The power of artificial intelligence (AI) to transform healthcare and drug development, from making R&D more effective to establishing optimal treatment regimens, is starting to have an impact. But whilst there is no doubt that AI can analyse colossal amounts of data faster than humans to reveal patterns and predictions to enhance disease diagnosis, inform safety profiles, deliver more accurate clinical trials and enhance public health, the insights delivered by AI will only be as good as the data it analyses; no amount of polishing by AI tools will rectify critical gaps in information that can only be addressed at source.
Application of AI could cut the time it takes for pharmaceutical companies in the research and development of new drugs. Successful research is dependent on understanding the effect a disease has on our biological systems. In is necessary to identify molecules most likely to interact with the relevant biologic targets and alter the pathophysiology of the disease. AI is able to examine vast quantities of unstructured data, determining what is, or is not, relevant, allowing the identification of the drug that will be most effective. AI can speed up this process, ensuring that new medicines are being brought to patients quickly and effectively. However, even in the apparently obvious area of drug discovery, questions are being raised as to the true impact of AI on the identification of viable molecules for further evaluation.
In light of these benefits however the advent of AI in R&D comes with certain challenges for drug safety and pharmacovigilance. Adverse drug events are becoming a greater issue for healthcare systems as they are correlated with non-adherence, poor health outcomes and an avoidable misuse of resources. The impact of this can be profound on the product, patient and the entirety of the healthcare system. With the greater emphasis on proactive post-market surveillance, it is clear that deficiencies in data quality cannot be rectified by post-processing; data has to be obtained first at source which will maximise the value of initial contact and in turn foster much more accurate safety signal detection and evaluation.
As a consequence, there is a real risk of too much trust being put into AI and not enough scrutiny. Machine learning functions initially by humans training the software to recognise patterns in the data. Once the software has been trained it is placed in sole charge of analysing raw data. Humans must accept the answers generated by the software at face value, unable to see how it arrived at that conclusion. The issue with this, of course, is that the software is only as good as the data or the rules that it learns from. For this software to produce accurate outcomes every time, it needs to be fed reliable data and humans are imperfect. The presentation of medical conditions including adverse events is variable so that any AI tools must be transparent in the algorithms they employ and provide risk-based QC steps to enable human experts to cross-validate the decision making. Mis-learning and misinterpretation can have significant consequences for patient safety.
From a Drug Safety perspective, intelligent application of AI coupled with great data input will not only drive efficiency across Pharmacovigilance system but also foster quicker detection of new safety signals. The goal is to enable Pharmacovigilance experts to focus on issues and products that carry the greatest risk; this is where richness of content is key and digital solutions that put the patient and the capture of their experiences at the forefront of safety processes will act as a platform to further cement pharmacovigilance as an essential component of best healthcare practice.
