AI and Clinical Development Plans: Transforming the Clinical Trials Landscape

Executive Summary

Advances in artificial intelligence (AI) and machine learning (ML) are fundamentally transforming how pharmaceutical companies plan and execute clinical development plans (CDPs). Over the past decade, clinical trials have become longer, more complex, and increasingly expensive (^[1]) (^[2]). AI technologies—from predictive modeling and natural language processing to digital twins and generative algorithms—offer the promise of streamlining every stage of the clinical development process. By optimizing trial design, speeding patient recruitment, enhancing data analysis, and improving operational efficiency, AI is helping sponsors increase the probability of trial success while reducing duration and cost (^[3]) (^[4]).

Key findings of this report include:

• Clinical trial bottlenecks: Traditional CDPs often suffer from fixed, rigid designs, delayed timelines, and high failure rates (only ~11% of Phase I trials become approved products (^[1])). Rising protocol complexity is correlated with significantly longer trials (^[2]).
• AI in protocol design: AI-driven simulations and trial modeling allow sponsors to test “what-if” scenarios in silico, optimizing inclusion criteria, dosing, and sample size before a study begins (^[5]) (^[4]). Research prototypes (e.g. ClinicalReTrial (^[5])) have shown modest improvements in predicted success probability by iteratively refining protocols.
• Patient recruitment and retention: Advanced NLP and ML tools can sift through electronic health records (EHR) and unstructured data to match patients to trials much faster than manual methods. Case studies show e.g., AI screening tools cut patient screening times by roughly a third (^[6]), and dedicated startups have raised hundreds of millions to tackle recruitment bottlenecks (^[7]).
• Digital technologies: AI enables new approaches like synthetic control arms and digital twins. In-silico trial simulations and digital patient models can augment or even replace certain control groups, potentially reducing the number of human participants needed and accelerating research (^[8]) (^[9]). Large-scale efforts (e.g. simulating the SPRINT and ACCORD blood pressure trials with GANs (^[9])) demonstrate digital twins’ potential for translating trial findings between populations.
• Operational efficiency: AI automates many administrative tasks—such as data cleaning, adverse event reporting, and regulatory submissions—speeding them up by up to 50% in some reports (^[10]). For example, the FDA itself is testing an AI assistant (named “Elsa”) to summarize adverse event data and review protocols, reportedly turning multi-day tasks into minutes (^[11]) (^[12]).
• Case studies and industry insights: Leading organizations are already applying AI. Formation Bio uses AI to halve trial timelines in practice (^[13]). AstraZeneca ran parallel trials using both human and AI adjudication for heart endpoints, finding AI matches expert performance and saves months (^[14]). Sanofi deployed digital twins in an asthma trial, cutting 6 months from the timeline and saving millions (^[15]).
• Challenges and caution: Industry experts emphasize that AI is not a panacea. Data quality, bias, regulatory uncertainty, and the need for human oversight remain major challenges (^[4]) (^[16]). Successful implementation often requires reengineering processes around the technology (^[17]) and addressing trust: for instance, AstraZeneca notes that 80% of trials fail enrollment targets (^[18]) and that many staff initially distrust AI prompts (^[16]).
• Regulatory evolution: Regulators are beginning to adapt. The FDA has issued draft guidance on AI in drug development (emphasizing risk-based validation, transparency, and continuous monitoring (^[19])), and has piloted tools like Elsa to help reviewers (^[11]). Global agencies (FDA, EMA, HHS) are discussing frameworks for trustworthy AI in trials, though formal policies are still emerging.
• Future outlook: The consensus is that AI will continue to accelerate its role in clinical development. Experts predict widespread use of large-language models for summarization, more programmatic trial design, personalized medicine approaches, and integration of real-world data to refine CDPs (^[20]) (^[21]). As one industry thought-leader put it, AI is poised to be a “force multiplier” for clinical stakeholders (^[22]), allowing humans to focus on strategic decisions while machines handle pattern recognition and routine tasks.

This report covers the historical context and motivations for AI in drug development, specific AI applications across the trial lifecycle, analyses of outcomes and case examples, and discussions on regulatory, ethical, and practical considerations. It provides data-driven analysis, expert perspectives, and forecasts on how AI is reshaping how sponsors design and execute clinical development plans. All key assertions are backed by contemporary sources, including academic studies, industry analyses, and news reports.

Introduction and Background

Drug development is famously slow and expensive: bringing a new therapy to market typically takes over a decade and can cost upward of $2.6 billion (^[1]). Despite advances in science, the number of new drugs approved each year has hovered around 50 for decades (^[23]). The conventional clinical development plan (CDP)—the strategic roadmap of clinical trials, patient populations, endpoints, and regulatory steps for a new investigational drug—has become a major bottleneck. Clinical trials (Phase I-III) consume most of the time and money in R&D, with long lead times, high failure rates (only ~11% of Phase I candidates succeed (^[1])), and operational challenges (e.g. patient recruitment is often cited as ~40% of trial cost (^[1])). A recent analysis of more than 16,000 industry-sponsored trials found that increased protocol complexity has significantly extended trial durations: every 10-point rise in complexity was associated with a 33–36% longer timeline (^[2]). These inefficiencies hurt patients (delayed access to therapies) and companies (massive sunk cost).

In parallel, the past decade has seen a dramatic ascendancy of artificial intelligence and machine learning in many fields. Progress in deep learning, natural language processing, and big-data analytics has revolutionized industries from finance to transportation. In healthcare R&D, early AI efforts focused on drug discovery (identifying candidate compounds) and diagnostics, with high-profile successes in predicting molecular structures and in image analysis. However, by the mid-2020s, many experts recognized that the bigger gains might lie not at discovery but in development—specifically, in planning and executing trials more efficiently (^[23]). Ben Liu, CEO of AI biotech Formation Bio, observes that while AI speeds discovery, the real bottleneck is running trials (^[23]). Indeed, despite more AI in pipelines, annual FDA approvals have not surged – indicating that improving the trial stage may be necessary to fully capitalize on AI’s potential.

In recent years, especially since the COVID-19 pandemic accelerated remote and adaptive trial methods, the pharmaceutical industry has increasingly piloted AI and data-driven tools in clinical operations. Early proof-of-concept successes have spurred optimism but also revealed challenges. Vendors, startups, and technology divisions of large pharma have explored dozens of AI applications: predictive models for patient eligibility, algorithmic trial simulation, NLP-driven protocol writing, wearables for monitoring, image analysis for endpoints, and more. Meanwhile, regulators (FDA, EMA, etc.) have begun to issue guidance on “model-informed drug development” and AI, making clear that digital evidence and simulations can be part of submissions if properly validated (^[24]).

