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From Code to Clinical Trials: Generative AI’s Role in Medical Writing

January 3, 2024
by Arvind Ramachandra Senior Vice President, Technology, Munish Singh AI/ML Solution Architect

The field of medical writing, characterized by the need for precision, expertise, and creativity, has historically been a demanding and intricate domain. However, recent advancements in generative artificial intelligence (AI) are ushering in a transformative era in medical writing. This article delves into the diverse ways generative AI is reshaping medical documentation, including accelerating clinical trial reports, streamlining regulatory content, and customizing patient education materials.

Each of the below sections addresses a specific aspect of how generative AI is impacting medical writing. Here’s a brief overview of each section:

Accelerating Documentation of Clinical Trials

At the heart of a medical writer’s responsibilities lies the meticulous documentation of clinical trials. Generative AI emerges as a formidable ally, capable of automating the synthesis of vital information from extensive datasets. Through the analysis of statistical outputs and the identification of safety signals, an AI assistant can accelerate the creation of clinical study reports, investigator brochures, and study protocols. This automated process not only saves significant time but also enhances accuracy by eliminating the potential for manual errors in data interpretation.
Illustrative Example:
Consider an AI assistant analyzing adverse event data from a phase 3 cancer trial. By automatically summarizing findings, it empowers medical writers to skip laborious manual reviews, enabling them to focus on the critical aspects of data interpretation and contextualization. This not only accelerates the documentation process but also allows writers to allocate more time to the nuanced and interpretative elements of their work.

Summary Report*

All the details on study of Exemestane with Celecoxib as Neoadjuvant Treatment in Postmenopausal Women. Here is a summary of the study examining the use of Exemestane combined with Celecoxib as a neoadjuvant treatment in postmenopausal women with breast cancer based on the provided documents: This study examined the use of Exemestane combined with Celecoxib as a neoadjuvant treatment in postmenopausal women with stage I, Ill and IV breast cancer. The study was registered on under the identifier NCT00201773 and was conducted by the Ohio State University Comprehensive Cancer Center. Rationale In postmenopausal women, the main source of estrogen is through the conversion of androgens, or sex hormones produced by the adrenal glands, into estrogens. This conversion is carried out by an enzyme called aromatase. Exemestane is an aromatase inhibitor which blocks the production of estrogens by inhibiting aromatase. Research indicates that the gene responsible for aromatase activity is CYP19. Therefore, Exemestane helps to inhibit aromatase activity through CYP019. Along with CYP019, another gene associated with breast cancer is an overexpression of COX-2 enzymes. Research suggests that COX-2 overexpression can cause cancer cell division, increased blood flow to tumors, and metastases. Celecoxib is a COX-2 inhibitor which blocks COX-2 activity. Compared to other non-steroidal anti-inflammatory drugs (NSAIDs), Celecoxib produces fewer side effects. The rationale of this study was to build on previous research to test the combination of Exemestane (an aromatase inhibitor) and Celecoxib (a COX-2 inhibitor) as a neoadjuvant treatment for breast cancer in postmenopausal women. Study Design This was a single center, open label, non-randomized interventional study. The primary purpose was treatment. The allocation was non-randomized. All participants received the treatment. The study used a single group assignment. There was no control group or randomization. The masking was open label, meaning the participants and researchers were aware of the treatment. Participants The study enrolled postmenopausal women aged 18 years and older with stage Il, Ill and IV breast cancer. Women who were pregnant or breastfeeding were excluded. A total of 11 patients were enrolled. Treatment Patients received oral Exemestane 25 mg once daily for 16 weeks. Starting at week 9, they also received oral Celecoxib 400 mg (two 200 mg capsules) twice daily for 8 weeks. Therefore, from weeks 9-16 patients received a combination of both Exemestane and Celecoxib. Several tests and exams were conducted throughout the study to monitor the patients, including a biopsy performed after the first 8 weeks on Exemestane alone. After 16 weeks on Exemestane and Celecoxib, patients underwent breast cancer surgery. Outcome Measures The primary outcome measure was to evaluate the number of patients with decreased gene expression of CYP19 in breast cancer by adding a COX-2 inhibitor (Celecoxib) to Exemestane. Gene expression was analyzed from tissue samples collected at the time of surgery after 16 weeks of treatment. Secondary outcome measures included evaluating the response rate to neoadjuvant Exemestane and Celecoxib using the Response Evaluation Criteria in Solid Tumors (RECIST 1.0). Response was evaluated by MRI and categorized as complete response, partial response, stable disease, or progressive disease. Safety and side effects were also monitored as secondary outcomes. Results The final results posted on in June 2015 stated that 11 patients were enrolled and completed the study. The primary outcome of decreased CYP19 expression with the addition of Celecoxib was not formally tested due to limitations in tissue analysis. For the secondary outcome of response rate, 7 patients achieved a partial response, 2 had stable disease, and 2 had progressive disease after 16 weeks of Exemestane and Celecoxib treatment. The combination of Exemestane and Celecoxib was well tolerated. The most common side effects were hot flashes, fatigue, headaches, and muscle aches. There were no serious adverse events reported. Conclusions In summary, this small single arm study examined the combination of the aromatase inhibitor Exemestane and the COX-2 inhibitor Celecoxib as a neoadjuvant treatment in 11 postmenopausal women with stage II-IV breast cancer. The primary outcome of decreased CYP19 expression could not be evaluated due to limitations in tissue analysis. The combination showed anticancer activity based on the secondary outcome of response rate, with 7 of 11 patients achieving a partial response. The combination was also well tolerated with manageable side effects. While the study was small and did not include a control group for comparison, the results provide preliminary evidence that combining Exemestane and Celecoxib may be an effective neoadjuvant therapy option for postmenopausal women with breast cancer. Larger randomized trials are needed to confirm these findings.
*A Summary Report on a Trial Study of Exemestane With Celecoxib as Neoadjuvant Treatment in Postmenopausal Women With Stage II, III, and IV Breast Cancer, Presented in a Comprehensive 3000-Word Document. In this illustrative example, the generative AI’s capability is showcased through the creation of a detailed summary report. The report pertains to a specific clinical trial study investigating the efficacy of a combined treatment approach using Exemestane and Celecoxib as neoadjuvant therapy in postmenopausal women diagnosed with Stage II, III, and IV breast cancer.
Key Components of the Example:
  • Study Details: The generative AI processes a wealth of data related to the clinical trial, including details about the study design, patient demographics, treatment protocols, and outcome measures.
  • Statistical Analysis: Leveraging its analytical capabilities, the AI extracts and interprets statistical outputs from the trial. This includes data on treatment efficacy, safety signals, and other relevant metrics crucial for medical writers.
  • Narrative Synthesis: The AI doesn’t just present raw data; it transforms it into a coherent narrative. This involves summarizing complex findings, identifying trends, and contextualizing the results within the broader landscape of breast cancer treatment.
  • Document Structure: The resulting document is a comprehensive report, meticulously organized and structured. It encompasses an introduction to the trial, methodology, results, discussion of findings, and potential implications. This structure aligns with the standards and expectations of clinical study reports.
  • Length and Detail: The report is crafted with a level of detail commensurate with the complexity of the clinical trial. At 3000 words, it provides an in-depth exploration of the study’s nuances, ensuring that medical writers have a robust foundation for their subsequent tasks.
Looking Ahead:

