| JNPMEDI offers AI-powered clinical trial consulting
| AI boosts efficiency, empowering humans to focus on high-value tasks
| A dedicated AI task force is advancing solutions for real-world clinical settings
The AI | By Deok-gyu Yoo
Youngyong Park, Chief Technology Officer (CTO) at JNPMEDI
"I expect AI to evolve beyond a simple supportive role into a partner that understands goals from a macro perspective and performs tasks independently. This shift does not diminish the human role but empowers it; by handling repetitive tasks 24/7 to boost efficiency, AI gives humans the opportunity to focus on ‘high-value work that only humans can do.’"
So says Youngyong Park, Chief Technology Officer (CTO) of JNPMEDI, when asked how AI will transform the industry over the next five years.
JNPMEDI is driving digital transformation (DX) in the clinical trial industry with the launch of ‘Maven Clinical Cloud,’ a clinical trial data management solution. The company digitizes clinical trials, provides consulting for regulatory approvals, and partners with Amazon Web Services (AWS) to deliver cloud services to hospitals.
We sat down with CTO Youngyong Park, the head of technology at JNPMEDI, for an in-depth discussion.
A screenshot of JNPMEDI’s Maven Clinical Cloud interface
- What led JNPMEDI to adopt AI technology in clinical operations and data management?
"Clinical trials are ultimately a race against time. Even a single day of delay can result in losses ranging from thousands to hundreds of thousands of dollars. It’s not just a matter of cost; delays also slow down the delivery of much-needed treatments to patients. Given the immense pressure of time and cost in the clinical environment, we concluded that AI is no longer an option, but a necessity.”
- What kind of AI is the company developing and researching in the medical field?
“Beyond merely improving operational efficiency, we are developing AI-based products aimed at innovating the entire clinical trial process. A clinical trial fundamentally begins with a protocol, and the entire process is designed and operated according to that protocol. Therefore, understanding the protocol is key to understanding the trial itself. Creating an AI capable of interpreting the protocol enables the automation and advancement of the entire process. To achieve this, our research focuses on AI technology that can structure and interpret unstructured natural language data. For instance, we are building use cases such as automatically generating case report forms (Protocol to eCRF) or creating rules for efficient and accurate data collection (Protocol to DVS) directly from the protocol.”
- How is AI-powered clinical data being utilized in practice?
“In the medical field, AI is currently playing a practical role across various areas—decision support, anomaly detection, and data quality improvement—beyond simple data analysis. For example, the FDA is moving toward reducing animal testing and adopting digital simulations and AI predictive models based on actual patient data as alternatives. This signals that AI is becoming a core technology assisting decision-making in the early stages of drug development. In line with this trend, we use AI to interpret variables and conditions defined in a protocol, which then automates eCRF design and data verification logic. This is reshaping how clinical data is collected and utilized. By understanding the context of clinical data and proactively predicting errors or outliers, AI is enhancing the overall accuracy and stability of clinical trials.”
- You launched a dedicated AI task force(TF) last June to strengthen AI capabilities. What progress has it made since then?
“We launched a dedicated AI Task Force to accelerate the application of AI across the entire clinical trial lifecycle. This team focuses on implementing practical solutions for real clinical settings, rather than just developing technology. A key achievement is our full-scale research into protocol-based automation, specifically ‘Protocol to eCRF’ and ‘Protocol to DVS.’ We have advanced our AI models to structure natural language data and generate rules, laying the groundwork for greater efficiency and consistency in eCRF design and data verification.
The team is also improving internal algorithms to enhance the standardization and alignment of diverse clinical data. By linking this with ‘Maven Converter,’ our automated CDISC data conversion solution, we are building an AI-based data standardization system. This serves as a turning point, enabling not only automation but also simultaneous regulatory compliance and quality improvement.”
- AI-driven data management is gaining attention in the clinical trial sector. How is JNPMEDI approaching this trend?
“Since clinical trials are a regulated industry, JNPMEDI pursues innovation and efficiency by applying AI to key stages while strictly adhering to the framework of existing processes. In particular, we are achieving results by automating and optimizing data collection, cleaning, and verification—tasks previously done manually—through a validated AI system. This approach boosts efficiency and accuracy without compromising the reliability of the established process, ultimately improving both the speed and quality of clinical trials.”
- It's understood that you are collaborating with Amazon Web Services (AWS). What are the key features and differentiated strengths of JNPMEDI's CDMS compared to competitors?