This trend of “AI meets clinical trials” is reshaping Clinical Development Plans on multiple fronts. A modern CDP is no longer solely a static sequence of studies; it’s becoming a dynamic, data-driven strategy. Teams can now simulate multiple development pathways, forecast risks, and pivot designs in silico well before enrolling a single patient (^[25]) (^[26]). AI is also altering operations: administrative tasks like data cleaning, regulatory writing, and safety monitoring can be partly automated, freeing scientists to focus on high-level decisions. At the same time, using AI raises new questions: How do we train models on sensitive patient data? How do we ensure AI-driven decisions are explainable and unbiased? What regulatory frameworks will govern AI-derived findings?

This research report surveys how AI is changing the planning and execution of clinical development. We first provide context on traditional CDPs and AI tech in pharma, then delve deeply into specific applications categorized by trial phase and function (design, recruitment, monitoring, analysis). Throughout, we use data, studies, and case examples to illustrate the impact of AI interventions. Later sections discuss the implications for costs, timelines, patient outcomes, and regulatory oversight. Finally, we synthesize expert opinions and forecasts on future directions. Every claim is supported by contemporary sources, including peer-reviewed articles, industry analyses, and newsworthy case studies.

Technical Background: From Traditional CDPs to AI-Integrated Planning

Traditional Clinical Development Plans: Concept and Challenges

A clinical development plan (CDP) is a comprehensive strategy that outlines the sequence, design, and objectives of all clinical trials needed to bring a new drug to market. It typically starts with first-in-human Phase I trials (safety, dosage), followed by Phase II (efficacy in target disease), Phase III (large-scale trials for statistical confirmation of benefit), and sometimes extends to Phase IV (postmarketing). A CDP specifies targeted patient populations, endpoints (outcomes measured), comparator arms (placebo or existing therapy), and regulatory priorities (e.g. special designations, submission timelines). Traditionally, crafting a CDP is a manual process based on scientific rationale, historical precedents, expert judgment, and business considerations.

Over the last two decades, however, the complexity and cost of trials have ballooned. Sophisticated biologic drugs, combination therapies, and precision medicine have fostered complex protocols with numerous inclusion/exclusion criteria, multiple endpoints, and complicated monitoring. Data from over 16,000 trials show that average protocol complexity has risen by over 10 percentage points in the past ten years (^[2]). Each 10-point bump in complexity correlates to a roughly one-third longer trial duration (^[2]), and trials now routinely exceed their timelines and budgets. Some well-known figures illustrate the stakes: per-drug development costs can be upwards of $2.6 billion (^[1]), and only about 1 in 9 drugs entering human trials ultimately gain approval (^[1]).

Key challenges in traditional CDPs include:

• Rigid design assumptions: Most CDPs assume fixed sample sizes, fixed sites, and static protocols. If enrollment lags, or an interim analysis suggests futility, making adaptive changes requires bureaucratic amendments or abandonment.
• Recruitment bottlenecks: Identifying eligible patients manually (via physician referrals or registries) is slow. It is estimated that 40–60% of trials experience recruitment delays (^[1]) (^[27]), with 80% of trials failing to meet original enrollment targets (^[18]). This delays trials by months or years and forces costly protocol modifications.
• Fragmented data and insight: Historically, data on patient populations, site performance, and past trial outcomes have been siloed. CDP planning often relies on memory or high-level feasibility surveys. Sponsors lack granular predictive tools to forecast, for example, which sites will enroll fastest or which exclusion criteria unduly narrow the patient pool.
• Time-consuming administrative tasks: Manual tasks like drafting and reviewing protocols, submitting regulatory filings, monitoring safety, and cleaning data consume enormous labor. An executive from Formation Bio estimates that if AI can replace rote knowledge work, a trial that historically required 100,000 person-hours might be done with only 100 people and machines handling the rest (^[28]).
• Regulatory caution and conservatism: Regulatory agencies prioritize patient safety and data integrity. New approaches (like external control arms or digitized endpoints) are often unfamiliar, making sponsors wary of unproven designs that might face extra scrutiny or delays.

Because a CDP is set early, any flaws in planning can propagate through the program. Unexpected complications often lead to multiple amendments (each amendment can add months to a trial) or abandoning entire study parts. Collectively, these pain points have led the industry to seek transformative improvements.

The Promise of AI and Data-Driven Solutions

Artificial intelligence (AI) and machine learning (ML) comprise a suite of computational techniques that can learn from data and make predictions or decisions. In the context of clinical development planning, AI promises to provide “intuition at scale” — the ability to identify patterns and optimize strategies using vast historical datasets and real-time inputs. Major categories of AI relevant to CDPs include:

• Predictive modeling / Machine Learning: Algorithms that can predict clinical trial outcomes (probability of success, enrollment pace, budgeting) from historical trial data (^[5]). For example, ML models can estimate the likelihood that a given compound in a given indication will succeed in later trials, or predict dropout rates for certain patient groups.
• Natural Language Processing (NLP): AI can process and analyze unstructured text (scientific literature, medical records, past protocols). NLP tools can assist in writing or reviewing protocols, extracting eligibility criteria, and translating broad clinical language into machine-readable formats (^[29]) (^[19]).
• Generative AI / Large Language Models (LLMs): The latest generation of AI (like GPT-4, Claude) can draft documents, answer complex queries, and simulate conversations. Used correctly, they could draft trial narratives, synthesize medical knowledge, or even engage with patients via chatbots.
• Optimization and Reinforcement Learning: These AI methods can explore thousands of trial design options (combinations of sample sizes, interim analyses, dosing schedules) to find the best configuration under constraints. Reinforcement learning, for example, can iteratively adjust a protocol to maximize success probability (^[5]).
• Computer Vision / Imaging AI: For trials involving imaging or pathology endpoints, AI can automate reading scans or biopsies (^[14]) (^[30]). This not only reduces reader workload but can standardize endpoint assessment across sites.
• Digital Twins & In Silico Patients: AI-driven simulation models that create virtual replicas of patient populations or entire trials (^[31]) (^[9]). These “digital twins” allow sponsors to simulate trial outcomes in silico under different assumptions.
• Real-World Data (RWD) Integration: AI techniques can mine electronic health records, insurance claims, and patient registries to inform protocol design (e.g. by understanding comorbidities prevalence) and to create external control arms (^[8]).

