In the medical field, understanding and managing the myriad symptoms that patients experience due to medication is a daunting task. This complexity is further compounded when considering Adverse Drug Reactions (ADRs), a spectrum of unwanted effects that vary from mild discomforts like nausea to severe, life-threatening conditions. The conventional approach to documenting and analyzing these reactions is through structured databases that categorize ADRs based on their severity and frequency. However, the challenge for healthcare professionals lies in effectively linking these symptoms to the responsible medication, especially when dealing with polypharmacy in patients.

The intricate nature of ADRs, coupled with the time constraints faced by physicians, often leads to this critical aspect of patient care being inadequately addressed. It's here that Artificial Intelligence (AI) can play a transformative role. By leveraging AI, healthcare providers can gain significant insights into the complex relationships between patient-reported symptoms, their medical history, and their medications. This technology not only assists in identifying common ADRs but is especially valuable in detecting rare and intricate drug interactions that might otherwise go unnoticed.

The presentation will focus on a research study conducted as part of a Master Thesis project at Karolinska Institutet. This study examines the efficacy of AI in uncovering rare cases of ADRs, thereby providing physicians with a powerful tool for regular and efficient drug monitoring. By harnessing AI's capabilities, the study demonstrates how technology can support healthcare professionals in making more informed decisions, ultimately enhancing patient safety and care quality.