“We are collaborating with AWS to design and validate optimal cloud architectures for the healthcare and life sciences sectors. ‘Maven Clinical Cloud’ is a prime example, having successfully passed AWS’s Foundational Technical Review (FTR), which evaluates architecture stability and security. A key differentiator is our selection as an official AWS MSP Partner last March. This allows us to provide consulting and technical resources that factor in regulatory requirements from the very design stage of the cloud, especially for trials involving digital elements. As ‘digital experts’ with a deep understanding of medical regulations, we offer solutions that drive innovation while ensuring regulatory compliance.”
- Your mid-to-long-term plan is to pursue an IPO and global expansion. What are your current plans and strategies?
“Our core strategy focuses on expanding into global markets, with the U.S. as a primary target. The U.S. market has a strict regulatory environment (FDA) and high technical standards, but also high demand for digital health and AI solutions. To enter this market, we built the ‘FDA Expert Solution,’ providing data-driven support for trial design and operation, including FDA regulatory strategy consulting. We are also eyeing the European market. By participating in events like the CDISC+TMF Europe Interchange, we gain firsthand insights into European regulatory trends and data standardization while strengthening our local network. Since Europe requires navigating multinational regulations like CE certification, we aim to provide differentiated value centered on data standardization technologies (e.g., AI-based SDTM conversion, automated eCRF design). These tailored strategies position us not just as a solution provider, but as a true clinical operations partner.”
- Beyond its own initiatives, JNPMEDI also supports Korean pharmaceutical and biotech companies entering the U.S. market. Could you introduce the specific solutions you provide for this?
“JNPMEDI has developed the ‘FDA Expert Solution’ to offer end-to-end support—from clinical trial design to data submission—for Korean pharmaceutical and biotechnology companies preparing to enter the U.S. market. Because the U.S. FDA strictly requires standardized and electronically submitted clinical data, we leverage AI to build regulatory-compliant data structures and workflows. Our AI models interpret the conditions and variables defined in the clinical trial protocol and use that information to extract the key elements needed for Case Report Form (eCRF) design. We are also developing capabilities that automatically propose and apply data quality control (QC) rules to enable more precise review and validation of collected data. Additionally, ‘Maven Converter,’ which was developed to automatically transform unstructured data into the CDISC formats required by the FDA, applies AI-based mapping technology to reduce the time and cost associated with data standardization while improving accuracy. In terms of global expansion, JNPMEDI is strengthening its role not just as a tool provider but as a practical clinical trial partner tailored to each country’s regulatory environment. Through AI-driven technologies, we are simultaneously enhancing regulatory readiness and global scalability.”
- What advantages and limitations did you face when applying your solutions?
"Our solutions have demonstrated clear advantages when applied to real clinical trial projects. The biggest strength is the efficiency of clinical data management. By automating eCRF design and the data cleaning and verification processes that were previously manual, we significantly shortened overall timelines and reduced the possibility of human error. Our data submission capabilities, aligned with international standards like CDISC, have also allowed companies preparing for global regulatory submissions to respond more quickly and accurately. Cloud-based platforms like Maven Clinical Cloud provide an environment for multiple users to collaborate simultaneously, enhancing the real-time nature, traceability, and data security of clinical trials.
However, some limitations exist. For example, users accustomed to traditional methods might find new digital tools unfamiliar at first, requiring a certain level of training. Additionally, because system environments and processes vary between hospitals and research institutions, customization is sometimes needed. We are working to minimize these initial adaptation issues through user-centric UX improvements, enhanced onboarding education, and building a field-specific technical support system. We are also continuously improving features to increase practical usability and satisfaction."
- What is the biggest challenge you currently face?
"The biggest challenge JNPMEDI faces is consistently providing solutions that satisfy both user experience and regulatory requirements in a rapidly evolving environment. Because clinical trials directly impact patient lives, our solutions must be not only technologically superior but also trustworthy and compliant. Currently, the systems, user environments, and data structures used in various clinical trial sites differ, so a core challenge is to advance our solution's structure to be flexible and customizable for each environment. To this end, we are continuously improving product features based on real user feedback, and are promoting a modular design, user-centric UI/UX improvements, and strengthened technical support to accommodate complex industry demands. Additionally, we recognize that continuous acquisition of reliable training data and ensuring the explainability of AI are important challenges for applying AI technology in practical clinical trial operations. We are also strengthening our internal data governance and collaboration systems for these purposes."