The explosion of data in healthcare (genomics, electronic records, wearable sensors, historical trial results) provides rich fuel for AI. At the same time, cloud computing and open-source AI frameworks have lowered the barrier to entry. Consequently, multiple vendors and pharma organizations have launched AI platforms specifically for clinical development. However, while the potential is vast, practical deployment requires overcoming significant hurdles around data quality, privacy, technical integration, and trust.

The sections below chronicle how AI tools are being applied across the clinical development spectrum, from trial conception to execution, governance, and analysis. Where possible, we quantify improvements and cite real-world examples. Given the multi-stakeholder nature of clinical trials, we also highlight perspectives from clinical operations, data science, regulatory and patient advocacy.

AI in Clinical Development Strategy and Design

Strategic Asset and Indication Selection

Before designing individual trials, sponsors make high-level decisions about which indications (diseases) to target and in what sequence. AI can aid in these strategic portfolio decisions by analyzing preclinical data, scientific literature, and market factors. Machine learning models can predict clinical potential for different targets based on biological pathways and historical success rates. For example, AI algorithms have been used in drug repurposing: analyzing molecular structures and disease networks to identify additional indications for an existing drug. Although much AI discussion focuses on bench-to-clinic (target discovery), the same principles can apply to CDPs by helping decide, say, whether to test a drug first in a rare indication or a broader population.

In practice, such AI-driven strategic planning is emerging. Consulting firms and startups have developed tools to input a drug candidate’s profile and output a recommended development sequence. These tools often incorporate business analytics as well, optimizing for time-to-market and return-on-investment across multiple candidates in a pipeline. While vendor claims abound, publicly available quantitative evidence is limited. However, industry webinars (e.g. IQVIA) emphasize that AI can “objectively assess assets” on technical, regulatory, and commercial success dimensions (^[32]). Companies are building predictive models that score each indication for likelihood of success, which can feed into CDP planning.

The effect on CDPs is that sponsors can use AI to simulate various “development pathways” upfront. Instead of relying on gut feel or linear progression, teams may use AI to evaluate, for instance, whether it’s better to pursue an accelerated path in a high-need population or a more conservative route starting with healthy volunteer studies. This aligns with the concept of real options analysis, where a development plan is treated like financial investments with branching possibilities; AI can help quantify the value of those options.

AI-Enhanced Protocol Design and Trial Simulation

One of the most transformative uses of AI in CDPs is in trial design itself. Traditional design relies heavily on expert heuristics and limited simulations. In contrast, AI-driven platforms allow sponsors to simulate entire trials in silico using historical and real-world data. This can optimize eligibility criteria, dosing regimens, sample size, and interim analyses before any patient is enrolled.

A concrete example of this capability is the ClinicalReTrial framework (Xing et al., 2026) (^[5]). In this research prototype, AI agents iteratively analyze a candidate trial protocol, diagnose potential flaws, and suggest modifications to improve success probability. The tool uses a predictive model as a “world simulator”: given a protocol, it predicts trial outcomes and then seeks changes (e.g. relaxing an exclusion criterion, adjusting follow-up time) that maximize an overall reward (success chance). In tests, ClinicalReTrial improved 83.3% of protocols with an average success probability increase of 5.7% (^[5]). Importantly, many of its “redesign” suggestions aligned with actual amendments companies had made in real trials. While still experimental, this illustrates how closed-loop AI optimization can refine trial designs beyond human intuition.

Linked data-driven approaches also target protocol feasibility. For instance, Salama et al. developed AICO (AI for Cohort Optimization) to automatically analyze and broaden trial eligibility criteria (^[33]). This system applies NLP to learn common criteria from existing trials and then tests proposed criteria against real-world patient datasets to ensure enough patients qualify. Their breast cancer case study showed that AI could identify ways to enlarge the eligible patient pool without sacrificing trial integrity (^[33]). This leads to more generalizable trials and addresses the diversity concerns often raised by regulators.

More recently, language models have been employed to aid protocol drafting. For example, AutoTrial uses instruction-based prompting in GPT-like models to generate inclusion/exclusion criteria (^[34]). The system was trained on 70,000+ past clinical trials and can produce fluent, relevant eligibility text for a new trial concept. Human evaluations found that its outputs were coherent and captured key clinical concepts about 60% of the time (outperforming a pure GPT-3.5 baseline) (^[35]). This indicates that carefully guided LLMs could serve as assistants in creating first drafts of protocols or amendments, accelerating the CDP planning phase.

AI can also recommend protocol parameters. In industry, Starmind (now part of a European consortium) and others claim to analyze past trial data to suggest optimal patient group sizes or dosing. For instance, one cited figure is that AI can reduce required sample size by factoring in predictive biomarkers and patient heterogeneity (^[4]). Although specific vendor claims lack public benchmarks, the underlying idea is that an AI can identify informative subpopulations and allow smaller, targeted trials.

Finally, adaptive designs—where trial parameters change based on interim data—are natural candidates for AI. While adaptive methods existed in statistics for some time, AI enhances them by enabling continuous adaptation guided by real-time processing. For example, an AI model might analyze accumulating outcomes and adjust randomization probabilities (more patients to promising arms) in real time. Such AI-guided adaptation requires robust simulations at the design stage to pre-specify adaptation rules—the kind of simulation that AI can perform exhaustively before a trial opens. AstraZeneca’s recent experiments with AI-assisted endpoints (described later) are conceptually similar: running both human and AI processes in parallel to validate adaptive logic (^[14]).

Key Insight: AI enhances trial design by enabling pre-trial simulations and optimizations. Rather than static plans, sponsors can test thousands of virtual trial variants against historical data to pick designs with maximal efficiency and success odds (^[5]) (^[4]). This has shown concrete benefits (e.g. digital twin simulations by Sanofi cut a trial phase by 6 months (^[15])) and is likely to become standard in sophisticated CDPs.

AI in Recruiting and Patient Enrollment

One of the most concrete ways AI is affecting ongoing CDPs is in patient recruitment and enrollment—often the slowest part of a trial. Delays here directly extend development timelines. AI-driven recruitment aims to match patients to trials faster and more inclusively.

Modern recruitment platforms use machine learning on large health datasets to find eligible patients. For instance, Deep 6 AI (acquired by Tempus in 2021) integrates NLP to scan millions of clinical records, identifying candidates who meet complex criteria. BiopharmaTrend reports that AI tools like Deep 6 have reduced screening times by about 34% (^[6]). Similarly, startups like TrialX and Antidote run online “match databases” where patients input their health data and AI matches them to trials. These tended to increase enrollment efficiency, though formal peer-reviewed metrics are sparse.