As generative AI continues to evolve, the acceleration of clinical trial documentation is likely to witness even greater strides. Future advancements might focus on refining AI algorithms to handle more intricate statistical analyses, providing medical writers with increasingly accurate and nuanced insights. The integration of real-time data from ongoing trials into the documentation process could become a reality, further enhancing the speed and comprehensiveness of report generation.

Streamlining Regulatory and Promotional Content

Generative AI proves invaluable in crafting regulatory documents and promotional content. In regulatory submissions, AI synthesizes complex data from preclinical and clinical studies into coherent narratives, ensuring compliance with organizational templates and guidelines. This expedites the submission process and enables writers to refine and polish drafts efficiently.
In the Realm of Promotional Content:
Generative AI remains abreast of the latest clinical practice guidelines, compensating for potential gaps in human knowledge. By automatically synthesizing information from the literature, it facilitates the creation of persuasive content at an unprecedented pace. Writers can then review and customize drafts, leveraging AI’s assistance in information synthesis and narrative formulation.
Looking Ahead:
The future of regulatory and promotional content creation could see generative AI evolving to interpret and apply dynamic regulatory frameworks seamlessly. AI tools may become adept at navigating the evolving landscape of compliance, ensuring that documents meet the latest standards effortlessly. Additionally, advancements might include AI-driven predictive analytics, assisting writers in anticipating regulatory requirements and proactively addressing them during the content creation phase.

Customizing Patient Education Materials

The customization of medical education materials for diverse audiences, including patients and caregivers, stands as a critical aspect of medical writing. Generative AI takes center stage in this domain, leveraging sociocultural data to tailor content based on demographics and health conditions. While writers construct the information architecture, the AI aids in precision-tuning messaging for greater relatability and impact.

For instance, an AI algorithm can adjust the reading level, terminology, and cultural references in a brochure on managing high blood pressure based on the patient’s age, ethnicity, and gender. This ensures that educational materials resonate with the target audience, thereby enhancing health literacy and treatment compliance. The ability to tailor content to specific demographics not only improves communication but also fosters a deeper understanding of medical information among diverse populations.

Looking Ahead:

Looking forward, generative AI’s role in customizing patient education materials is likely to become even more patient-centric. AI algorithms may evolve to not only adapt content based on demographics but also personalize information based on individual patient health records and preferences. The future may witness the integration of interactive elements, such as virtual assistants, to enhance patient engagement and comprehension.

The Future of AI-Assisted Medical Writing

While generative AI presents unprecedented opportunities for efficiency and access to up-to-date information, it is crucial to acknowledge the complementary nature of human expertise. AI tools function as valuable assistants, managing data synthesis and initial drafting, but human writers bring essential qualities such as curiosity, wisdom, and accountability to the table.

Looking Ahead:

As generative AI continues to advance, the medical writing field should proactively embrace these technologies, upskilling to responsibly leverage emerging tools. This agile adoption has the potential to transform current workflows, liberating writers from manual drudgery and allowing them to focus on higher-value analysis and creative expression. Additionally, it holds the promise of democratizing top-tier writing expertise, making it more accessible for underserved medical product markets.

In conclusion, the synergy of generative AI and medical writing marks a paradigm shift in documentation practices. As technology progresses and the scale of data and models in the life sciences domain expands, the impact of AI-assisted medical writing is poised to grow exponentially. By combining human creativity with technological prowess, this collaboration ushers in a new era of productivity, impact, and accessibility in the field of medical writing. The future promises not only increased efficiency in document creation but also a deeper understanding and communication of complex medical information across diverse audiences.


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