- How does your solution manage patient data, including personal information?
“Security is our top priority. We maintain a robust security management system, having obtained major international certifications including ISO 27001 (Information Security Management System), ISO 27701 (Privacy Information Management System), and ISO 27799 (Personal Health Information Security Management). Our core solution, Maven Clinical Cloud, passed AWS’s Foundational Technical Review (FTR), ensuring we meet global-level data protection requirements even in a cloud environment.”
- As AI technology advances, the clinical trial paradigm is changing. How do you expect AI to impact clinical trials in the future?
“Until now, AI has played a supportive role, automating parts of the process like data entry or anomaly detection. In the future, I believe AI will play a core role from the design stage itself. Data-driven decisions will shape how trials are designed—determining how to accelerate high-potential trials or improve the success rate of risky ones. We are collecting diverse data types to design more precise clinical trials.”
- How do you see AI and cloud technology evolving in the next 1-2 years?
“Within the next one to two years, I expect AI technologies to become more practical and problem-solving oriented. In particular, Generative AI and AI Agents will go beyond simple automation to demonstrate higher productivity in the clinical domain.”
- How will AI and cloud technology evolve in this field over the next five years?
“Currently, AI assists in decision-making. However, as I mentioned, I expect it to evolve into a partner that understands macro goals and performs tasks independently. This is not a change that diminishes the human role, but rather one where AI, capable of performing repetitive tasks 24/7, offers greater efficiency, thereby giving humans the opportunity to focus more on 'tasks only humans can do.' Empathy, ethical judgment, and complex contextual interpretation still remain uniquely human domains, and we will continue to develop technology that does not harm these essential qualities."
- Any final words?
“While AI can replace people in some areas, we aim to create AI that extends and complements the capabilities of medical professionals. We focus on practical, trustworthy solutions that put clinical field problems at the center. All our technical efforts are aimed at building better medical infrastructure. I believe this will ultimately create an environment where more people can access medical benefits in a timely and cost-effective manner.”
✔️ To see the original article: https://www.newstheai.com/news/articleView.html?idxno=7862
| JNPMEDI offers AI-powered clinical trial consulting
| AI boosts efficiency, empowering humans to focus on high-value tasks
| A dedicated AI task force is advancing solutions for real-world clinical settings
The AI | By Deok-gyu Yoo
Youngyong Park, Chief Technology Officer (CTO) at JNPMEDI
"I expect AI to evolve beyond a simple supportive role into a partner that understands goals from a macro perspective and performs tasks independently. This shift does not diminish the human role but empowers it; by handling repetitive tasks 24/7 to boost efficiency, AI gives humans the opportunity to focus on ‘high-value work that only humans can do.’"
So says Youngyong Park, Chief Technology Officer (CTO) of JNPMEDI, when asked how AI will transform the industry over the next five years.
JNPMEDI is driving digital transformation (DX) in the clinical trial industry with the launch of ‘Maven Clinical Cloud,’ a clinical trial data management solution. The company digitizes clinical trials, provides consulting for regulatory approvals, and partners with Amazon Web Services (AWS) to deliver cloud services to hospitals.
We sat down with CTO Youngyong Park, the head of technology at JNPMEDI, for an in-depth discussion.
A screenshot of JNPMEDI’s Maven Clinical Cloud interface
- What led JNPMEDI to adopt AI technology in clinical operations and data management?
"Clinical trials are ultimately a race against time. Even a single day of delay can result in losses ranging from thousands to hundreds of thousands of dollars. It’s not just a matter of cost; delays also slow down the delivery of much-needed treatments to patients. Given the immense pressure of time and cost in the clinical environment, we concluded that AI is no longer an option, but a necessity.”
- What kind of AI is the company developing and researching in the medical field?
“Beyond merely improving operational efficiency, we are developing AI-based products aimed at innovating the entire clinical trial process. A clinical trial fundamentally begins with a protocol, and the entire process is designed and operated according to that protocol. Therefore, understanding the protocol is key to understanding the trial itself. Creating an AI capable of interpreting the protocol enables the automation and advancement of the entire process. To achieve this, our research focuses on AI technology that can structure and interpret unstructured natural language data. For instance, we are building use cases such as automatically generating case report forms (Protocol to eCRF) or creating rules for efficient and accurate data collection (Protocol to DVS) directly from the protocol.”