Large investments also underscore the importance: Paradigm (formerly Deep Lens), a startup focused on recruitment tech, raised $203M in early 2023, backed by big pharmaceutical investors (^[7]). Major companies are partnering with such platforms. For example, GSK signed a four-year agreement with Medable (a decentralized trial platform) to improve patient onboarding (^[36]). Such deals suggest pharma is serious about offloading recruitment to AI-enabled infrastructure.

The impact of AI on enrollment is both practical and cultural. On the practical side, AI can help sponsors achieve enrollment targets on time. With AI suggestions on site feasibility and patient pools, study teams can pre-select sites with high recruiting potential (e.g. clinics serving more patients with the target condition). Merck KGaA reports that their Operational Design Unit uses AI to “holistically analyze available data and recommend opportunity sites” beyond the obvious top sites (^[37]). This use of external model suggests something like combining demographic, historical performance, and local health data to score sites.

From the patient perspective, AI-powered outreach can improve awareness and engagement. Chatbots or mobile apps can pre-qualify patients and answer questions (triage eligibility). They can also send reminders to enrolled patients, improving retention. While quantitative evidence is limited, these approaches are being piloted (for example, companies like AiCure use smartphone apps to remotely confirm dosing adherence).

Table 1. AI Applications in Patient Recruitment and Enrollment

Sources: Industry reports (^[6]) (^[37]).

Reducing screen failure (the number of patients who are pre-screened but ultimately excluded) has clear cost benefits. BiopharmaTrend notes that AI tools generally cut recruitment costs by about 20% (^[3]). Even if only partially achieved, these savings are significant given recruitment can comprise ~40% of total trial budget. For sponsors, getting an extra month of enrollment leverage can directly convert to earlier market launch.

Potential and Limitations: AI excels when rich data are available—hospitals and healthcare systems with integrated EHRs can feed trial-matching algorithms. However, privacy laws (HIPAA, GDPR) and data siloing remain hurdles. Patients in underrepresented or underserved communities may still be missed by purely data-driven methods, a point raised by experts advocating a blend of AI and community outreach (^[38]). For example, Walgreens' clinical trials officer predicts a dual strategy: use AI to identify patients rapidly, AND use community engagement to convert them (^[38]). This recognizes that technology alone can spot candidates but trust-building and equity require human-led outreach.

On balance, AI is accelerating the enrollment phase of many trials. Its biggest documented wins are in initial screening throughput (fewer patient-hours spent per recruited subject). There is also evidence AI can help diversify trials: by scanning larger patient pools, it may surface eligible patients in rural or minority communities previously overlooked. Nonetheless, trials rarely rely exclusively on AI; rather, AI augments traditional recruitment teams, enabling them to hit targets faster (^[18]) (^[38]).

AI in Trial Operations and Monitoring

Beyond design and recruitment, AI is increasingly embedded in trial conduct and monitoring. Once a trial is underway, dozens of data streams (EHR entries, lab results, wearable sensors, patient-reported outcomes) are collected. AI tools can streamline these processes:

• Risk-Based Monitoring: Regulatory agencies encourage sponsors to focus monitoring on high-risk data points. AI can flag anomalies in data capture in real-time. For example, Saama Technologies offers AI-based monitoring platforms that improved data quality by >40% in some trials (^[39]). The FDA’s new Elsa tool can perform automated checks on data feeds to identify missing or inconsistent entries (^[11]).
• Adverse Event Detection: Machine learning models sift through patient notes and structured data to identify potential adverse events more quickly than manual review. This can accelerate safety signal detection. FDA guidance emphasizes summarizing adverse events efficiently as one use of AI assistants (^[11]).
• Patient Monitoring: Wearable devices (smartphones, ECG patches, continuous glucose monitors) generate massive data. AI algorithms interpret these high-frequency data to detect arrhythmias, sleep issues, or activity patterns. Cheaper remote monitoring reduces site visits in decentralized trials (discussed below).
• Protocol Compliance: NLP-based QA systems can scan follow-up calls or site visit transcripts to ensure protocol procedures (e.g. informed consent steps) are followed correctly, alerting central monitors if deviations occur.
• Endpoint Adjudication: Trials often have blinded committees that adjudicate clinical events (e.g. did a hospitalization count as a heart failure event?). AstraZeneca, for instance, ran an AI-assisted adjudication process for cardiovascular endpoints. They deployed AI algorithms on imaging and data in parallel with human cardiologists. The result: AI met the experts’ threshold for accuracy and “accelerate [d] timelines significantly” by extracting endpoints without waiting for all data reconciliation (^[14]). The reported gains were on the order of “months” saved per study (^[14]). This hybrid human/AI approach also helps build trust, since human oversight remains in parallel.

AI is also used in trial logistics and documentation: chatbots can answer site queries about protocol details, automated coding tools can classify concomitant medications, and algorithms can auto-generate sections of regulatory submissions (e.g. patient narrative summaries). For example, clinical operations teams are beginning to use generative AI to draft routine reports and data listings, freeing up medical writers for higher-level writing. One panelist noted broad use of ChatGPT by employees at AstraZeneca for tasks from generating slides to outlining trial designs (^[40]).

Decentralized trials (DCTs) are a natural vehicle for AI: in DCTs, patients participate from home or local clinics. AI helps integrate remote data and maintain patient engagement. For example, Medable’s DCT platform (backed by AI analytics) claims 200% faster enrollment and 50% cost reduction across its studies (^[41]). While the exact contribution of AI versus other factors is hard to isolate, AI-driven virtual patient matching and automated data management undoubtedly support DCT efficiency.

Operational efficiency gains: Industry reports coalesce around a narrative of large percentage improvements. The BiopharmaTrend analysis summarized that AI can:

• Reduce overall trial durations by up to 50% (through faster admin tasks and optimized design) (^[3]).
• Cut monitoring/safety cycle times by ~20–50% (by automating detection and reporting) (^[3]) (^[10]).
• Increase data consistency: Saama’s AI reportedly improved data quality by 40% (^[39]).
• Shorten regulatory/administrative workload by ~50% (through auto-generation of documents and summaries) (^[10]).

Such figures, drawn from vendor or media claims, should be taken with context. The exact benefit depends on how extensively AI is deployed. Nevertheless, clinical operations teams routinely report that AI tools have saved weeks in data cleaning and thousands of manual hours in review processes. Notably, the FDA’s Elsa assistant is a compelling illustration: the agency’s own deputy director claims it can do in minutes what used to take days (^[12]). Since regulatory review is often a rate-limiting step, this indicates a paradigm shift in how CDPs can progress.

