The impact of customized AI workflows on streamlining drug target identification, literature review, and healthcare professional engagement in clinical research

Clinical research helps create new drugs, devices, and treatments. But it usually involves many complex steps with a lot of scientific data, clinical trials, and frequent communication between sponsors, researchers, and healthcare providers. Traditionally, many of these tasks take a lot of manual work and time.

Customized AI workflows made for life sciences can solve these problems. They automate repetitive tasks, look through huge amounts of scientific data, and help make decisions at different research stages. IQVIA recently launched AI tools, using NVIDIA’s technology, that show this approach. These AI tools use NVIDIA’s NIM Agent Blueprints for quick setup, NeMo Customizer to fine-tune models, and NeMo Guardrails to keep operations safe. They cover all US health system rules.

These AI workflows have many uses but mainly focus on three important areas:

• Drug Target Identification
• Literature Review
• Healthcare Professional Engagement

Streamlining Drug Target Identification

Drug target identification is the first important step in drug development. It means finding molecules or genes in the body linked to diseases to create treatments that focus on these markers.

This process needs analyzing large biomedical datasets, which are spread out in many databases and publications. AI tools can quickly gather and study these data sets better than manual work. IQVIA’s AI workflows use machine learning algorithms made for life sciences data to check genomic, biochemical, and clinical information.

In the US, where drug development is very competitive, these AI tools can cut down the time needed for discovery. IQVIA says their AI agents automate data review and create insights that fit US research and regulatory rules. This helps drug teams find good targets faster. Saving weeks or months in early development is important because time affects costs, patients’ benefits, and the success of the drug.

Also, AI helps improve accuracy by reducing human errors in finding candidate targets. It helps judge risks and viability by combining knowledge from clinical trials, molecular biology, and market data. This mix fits the strict US clinical and regulatory rules and helps meet FDA guidelines during drug discovery.

Accelerating Literature Review in Clinical Research

Clinical research depends heavily on reviewing scientific papers to make decisions based on evidence. Researchers must keep up with thousands of new papers every month, medical rules from regulators, and data from ongoing clinical trials. This amount of information quickly becomes too much for manual reading.

IQVIA’s AI workflows, powered by NVIDIA AI tools, automate this hard job. Their AI agents scan, filter, and organize important literature in seconds. This lets US researchers and staff spend more time evaluating science rather than gathering data. Kimberly Powell, Vice President of Healthcare at NVIDIA, notes that these AI workflows help researchers go through literature faster, shortening the time needed to plan trials.

Since many clinical trials happen at once in the US, this ability is very useful. Faster literature review improves trial design and helps teams adjust quickly to new scientific results or rule changes. It also helps smaller research groups and hospitals with less staff by giving fast, detailed summaries fit for their study needs.

A special feature is AI’s ability to pull out specific details using Natural Language Processing (NLP). In the US healthcare system, where clinical notes and documents often use complicated language, AI reads and understands the data well. This cuts down the paperwork load on research staff and supports meeting compliance rules.

Enhancing Healthcare Professional Engagement

Engaging healthcare professionals is an important part of clinical research and the launch of new products. It means two-way communication between life sciences companies and doctors, specialists, and pharmacists. Good engagement helps with recruiting patients for trials, using new treatments, and monitoring after a product launch.

IQVIA’s AI tools support this by creating personalized communication based on data. They analyze healthcare professional behavior, past data, treatment choices, and market trends. This helps US healthcare groups improve how they communicate, pick better times to reach out, and customize messages to fit healthcare professionals’ needs.

This is helpful because the US healthcare market has many players with different goals. AI helps companies handle this complexity while following privacy rules like HIPAA, building trust between providers and researchers.

Parts of the engagement work, like scheduling follow-ups or summarizing meetings, can be automated by AI. This saves staff time and makes coordination during trials or product launches faster. It frees up research staff and IT managers to focus on bigger tasks like data management and analysis.

AI in Healthcare Workflow Automation and its Relevance

Automation has been used in healthcare for many years. Adding AI makes it more precise and adaptable. AI automation not only finishes routine tasks but also helps with decision-making and real-time data analysis. For US healthcare managers and IT staff, using these tools can change how well the organization works.

In clinical research, workflow automation includes scheduling appointments, sending patient reminders, helping with clinical notes, and billing. Customized AI workflows go further by adding healthcare-specific rules and learning from data to get better over time.

For example, AI with NLP reduces the time doctors and research staff spend on notes by transcribing and summarizing patient visits and records automatically. This saves time and helps follow US regulatory documentation rules.

Beyond notes, AI monitors trial progress and participant data, spotting problems or risks early. AI also helps coordinate work between departments to keep things running smoothly and avoid trial delays. This is very important in the US, where trials must meet both federal and review board rules.

IQVIA’s Healthcare-grade AI® platform is an example of mixing advanced AI with healthcare knowledge. It makes sure AI works fast and follows privacy rules and patient safety standards. This balance is key to healthcare rules in the US.

The Importance of Data Privacy, Compliance, and Safety

Customized AI workflows in clinical research must keep patient data private and safe. In the US, laws like HIPAA require strong protection of patient information.

IQVIA uses several privacy technologies in its AI. These include secure data handling, anonymizing data, and controlled access to keep sensitive information safe. They also use NeMo Guardrails tech to stop AI from producing wrong or inappropriate results. This is important to follow rules and keep clinical work trustworthy.

This focus makes sure AI tools follow legal limits, protect patient info, and meet ethical standards in research. For healthcare managers and IT staff, using AI with built-in compliance cuts risks and builds confidence in digital changes.

AI Supporting Clinical Trial Acceleration

Clinical trials often take a long time, cost a lot, and are complex. AI tools, like those built by IQVIA and NVIDIA, help shorten trials by improving each stage—from discovery to market release.

Using AI agents in target ID and literature review cuts down time spent on early data work and hypothesis tests. AI also helps communicate with healthcare professionals for faster recruitment and keeping the right participants.

These improvements speed up new treatments’ development and availability in the US, where there is high demand for new and affordable medicines. Faster trials also reduce costs for sponsors and let patients get experimental treatments sooner.

Optimizing IT Infrastructure for AI in Clinical Research

Healthcare managers and IT staff need to prepare their IT systems to use AI workflows. AI requires powerful computing, safe cloud storage, and data connections with Electronic Health Records (EHR) systems.

IQVIA works with NVIDIA to use high-performance hardware and flexible AI platforms like NIM Agent Blueprints. This setup fits many US clinical research places—from big universities to small hospitals.

For success, IT teams must ensure AI tools can work well with current clinical and administrative software. They need to manage data flow, keep real-time updates, and make sure systems stay secure and available.

Future Outlook for AI in US Clinical Research

The US clinical research field will likely keep relying more on customized AI workflows. The market for AI in healthcare is expected to grow from $11 billion in 2021 to almost $187 billion by 2030. This means using AI tools may become normal practice.

It will be important to train staff, regularly check AI models, and keep them accurate and reliable. Regulatory agencies like the FDA are also updating rules to keep AI safe and useful in trials and healthcare.

Events like IQVIA TechIQ 2025 in London will offer more information and discussions about AI in clinical research. US healthcare leaders and IT workers should watch these developments to stay updated and competitive.

By using customized AI workflows for drug target identification, literature review, and healthcare professional engagement, US clinical research groups can improve efficiency, lower costs, and better patient results. These AI tools provide a practical way to handle growing data and regulatory challenges in the US, leading to smoother, data-driven clinical research processes.