- How is AI-powered clinical data being utilized in practice?
“In the medical field, AI is currently playing a practical role across various areas—decision support, anomaly detection, and data quality improvement—beyond simple data analysis. For example, the FDA is moving toward reducing animal testing and adopting digital simulations and AI predictive models based on actual patient data as alternatives. This signals that AI is becoming a core technology assisting decision-making in the early stages of drug development. In line with this trend, we use AI to interpret variables and conditions defined in a protocol, which then automates eCRF design and data verification logic. This is reshaping how clinical data is collected and utilized. By understanding the context of clinical data and proactively predicting errors or outliers, AI is enhancing the overall accuracy and stability of clinical trials.”
- You launched a dedicated AI task force(TF) last June to strengthen AI capabilities. What progress has it made since then?
“We launched a dedicated AI Task Force to accelerate the application of AI across the entire clinical trial lifecycle. This team focuses on implementing practical solutions for real clinical settings, rather than just developing technology. A key achievement is our full-scale research into protocol-based automation, specifically ‘Protocol to eCRF’ and ‘Protocol to DVS.’ We have advanced our AI models to structure natural language data and generate rules, laying the groundwork for greater efficiency and consistency in eCRF design and data verification.
The team is also improving internal algorithms to enhance the standardization and alignment of diverse clinical data. By linking this with ‘Maven Converter,’ our automated CDISC data conversion solution, we are building an AI-based data standardization system. This serves as a turning point, enabling not only automation but also simultaneous regulatory compliance and quality improvement.”
- AI-driven data management is gaining attention in the clinical trial sector. How is JNPMEDI approaching this trend?
“Since clinical trials are a regulated industry, JNPMEDI pursues innovation and efficiency by applying AI to key stages while strictly adhering to the framework of existing processes. In particular, we are achieving results by automating and optimizing data collection, cleaning, and verification—tasks previously done manually—through a validated AI system. This approach boosts efficiency and accuracy without compromising the reliability of the established process, ultimately improving both the speed and quality of clinical trials.”
- It's understood that you are collaborating with Amazon Web Services (AWS). What are the key features and differentiated strengths of JNPMEDI's CDMS compared to competitors?
“We are collaborating with AWS to design and validate optimal cloud architectures for the healthcare and life sciences sectors. ‘Maven Clinical Cloud’ is a prime example, having successfully passed AWS’s Foundational Technical Review (FTR), which evaluates architecture stability and security. A key differentiator is our selection as an official AWS MSP Partner last March. This allows us to provide consulting and technical resources that factor in regulatory requirements from the very design stage of the cloud, especially for trials involving digital elements. As ‘digital experts’ with a deep understanding of medical regulations, we offer solutions that drive innovation while ensuring regulatory compliance.”
- Your mid-to-long-term plan is to pursue an IPO and global expansion. What are your current plans and strategies?
“Our core strategy focuses on expanding into global markets, with the U.S. as a primary target. The U.S. market has a strict regulatory environment (FDA) and high technical standards, but also high demand for digital health and AI solutions. To enter this market, we built the ‘FDA Expert Solution,’ providing data-driven support for trial design and operation, including FDA regulatory strategy consulting. We are also eyeing the European market. By participating in events like the CDISC+TMF Europe Interchange, we gain firsthand insights into European regulatory trends and data standardization while strengthening our local network. Since Europe requires navigating multinational regulations like CE certification, we aim to provide differentiated value centered on data standardization technologies (e.g., AI-based SDTM conversion, automated eCRF design). These tailored strategies position us not just as a solution provider, but as a true clinical operations partner.”
- Beyond its own initiatives, JNPMEDI also supports Korean pharmaceutical and biotech companies entering the U.S. market. Could you introduce the specific solutions you provide for this?
“JNPMEDI has developed the ‘FDA Expert Solution’ to offer end-to-end support—from clinical trial design to data submission—for Korean pharmaceutical and biotechnology companies preparing to enter the U.S. market. Because the U.S. FDA strictly requires standardized and electronically submitted clinical data, we leverage AI to build regulatory-compliant data structures and workflows. Our AI models interpret the conditions and variables defined in the clinical trial protocol and use that information to extract the key elements needed for Case Report Form (eCRF) design. We are also developing capabilities that automatically propose and apply data quality control (QC) rules to enable more precise review and validation of collected data. Additionally, ‘Maven Converter,’ which was developed to automatically transform unstructured data into the CDISC formats required by the FDA, applies AI-based mapping technology to reduce the time and cost associated with data standardization while improving accuracy. In terms of global expansion, JNPMEDI is strengthening its role not just as a tool provider but as a practical clinical trial partner tailored to each country’s regulatory environment. Through AI-driven technologies, we are simultaneously enhancing regulatory readiness and global scalability.”