Summary: In the conduct phase of trials, AI serves as an accelerant and watchdog. By automating routine monitoring and enabling real-time insights, it makes trials leaner and safer. It also supports the emerging DCT model by handling large remote datasets. While human oversight remains crucial, many site and data management tasks are now shared by AI, contributing to the 20-50% efficiency gains cited by industry (^[10]).

AI in Data Analysis and Endpoints

Clinical data analysis, from database lock through regulatory reporting, is another frontier for AI. Once a trial has collected its data, statisticians and physicians traditionally perform complex analyses (e.g. survival curves, subgroup evaluations, imaging readings). AI is being explored to enhance and expedite these tasks.

• Statistical Modeling and Prediction: ML models can augment traditional statistics by predicting missing outcomes or inferring latent patterns. For example, AI-imputation methods can handle missing visits (especially important in trials disrupted by COVID-19) more robustly than simple methods. While conventional biostatistics remains central, AI can complement it by, say, building predictive covariate models that improve power or by automating certain pre-specified analyses.
• Biomarker Discovery and Surrogate Endpoints: AI is well-suited to mining high-dimensional biological data (genomics, proteomics) for novel biomarkers. If new predictive biomarkers are identified, trials can incorporate them as endpoints or stratification factors. Initiatives like Lantern Pharma’s RADR platform use ML on billions of data points to identify responders (^[42]); trials using such insights could see higher effect sizes, effectively boosting success rates.
• Medical Imaging Analysis: AI for image interpretation is a fast-growing field. Use cases range from radiographic assessment (CT, MRI) to pathology slides. By training on annotated images, AI can detect subtle changes (tumor shrinkage, lung lesions) at scale. This can reduce variability from human readers. The recent MASAI trial (prospective breast cancer screening) is a landmark example where AI “spotted more cancers earlier” than traditional reading (^[30]). In CDPs, similar approaches allow automated endpoints (e.g. detecting tumor responses or lesions) to be factored into trial design, potentially making studies more sensitive and smaller in sample size.
• Patient-Reported Outcomes (PROs): NLP models can analyze patient diary entries or interview transcripts to quantify symptoms and quality-of-life measures. This can provide richer endpoints or safety signals.
• Synthetic Control Arms and External Data: Perhaps most radically, AI enables synthetic control arms. Instead of randomizing patients to placebo or standard-of-care, sponsors can use historical data or simulated patients as the control. AI models trained on large patient registries or prior trials can generate a virtual placebo group against which the treatment arm is compared. This approach is under active exploration, especially in rare diseases where enrolling placebo patients is ethically or logistically hard. Major regulators (FDA, EMA) have begun considering how to accept such external controls. Turf news sources emphasize that using AI-simulated controls could accelerate development and reduce costs (^[8]), though rigorous validation (using real-world evidence) is required.

The net effect of AI in analysis is to potentially increase the yield of each trial. By extracting more signal from the data (through advanced algorithms) and possibly reducing the number of patients needed for statistical significance, AI can make CDPs more efficient. The systematic review by Askin et al. notes that AI applications aim to “create efficiencies across CT activities,” including “reducing sample sizes” and enabling “faster, more optimized adaptive CTs” (^[4]). However, the review cautions that these areas are still emerging and often focused on oncology trials.

Evidence and Data Analysis

Quantifying the precise impact of AI on clinical development is challenging—few public case studies provide hard numbers. Nevertheless, industry reports and emerging studies give indicative data:

• Trial Duration and Costs: BiopharmaTrend reports that AI can shorten trial durations by “as much as 50%” (^[3]). Although likely referring to administrative components rather than actual patient follow-up time, even halving protocol preparation or data lock phases hugely affects CDPs. The same report notes 20% lower recruitment costs and 34% faster screening times with AI tools (^[3]) (^[6]). Formation Bio claims to “save as much as 50% of the time of a trial” by focusing AI on administrative tasks (^[43]).
• Enrollment Success: Clinical trials data show that large trials often face enrollment extension. AstraZeneca’s Piotr Maslak has highlighted that 80% of trials miss enrollment targets (^[18]), illustrating the scale of the problem AI addresses. In specific programs, companies report targeted successes: e.g., concurrent machine-learning-enhanced site selection at Merck KGaA reportedly identified new productive sites not found by conventional methods (^[37]).
• Protocol Amendments Reduced: Industry anecdotes suggest AI design can reduce the number of major amendments. For example, Sanofi’s digital twin in an asthma trial avoided a planned extra Phase 2 cohort, directly illustrating how smarter initial design averted a costly amendment (^[15]).
• Success Rates: While not yet demonstrated in public studies, AI is often claimed to improve trial success rates by helping choose endpoints and populations. One industry take is that predictive models could uplift success odds by a modest single-digit percentage for many trials. The ClinicalReTrial study reports a 5.7% gain in predicted success (^[5]), which from a baseline (say ~11% overall success) could be meaningful. However, real-world evidence of improved approval outcomes is not yet available, partly because such long-term results are often proprietary.
• Operational Efficiency: Survey data indicate broad interest and pilot usage, but concrete numbers vary. Some market research indicates “AI can reduce the time to develop a new drug by up to 50%” (^[44]), though that claim mixes discovery and development phases. Clinical operations teams frequently cite 20-30% time savings on tasks like data reconciliation and report writing when using AI tools. The FDA’s internal example of Elsa (tasks in minutes vs days (^[12])) suggests a potential 80-90% reduction in certain review tasks, albeit on a small scale.

Overall, the evidence suggests that AI interventions tend to yield tens of percent improvements in key metrics like recruiting speed, data processing time, or error rates (^[6]) (^[14]). Even partial gains compound across the multi-year CDP timeline. For instance, if patient recruitment is 40% of costs, cutting recruitment time/cost by 20% saves about 8% of total trial expense. If protocol preparation time is halved, another chunk is saved. Formation Bio’s business model targets a paradigm where trials are “cheaper and faster” so more of the sponsor’s effort can go into generating broad access (“employ 100 people instead of 100,000” (^[28])).

Evidence from Trials and Studies: Academic and conference reports consistently identify recruitment and design as primary AI targets. The 2023 scoping review found that over half of AI-in-clinical-trials literature focuses on recruitment (^[4]). It also notes that as AI enters practice, “we anticipate… the volume of [AI] implementation to increase rapidly.” In other words, although still nascent, AI tools are on the cusp of broader adoption, and even early pilots indicate worthwhile benefits (^[45]) (^[46]).

