人工智能如何改变制药行业的预测

OCTOBER 2025 How AI is TransformingForecasting in Pharma The Transformative Impact ofAI on Pharma Forecasting priorities are set, and future patient access ismapped. And now, AI is redefining what that lens There is no shortage of discussion about theopportunities artificial intelligence (AI) brings tohealthcare and, specifically, the pharmaceuticalindustry. A recent report found that 95% ofpharmaceutical companies already invest in AIcapabilities. In the next 5 years the amount of AI as a Catalyst for SmarterForecasting Traditional forecasting has long relied on structureddata, historical analogs, and expert assumptions.While effective, this approach often struggles with By processing vast and varied data sources –clinical data, epidemiological trends, electronichealth records, genetic profiles, demographicshifts, and even socioeconomic indicators – AIcould generate forecasts that are not only moreaccurate but also more nuanced. Thus allowing In the next 5 years the amountof investment in AI by pharmais expected to grow by 600%to $25B, with AI’s impact onnew drug development alone But amid this broad transformation lies a quieter,equally significant revolution: the way we forecast.Forecasting in pharma is the lens through which These insights would fundamentally alter howpharmaceutical companies approach risk, •Commercial forecastsbecome sharper, helpingcompanies plan supply chains, pricing, and launch Disease Forecasting: One of the most profound impacts of AI is in diseaseforecasting. Traditional epidemiological modelsoften struggle to capture the complex interplay •Healthcare systemsbenefit from better resourceallocation, potentially reducing costs and AI doesn’t just improve thescaleof forecasting, itchanges itsscope. Imagine forecasting the progression of diabetesnot just through incidence rates but by layering invariables such as socioeconomic status, regional Shifting the Focus:Patient-Centric AI The promise of AI is not only greater accuracy butalso a fundamental reorientation toward patient- Healthcare is moving from reactive to proactive. AI-driven tools already analyze genetic predispositions,family histories, and lifestyle data to identify high-risk individuals long before symptoms manifest. This matters for more than scientific curiosity. Earlierand more accurate disease prediction has practical •R&D investment decisionscan be more preciselyaligned to areas of greatest need. For example: •Early detection of cardiovascular risk couldshrink the market for advanced therapies whileexpanding the need for preventative medications.•Genetic screening may identify rare diseasepatients earlier, growing populations that ON-DEMAND WEBINAR Catch up with our AI webinarwhere leading experts fromJ+D Forecasting shed light onthe transformative potential of The promise of AI is not onlygreater accuracy but also afundamental reorientation WATCH ON-DEMAND For pharmaceutical forecasters, this requiresmodels that can flex in real time, adaptingas diagnostics expand patient cohorts or as stratification strategies, and regulatory pathways,AI models could identify the factors most closelycorrelated with success. These insights wouldn’t just •Regulatory foresight: AI can identify risks in trialdesign that could lead to regulatory setbacks.•Competitive positioning: AI can simulate howdifferent launch strategies might perform incrowded therapy areas. BESPOKEMODEL Robust, custom-built modelsthat meet your specific brandand forecast requirements, The result is more than better predictions, it’s better FIND OUT MORE DISCOVERY NEWSLETTER Need pharma insights? Signup and get insights into thetrends and hot topics shaping Predicting Probability of Success Bringing a new drug to market is fraught with risk;scientific, regulatory, and commercial. Historically,forecasting models have been able to estimate SIGN UP AI potentially changes this. By analyzing decadesof trial data alongside molecular structures, patient Key Use Cases of AI Across the Pharma Value Chain To fully understand AI’s forecasting impact, it helps to step back and look at where AI is most visiblyreshaping pharma today: •Accelerating Drug Discovery:AI platforms can now analyze millions of compounds in silico, identifyingpromising candidates at a fraction of the cost and time. Some estimates suggest discovery timelines couldshrink by 80%, with costs reduced by up to 70%. For forecasters, this means faster pipeline expansion and •Optimizing Clinical Trials:Patient recruitment, long a bottleneck, is being transformed by AI’s ability tomatch candidates more precisely and to monitor data in real time. Failure rates can drop, timelines shorten,and trial outcomes become more predictable. Forecasting must adapt to this acceleration, modeling not Source: J+D Forecasting client survey. •Enhancing Diagnostics:AI diagnostic tools are already outperforming human clinicians in some domains,such as image recognition for cancer scre