- What advantages and limitations did you face when applying your solutions?
"Our solutions have demonstrated clear advantages when applied to real clinical trial projects. The biggest strength is the efficiency of clinical data management. By automating eCRF design and the data cleaning and verification processes that were previously manual, we significantly shortened overall timelines and reduced the possibility of human error. Our data submission capabilities, aligned with international standards like CDISC, have also allowed companies preparing for global regulatory submissions to respond more quickly and accurately. Cloud-based platforms like Maven Clinical Cloud provide an environment for multiple users to collaborate simultaneously, enhancing the real-time nature, traceability, and data security of clinical trials.
However, some limitations exist. For example, users accustomed to traditional methods might find new digital tools unfamiliar at first, requiring a certain level of training. Additionally, because system environments and processes vary between hospitals and research institutions, customization is sometimes needed. We are working to minimize these initial adaptation issues through user-centric UX improvements, enhanced onboarding education, and building a field-specific technical support system. We are also continuously improving features to increase practical usability and satisfaction."
- What is the biggest challenge you currently face?
"The biggest challenge JNPMEDI faces is consistently providing solutions that satisfy both user experience and regulatory requirements in a rapidly evolving environment. Because clinical trials directly impact patient lives, our solutions must be not only technologically superior but also trustworthy and compliant. Currently, the systems, user environments, and data structures used in various clinical trial sites differ, so a core challenge is to advance our solution's structure to be flexible and customizable for each environment. To this end, we are continuously improving product features based on real user feedback, and are promoting a modular design, user-centric UI/UX improvements, and strengthened technical support to accommodate complex industry demands. Additionally, we recognize that continuous acquisition of reliable training data and ensuring the explainability of AI are important challenges for applying AI technology in practical clinical trial operations. We are also strengthening our internal data governance and collaboration systems for these purposes."
- How does your solution manage patient data, including personal information?
“Security is our top priority. We maintain a robust security management system, having obtained major international certifications including ISO 27001 (Information Security Management System), ISO 27701 (Privacy Information Management System), and ISO 27799 (Personal Health Information Security Management). Our core solution, Maven Clinical Cloud, passed AWS’s Foundational Technical Review (FTR), ensuring we meet global-level data protection requirements even in a cloud environment.”
- As AI technology advances, the clinical trial paradigm is changing. How do you expect AI to impact clinical trials in the future?
“Until now, AI has played a supportive role, automating parts of the process like data entry or anomaly detection. In the future, I believe AI will play a core role from the design stage itself. Data-driven decisions will shape how trials are designed—determining how to accelerate high-potential trials or improve the success rate of risky ones. We are collecting diverse data types to design more precise clinical trials.”
- How do you see AI and cloud technology evolving in the next 1-2 years?
“Within the next one to two years, I expect AI technologies to become more practical and problem-solving oriented. In particular, Generative AI and AI Agents will go beyond simple automation to demonstrate higher productivity in the clinical domain.”
- How will AI and cloud technology evolve in this field over the next five years?
“Currently, AI assists in decision-making. However, as I mentioned, I expect it to evolve into a partner that understands macro goals and performs tasks independently. This is not a change that diminishes the human role, but rather one where AI, capable of performing repetitive tasks 24/7, offers greater efficiency, thereby giving humans the opportunity to focus more on 'tasks only humans can do.' Empathy, ethical judgment, and complex contextual interpretation still remain uniquely human domains, and we will continue to develop technology that does not harm these essential qualities."
- Any final words?
“While AI can replace people in some areas, we aim to create AI that extends and complements the capabilities of medical professionals. We focus on practical, trustworthy solutions that put clinical field problems at the center. All our technical efforts are aimed at building better medical infrastructure. I believe this will ultimately create an environment where more people can access medical benefits in a timely and cost-effective manner.”
✔️ To see the original article: https://www.newstheai.com/news/articleView.html?idxno=7862