To illustrate progress with concrete examples, we now present representative case studies.

Case Studies and Real-World Examples

Formation Bio: AI-Driven Trial Staffing and Speed

As described in TIME’s coverage, Formation Bio is a biotech company explicitly founded to exploit AI in clinical operations (^[47]). The company uses AI principally to automate adminand analytical tasks around trials. According to CEO Ben Liu, they have demonstrated saving “as much as 50%” of trial time by automating tasks like patient recruitment, regulatory filings, and data analysis (^[43]). Critically, Formation does not use AI to shorten the actual treatment period (i.e. they don’t give patients an AI that makes them recover faster), but rather to compress everything before and after the trial’s dosing phase.

Formation’s model is to take promising drugs (bought or licensed), run the trial themselves with AI augmentation, and then sell the results or rights. They report already spinning out two successful deals: one drug to Sanofi for €545M, another to Eli Lilly for nearly $2 billion (^[48]). Those sales allegedly reflect that Formation’s use of AI let them “run trials cheaper and faster.” The company suggests they can do trials employing a few hundred people with AI rather than tens of thousands (^[28]). While independent verification is scarce, this example highlights that industry venture capital is betting on AI to create more efficient development companies.

Exscientia: AI in Patient Matching and Drug Design

Exscientia, a UK-based AI pharma, illustrates both discovery and trial acceleration. Although originally focused on drug design, one pilot in 2022 used AI to match patients to individualized therapy plans (^[49]). Researchers gave an AI system tumor samples from 143 cancer patients and a library of 139 cancer drugs (not originally matched to those patients). The AI predicted which drug would work best for each tumor. After treating 56 patients with AI-recommended drugs, 54% experienced disease control for a duration ~1/3 longer than their previous therapies (^[49]). This is a striking improvement for heavily pre-treated patients, suggesting AI can meaningfully improve outcomes by optimizing patient–drug matching in trials.

Exscientia also holds the distinction of bringing the first AI-designed drug candidate into human trials. In 2020 they entered an obsessive-compulsive disorder drug into Phase I—its molecule was identified by AI in just 12 months (^[50]), far faster than the typical several-year timeline. Since then, Exscientia has advanced several more AI-designed compounds (for cancer, asthma, etc.) into trials (^[50]). While the final clinical success of these remains to be seen, the pace of candidate generation was unprecedented. This underscores that AI can speed up the very inputs into clinical development, but also hints at why CDPs need improvement: accelerating discovery without streamlining trials would clog the development pipeline.

AstraZeneca: Parallel Human/AI Endpoints

At a recent industry conference, AstraZeneca’s Chief Data Scientist James Weatherall shared that AZ is trialing AI as a “supporting reader” for imaging endpoints in cardiovascular trials (^[14]). In one example, both human cardiologists and an AI system independently evaluated echocardiograms for heart failure endpoints. The AI produced adjudication results in substantially less time (by directly analyzing incoming data feeds rather than waiting for full data reconciliation) (^[14]). By processing images in real-time, the AI could save months of waiting for final trial outcomes. Importantly, AZ kept humans in the loop; they first ran trials in parallel (humans and AI) to confirm AI matched the standard. Weatherall reports that AI matched expert thresholds and did not degrade trial integrity (^[14]). If replicated at scale, this approach could be a template: use AI to pre-read all images, then have humans focus only on ambiguous cases or final validation. This again exemplifies how process re-engineering (running parallel reading) can deliver time savings (^[17]) (^[14]).

Sanofi: Digital Twins in Asthma Trials

A case study involves Sanofi leveraging digital twin simulations. In an asthma drug trial (detailed in a BiopharmaTrend report), Sanofi created virtual patient cohorts to refine dosing strategies. Using digital twins, Sanofi determined that an extra Phase II dose-finding cohort was unnecessary. In practical terms, the AI-informed plan allowed them to eliminate an entire additional cohort of trial participants, saving “millions” of dollars and reducing trial duration by six months (^[15]). Such a gain greatly streamlines the CDP, as Phase II typically gates Phase III entry. By adjusting their plan based on in-silico results, Sanofi effectively re-optimized their development plan midstream. This example is notable because it contrasts with the usual approach—where companies might add extra cohorts if the initial data are unclear. Instead, Sanofi confidently proceeded without extra studies, buoyed by AI simulation.

Medable and DCT Platforms

Medable is a leading decentralized trial (DCT) platform that infused AI analytics into its operations. According to press releases, Medable clients saw 200% faster enrollment and 50% reduced cost across various trials using their platform (^[51]). These figures likely combine AF-based matching with remote technologies (eConsent, telemedicine, digital endpoint capture). For example, in oncology DCTs, Medable claims that remote patient visits and monitoring reduced dropouts and accelerated accrual. These outcomes suggest that, especially when AI is combined with digital tools, sponsors can dramatically reduce the time and expense of multi-center trials, aligning with CDP goals of speed and efficiency.

IBM Watson and Cautions

Not all AI-driven initiatives have gone smoothly. IBM’s Watson Health famously attempted to apply AI to cancer treatment planning and trial matching, but some high-profile partnerships faltered. For instance, a 2018 collaboration with MD Anderson was wound down after Watson gave unsafe treatment recommendations in some cases (likely due to inadequate training data). This serves as a caution: if AI models are trained on limited or biased data, they can mislead protocols. The clinical development community views such examples as learning experiences, highlighting the importance of rigorous validation and oversight (^[52]) (^[19]).

Summary of Case Evidence

These examples demonstrate AI’s multifaceted impact on CDPs. Formation Bio and Exscientia illustrate how AI can cut years out of drug development. AstraZeneca and Sanofi show how AI-driven insights can shrink trial length and cost by optimizing protocols. Decentralized trial platforms like Medable report striking efficiencies when AI is integrated. And while successes are inspiring, cautionary tales (e.g. IBM Watson) emphasize that trust and validation must accompany adoption.

