There is no shortage of discussion about the opportunities presented by artificial intelligence and AI in the pharma industry. 50% of global healthcare companies plan to implement AI strategies by next year and AI’s impact on new drug development is expected to grow 40% annually. There is no part of the pharmaceutical ecosystem that isn’t touched by AI – or at least speculation about AI. From drug discovery to clinical trials to diagnostics – processes will be cheaper, more efficient and more effective. At least, that’s the plan.
While each of these areas carries huge opportunity in their own right, they also have the potential to impact the way in which we perform forecasting. Not only improving market sizing and peak share estimates, but also improving our understanding of disease evolution. We’ll be discussing this topic in a new webinar (you can register here), and in the meantime I wanted to share a few initial thoughts on the topic.
Disease Forecasting: A Game-Changer
Part of the potential of AI lies in its ability to predict disease progression with unprecedented accuracy. By harnessing AI algorithms, we can consider a multitude of factors—demographics, causation, environment, and socioeconomic indicators—to refine our forecasts. But it doesn’t stop there. AI-driven diagnosis promises early detection and treatment, fundamentally transforming our approach to progressive diseases. As we delve deeper into AI-powered forecasting, we’re not only improving market sizing and peak share estimation but also gaining fresh insights into disease evolution.
Shifting the Focus: Patient-Centric AI
Healthcare is on the cusp of a paradigm shift. AI enables earlier disease diagnosis, shifting our focus from late-stage treatments to proactive interventions. Imagine AI systems analysing electronic health records, genetic profiles, and demographic data to identify high-risk individuals—those prone to diabetes, cardiovascular diseases, or specific cancers. This shift necessitates adjustments to our epidemiological models, but the payoff is substantial. Pharmaceutical companies can develop targeted interventions, enhance patient outcomes, and alleviate the strain on healthcare systems.
Predicting Probability of Success
The path to launching a new drug or therapy is a long and arduous one so understanding the probability of success is key. AI can help us to better assess the factors that influence success. By training AI models to analyse vast data sets and identify the main drivers for success, we can create more accurate and reliable predictive models. Technical and regulatory success are only part of the battle. Better predictive models can also help to optimise launch strategies to maximise the chance of commercial success as well – critical, particularly in some of the more competitive therapy areas.
Join the discussion
There is much more to cover on this topic and we’ll be doing just that in the webinar, “AI and ML in Pharma: Redefining the Forecasting Landscape” on 31st July. I’ll be joined by Daniel Chancellor, VP Thought Leadership for our parent company, Norstella and Stefano Driussi, Head of Software Engineering at J+D Forecasting, and we’ll be discussing some of the practical applications of AI in pharma, specifically within forecasting. I hope to see you there!
