines? Discrimination, equality and safety are definitely challenges and important ethical issues we should keep in mind when discussing AI-based precision medicine in the future.
Balancing access to innovation and research with the protection of patients' privacy and genetic information is a challenge that requires careful balancing. International regulations such as the GDPR, AI Act, EHDS and HIPAA provide guidance, but the question is whether a universal framework can be designed to both ensure privacy and promote data sharing. The quality of the data also needs to be standardized to better validate AI models.
If one could dream freely, would it be possible to develop a common global solution that meets these requirements, or will each organization need to tailor its own approach to address this complex issue?
Translating AI insights from research to everyday clinical practice is another major challenge. Healthcare professionals need to be trained to understand and properly apply AI models and decision support. According to a World Economic Forum report, new skills and collaborations between technology developers and the healthcare sector are needed to ensure that AI has a broad impact and that the opportunities truly benefit patients.
If healthcare professionals and technology developers can develop a common understanding, we can expect breakthrough and innovative treatments in the near future.
Next steps for AI-based precision medicine - what is needed?
For AI-based precision medicine to reach its full potential, more studies and broader collaborations between technology and healthcare stakeholders and regulators are needed. A strengthened infrastructure and transparent AI models can build trust among both healthcare professionals and patients. At the same time, clear accountability frameworks are needed where healthcare providers and AI developers work together to improve the quality and outcomes of care.
An open dialogue on both the potential and the ethical issues of AI in precision medicine is an important foundation for sustainable development in the field. The future of precision medicine is exciting to say the least, but for it to be realized, technology, legislation and people need to go hand in hand throughout the healthcare process.