From these cases, common themes emerge:

• Time Savings: Many AI uses shave months or years off trials (e.g. 6 months saved via protocol optimization (^[15]), or eliminating tasks that took days to minutes (^[12])).
• Cost Reduction: By cutting administrative workload and excess cohorts, companies report savings of millions (Sanofi) or hundreds of millions (formation Bio’s drug deals (^[53])).
• Higher Success Confidence: AI helps make better-informed decisions, reducing the chances of failure due to poor design or site issues (even if quantitative success rates remain proprietary).
• Patient-Centricity: By lowering trial burdens (fewer visits, remote participation), AI-driven DCTs can make CDPs more patient-friendly (^[41]).
• Regulatory Acceptance: These cases often operate at the edge of current regulation—for example, Sanofi’s use of digital twins was possible only because regulators are increasingly open to model-based evidence (^[54]).

Analytical Discussion and Implications

AI-driven innovations in CDPs have broad implications:

1. Acceleration of Development Timelines: Sponsors argue that AI could allow them to "run trials cheaper and faster", potentially reaching patients years earlier (^[28]). If broadly realized, an industry could see drugs come to market with significantly reduced lag times. This could in turn rejuvenate stagnant annual approval counts and improve return on R&D investment.

2. Change in Workforce and Skills: Multiple industry leaders note that existing processes were not designed for AI (^[17]) (^[55]). Successful AI adoption often required redesigning workflows. Broadly, clinical teams will shift from rote tasks to more strategic roles—monitoring AI outputs, tweaking models, and focusing on patient care. Organizations will need to train or hire data scientists, AI specialists, and “prompters” to support these transitions (^[56]) (^[57]). Early adopters advocate embedding AI tools into normal workflows (e.g. integrating an AI reader in the imaging pipeline) rather than making them optional add-ons (^[58]).

3. Patient Access and Equity: AI-enabled matching can theoretically increase trial access for more diverse patients, as algorithms can identify qualified participants in communities or demographics underrepresented before. For example, Walgreens plans to use AI for rapid population identification while simultaneously engaging communities for trust (^[38]). However, there is a risk that biased training data could perpetuate inequities. Ensuring diversity in model inputs and using AI responsibly are active research topics.

4. Regulatory Interaction: Regulators have signaled strong interest in AI. The FDA’s 2025 draft guidance (prepared after extensive workshops) emphasizes that AI tools used in drug development must be validated and transparent (^[19]). It outlines a 7-step credibility framework: sponsors must pre-define the model’s “context of use,” manage risks, and continually re-evaluate (including a proposed “Algorithm Change Protocol” for any model updates) (^[19]) (^[59]). The message is clear: AI can be used if systematic controls are in place. The FDA’s own Elsa project demonstrates support for integration, albeit as an assistant to reviewers, not a standalone. The European Union’s upcoming AI Act will also likely impact clinical trials, requiring organizations to provide AI literacy training and governance (as AstraZeneca leaders noted (^[60])).

5. Ethical and Trust Considerations: Trust in AI remains an issue. As Clinical Trials Arena reported, industry insiders agree that AI must be implemented thoughtfully to overcome skepticism (^[61]) (^[16]). The panel at OCT DACH 2025 stressed that broad staff training and change management are essential—simply deploying an AI tool seldom convinces clinicians by itself (^[16]). Moreover, accountability is a concern: if an AI “agent” takes actions (e.g. drafting a report), who is responsible? Panelists have suggested that for now, human users must remain ultimately accountable and sign off on any AI output (^[62]). Standard operating procedures will need to be updated to document AI use (prompts and code included) for audits (^[63]).

6. Competitive Landscape: The push for AI in CDPs introduces new players and alliances. Tech companies (Google, Amazon Web Services, Startups like Insitro, GNS Healthcare) partner with pharma for data analytics. At the same time, pharma giants form internal AI units (e.g. Janssen’s AI group, Roche’s AKC, Novartis’s Sandoz in-house AI). Contract Research Organizations (CROs) are adapting too, offering AI-enhanced services. Observers note that “data-rich” organizations or consortia may gain advantage in deploying continuously learning models (^[21]).

7. Changing Nature of Evidence: As AI becomes more integral, CDPs might shift toward continuous learning and adaptive post-marketing. For instance, if digital twins and real-world monitoring are trusted, companies may finalize trials with AI-derived data and then refine their understanding in real time post-launch. This could blur lines between clinical trials and real-world evidence, necessitating new regulatory frameworks (FDA has begun discussions on real-world endpoints for approval, partly enabled by AI).

Regulatory and Ethical Considerations

The integration of AI into CDPs raises regulatory and ethical issues that must be carefully managed. Key considerations include:

• Model Validation and Transparency: Regulators stress that any AI model affecting patient safety or trial outcomes must be rigorously validated (^[19]). This means sponsors need to test models on held-out (unseen) data, document performance, and ensure that the model only does what it was designed for (the “context of use” concept). Explainability is less emphasized than validity: industry implementers note that proof of benefit and correctness trumps having fully transparent algorithms (^[64]). FDA draft guidance requires risk-based validation and lifecycle monitoring of AI models (^[19]).

• Data Privacy and Ethics: AI in clinical trials often requires processing sensitive patient data (EHR, genetic profiles). Privacy regulations (HIPAA in the US, GDPR in the EU) limit how data can be combined and used. Sponsors must ensure data are de-identified or patients have consented for secondary use. Ethical oversight extends to avoiding algorithmic bias: if an AI was trained primarily on non-diverse populations, it may perform poorly on underrepresented groups, exacerbating health disparities. Thus, clinical teams must audit model outputs for fairness.

• Regulatory Guidance on AI: Many regulators are still catching up. The FDA has been proactive: aside from the draft guidance on AI in drug decision-making (^[19]), it also hosts the new AI assistant Elsa for internal use (^[11]) and is exploring phasing out animal tests with AI/NAMs (^[65]). The European Medicines Agency (EMA) and Pharmaceuticals and Medical Devices Agency (PMDA) in Japan have expressed interest in “model-informed drug development.” The FDA’s Clinical Trials Transformation Initiative (CTTI) has also been working on AI guidance. However, concrete rules specifically tailored to AI in trials (like a “regulatory dossier for AI tools”) are still under development.

• Human Oversight: Both industry and regulators insist on human-in-the-loop for any critical decision. “AI supporting reader” approaches—where a human remains the ultimate adjudicator—are seen as best practice (^[66]) (^[14]). Even for administrative tasks, companies require sign-offs on AI-generated documents. Enterprises need robust audit trails: One panelist questioned how to log AI agent actions for compliance (^[63]). For now, responsibility usually lies with the person submitting data to regulators, not the AI that helped prepare it.

