Alec Silberg ’28
Traditional clinical trial workflows involve massive documentation overhead, protocol complexity and fragmented communication. They create a structure to facilitate clinical trial processes from trial enrollment to data analysis. Today, these workflows are becoming increasingly digital. Clinical software solutions in the past few decades have created a transition from paper and wet-ink signatures to online systems. Digitizing healthcare documentation and creating value out of newly-accessible data has facilitated increased efficiency and efficacy in clinical practice.
Even though records of all sorts have become digitized since the dawn of the internet, many workflows in healthcare have remained on paper. Digitizing these files and processes has allowed for an increase in both speed and quality while saving costs (Transitioning, 2024). Moreover, these software operations are easily scaled, allowing for accessible onboarding and secure sharing. Creating a digital infrastructure and pipeline for healthcare data interoperability proves an effective replacement of paper systems (Transitioning, 2024). In addition, virtual systems and clinical solution companies encourage regulatory compliance. Workflow providers operate closely with the FDA and under HIPAA, for example, to ensure umbrella approval (Florence Healthcare, 2025). Adoption of these platforms therefore comes with compliance, largely due to increased visibility and automation. Lastly, digital platforms, unlike paper, are flexible and allow for updates (Transitioning, 2024). Paper limits accessibility and sharing, complicating and slowing data sharing. Altogether, digitization is evidenced to be a transformative shift in medical research and its rise has opened the door for artificial intelligence (AI) applications.
Today, AI is being incorporated to automate and optimize the clinical-research workflow. Not only do AI applications in this space mitigate administrative overload and human error, but they capture and label data through automated classification and operations. Benefits for this include more informed decision-making and easier access to data. In addition, AI utilizes large language models (LLMs), deep-learning systems trained on massive datasets, to enable predictive modeling (How AI, 2025).
Florence Healthcare, a company that connects research sites, sponsors, and Contract Research Organizations (CROs) to streamline trial workflows, exemplifies how workflow-focused platforms are evolving toward AI-enabled orchestration. Traditionally, Florence facilitates clinical trials by serving as a centralized digital platform through which research sites manage regulatory documents, communicate with sponsors and CROs, and track study progress. These processes typically rely on standardized questionnaires and surveys completed by sites to report readiness, compliance status, and operational metrics (Florence Healthcare, 2025). Sponsors and CROs use this information to assess site performance, guide trial startup decisions, and monitor ongoing execution (Riera, 2024). Florence is using AI to tackle three challenges that CROs face head-on: limited visibility into site performance, inefficient trial study start-up processes, and fragmented oversight and monitoring (Florence announces, 2025). Adding AI capabilities to their system allows sponsors–the companies responsible for trials, like pharmas–to easily navigate through websites and find more accurate results with surveys. Doing so facilitates efficient and accurate monitoring across clinical processes. Moreover, AI-driven contract negotiations accelerate timelines and automated document exchange simplifies document management for startups and trial conductors (Florence announces, 2025). While Florence is first integrating AI as a feature to reduce administrative work, it is ultimately adapting its framework to embed this technology across the board.
Florence’s AI applications are but a few of the many uses of AI in clinical trial workflows. Its integration allows for consistency across medical analysis, something humans struggle to attain (Riera 2024). The speed and precision of AI tools also promote early-stage disease detection. Through their implementations, Florence has exhibited how AI agents can monitor task completion, send reminders, and update trackers (Florence announces, 2025). Additionally, predictive modeling can be used for enrollment timelines as well as risk assessment and supply management. These all prove key to upgrading Florence’s seven core offerings in the market, related to site management, document exchanges, and remote monitoring (Florence announces, 2025). While the upside is substantial, realizing it fully requires awareness of the obstacles that accompany AI adoption in clinical research.
There are multiple challenges and considerations for deploying AI tools. Given that AI LLM models often require large data sets for training purposes, data access and costs act as a significant consideration in technology development. Image sharing, for example, is limited due to HIPAA regulations, which hinders available data and makes its storage and annotation costly (Riera, 2024). Moreover, even with sufficient training data, AI models can suffer from “hallucinations” of information. More specifically, if the training data is insufficient or biased, it can lead predictive models to produce incorrect results (Riera, 2024). For example, its data could be derived from one population or ignore inequalities. Integrating AI tools into existing workflows also demands substantial coordination across teams to ensure compatibility, usability, and long-term maintenance (Riera, 2024).
Clinical trials no longer hinge solely on scientific discovery but on the systems capable of operationalizing it. As digitization gives way to true workflow intelligence, AI stands to become the infrastructure through which modern research is executed. Platforms like Florence Healthcare illustrate this shift by demonstrating how integrated AI can streamline study start-up and execution. However, the promise of these tools depends on incremented implementation, rigorous validation, and an industry willing to adapt entrenched practices. Nonetheless, it seems evident that future innovation will be incredibly accelerated by AI implementation into workflows, encouraging both more effective processes and reduced overhead.
Alec Silberg is a staff writer at The Princeton Medical Review. He can be reached at alec.silberg@princeton.edu.
References
Transitioning from Paper to Electronic Workflows in Clinical Trials with Devana. RealTime. (2024, July 5). https://realtime-eclinical.com/2024/02/21/transitioning-from-paper-to-electronic-workflows/#:~:text=Efficiency%20and%20Speed:%20One%20of,course%20of%20a%20clinical%20trial.
Florence announces AI-assisted workflows to transform clinical trials. Florence. (2025, September 2). https://www.florencehc.com/press-release/florence-announces-ai-assisted-workflows/Florence Healthcare: Transform Clinical Trial Study & Site Operations. Florence. (2025, December 22). https://www.florencehc.com/
How AI is Transforming Clinical Trials: AHA. American Hospital Association. (2025, October 21). https://www.aha.org/aha-center-health-innovation-market-scan/2025-10-21-how-ai-transforming-clinical-trials#:~:text=Artificial%20intelligence%20(AI)%20is%20rapidly,with%20AI%20in%20various%20capacities.
Riera, A. (2025, December 3). How to Incorporate AI Technology into Medical Imaging Workflows and Why You Should Do It. QMENTA. https://www.qmenta.com/blog/how-to-integrate-ai-in-medical-imaging#:~:text=These%20workflows%20are%20created%20with,current%20challenges%20of%20AI%20technologies.
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