• Ethical Trial Conduct: If AI enables synthetic control arms, there are ethical implications: eliminating placebo groups benefits patients but raises questions about historical data adequacy. Regulators will review these on a case-by-case basis. AI-driven patient matching raises privacy concerns (especially if social media or GPS data is used). The overall shift to more AI and remote trials must also respect patient autonomy and informed consent; clarity on how AI is used will likely need to be included in consent forms.

Future Directions and Outlook

Looking ahead, experts and trends point to several future developments in AI-enhanced CDPs:

• Wider Use of Generative AI/LLMs: Large language models are already being piloted for tasks like writing clinical study reports, patient letters, and even drafting trial protocols (with human editing) (^[67]). In coming years, it is expected that LLMs will become commonplace for automating medical writing tasks and literature reviews. For example, AstraZeneca’s Weatherall mentioned widespread internal use of ChatGPT and similar tools for presentations and trial design ideas (^[40]). The next step may be LLMs that can “talk” to each other, exchanging domain knowledge (as AZ is testing agent-to-agent systems (^[68])).

• Closed-Loop Adaptive Trials: Imagine a trial where an AI system continuously monitors interim data and automatically adjusts arms or endpoints per pre-specified algorithms, almost in real-time. While fully autonomous trials are not yet happening, some companies foresee such workflows. The infrastructure (real-time data capture, connected databases, predictive models) is rapidly maturing. Regulatory acceptance will depend on demonstrating patient safety. Trials have already become more dynamic (e.g., oncology “umbrella” or “basket” trials); AI can further enable this by allowing more frequent, data-driven decisions within a trial.

• Synthetic Trials and External Data Usage: The use of real-world evidence (RWE) is growing. AI will push this further with synthetic cohorts and digital twins. In rare diseases or pandemics, we may see trials with only a few real participants and the rest simulated, evaluated by regulators. Agencies are tracking this: the FDA’s Real-World Evidence program and EMA’s qualification of novel endpoints will play roles. For clinical development planning, this means future CDPs may allocate fewer resources to large Phase III recruitment, relying instead on integrated evidence from AI-augmented sources.

• Precision and Personalized Trials: As AI uncovers biomarkers and patient subtypes, CDPs will likely incorporate more molecular or digital stratification. Trials may become smaller but more targeted. Machine learning could, for instance, pre-screen patients’ genomic profiles to enroll only those most likely to respond, raising success rates. This precision approach is already visible in oncology (e.g. basket trials for specific mutations). AI will expand it to more disease areas, potentially altering how development plans are structured (e.g. having multiple mini-cohorts rather than one large one).

• Integration of Multi-Omics and IoT: Future CDPs might leverage continuous data flows. For instance, wearable sensors could feed into AI models to monitor drug effects in the real world, altering post-marketing studies. The lines between clinical trial and clinical practice may blur: a development plan might start in Phase II and seamlessly transition into observational follow-up with AI analyzing longitudinal wearable data. This could redefine endpoints (from biochemical lab results to aggregated behavior patterns) and accelerate the overall drug development cycle.

• Standardization and Collaborative Data Sharing: Realizing AI’s potential will require large shared datasets. We may see pre-competitive collaborations or consortia to pool trial data (in anonymized form) for AI training. Regulatory endorsement of such data marketplaces could be a game-changer. Standardized CDP frameworks may emerge, somewhat like how IFRS standards economics. For example, the Good Simulation Practices initiative is already outlining how in-silico results should be reported.

• Ethical and Societal Impact: As AI takes on more trial tasks, regulators and ethicists will scrutinize the implications. Discussions will occur on algorithmic accountability, patient consent for AI use, and equitable access. The EU AI Act (likely implemented by mid-decade) will impose requirements on high-risk AI systems (which clinical trial tools would likely qualify as). This will drive companies to invest in explainability and bias audits. Patient advocacy groups are expected to demand transparency on how AI was used, especially if recommendations directly affect who gets treated.

In summary, the trajectory is toward smarter, faster, and more adaptive development plans. AI will not replace all human expertise but will become embedded in every stage of CDPs. The future impacts may include: markedly shortened time-to-market for new drugs, greatly reduced trial costs, and more therapies finding the right patients. One industry leader predicted that AI in 2024 will “serve as a force multiplier” allowing stakeholders to “significantly amplify their impact” (^[22]). In the long run, the success of AI in CDPs will be measured by tangible gains: more cures reaching patients, developed with fewer errors and delays.

Conclusion

Clinical development plans have traditionally been laborious, high-stakes roadmaps. Introducing AI and advanced analytics is proving to be a game-changer for this domain. Today’s evidence shows that AI can (and is already beginning to) transform CDPs by enabling:

• Faster, more data-driven trial design and decision-making.
• More efficient patient matching and enrollment.
• Automated monitoring and analysis throughout trials.
• Insights that reduce costs, streamline timelines, and improve trial quality.

These shifts are backed by case studies (e.g. Formation Bio, Exscientia, Sanofi) and growing industry consensus. However, technology is only half the story: realizing these benefits requires also rethinking processes, investing in talent, and carefully managing risks. Regulatory agencies are playing catch-up, but recent guidance documents and AI tools (like the FDA’s Elsa) suggest a modernized environment is emerging. Experts caution about the need for human oversight, bias control, and change management; trust-building is crucial (^[17]) (^[16]).

For pharmaceutical and biotech organizations, the message is clear: AI is not a speculative luxury but a strategic imperative. The arbitrage opportunity—in Ben Liu’s words—is that fully utilizing AI could let us “offer drugs with far more expanded access at lower cost” (^[28]). Achieving that vision will take time and care, but the potential upside is transformative: shorter development cycles, more success, and ultimately faster delivery of life-saving therapies.

As we conclude, it is important to note that claims of AI’s impact are continuously evolving. Sources frequently highlight optimistic projections (e.g. multi-decade drug development halved) alongside cautionary uncertainties. Thus, ongoing research, pilot projects, and regulatory trials will continue to refine our understanding. This report collates the current state of knowledge—as of 2026—painting a picture of rapid change in clinical development. The coming years will show which AI approaches deliver on their promise, but it is undeniable that AI has begun to reshape how we conceive and execute clinical development plans (^[23]) (^[69]).

Key sources: Industry analyses, journal reviews, and news reports were synthesized in this report. Citations throughout (e.g. (^[23]) (^[2]) (^[26]) (^[4])) document the data points and expert insights presented. Notably, regulator and ethics issues, though secondary to the main focus, were also covered via official documents and thought-leader publications (^[19]) (^[64]). This collection of references provides a comprehensive, up-to-date evidence base for understanding AI’s role in clinical trial planning and execution.