Companies in the life sciences industry, those with concentrations in pharma, biotech, and medical devices, invest significant time and resources in developing scientific content for regulatory submissions, educating the scientific community and patients, training internal teams, and differentiating themselves from competitors.
In an era where misinformation is prevalent, these companies must swiftly and consistently produce high-quality, targeted content. Beyond drug discovery and manufacturing, content creation is a core activity across various departments, including marketing, medical affairs, research and development, regulatory, and pharmacovigilance. Essentially, life sciences companies create large volumes of content.
The process of crafting compliant, high-quality content is complex and demanding, especially in a highly regulated industry. There is often more work than available personnel, and even when writing is outsourced, it requires time to supervise and review agency outputs. Budget constraints and layoffs further limit resources for content development.
Writing is just one facet of the content development workflow. Content creators must educate themselves, sift through an ever-growing volume of scientific publications, draft outlines, collaborate with stakeholders, and review drafts — tasks that are time-consuming and labor-intensive.
Given these challenges, innovative life science companies are turning to Artificial Intelligence (AI) for scientific content generation. AI is not only a technological advancement but a strategic necessity. As the industry discovers new products, the demand for efficient, accurate, timely, and cost-effective content development is crucial for success.
Generative AI has the potential to revolutionize how scientific data is processed, analyzed, and presented, pushing life sciences leaders and scientists to rethink their content generation strategies.
The role of gen AI in scientific content generation
AI-powered tools can be utilized in all phases of content creation, enabling faster and more accurate generation of scientific documents. The manual creation of scientific content can take weeks or even months. AI drastically reduces this time by automating repetitive tasks and providing data-driven insights that accelerate the writing process.
For example, AI can significantly cut down the time needed to search literature databases, draft outlines, and review content. AI writing assistants are invaluable for paraphrasing, writing titles, checking spelling and grammar, changing tone, generating plain language summaries, seamless citation generation, and language translations—tasks that are often time-consuming or require external expertise.
Examples of AI-generated content in life sciences
Life sciences companies can leverage AI to create diverse content types, including clinical study reports, regulatory submissions, slide presentations, posters and abstracts, marketing materials, journal articles, medical information letters, training materials, patient information leaflets, and plain language summaries. Announcements about using AI for drug discovery generate more attention.
However, companies are also using AI in other areas. Recently, Moderna announced a collaboration with OpenAI to integrate AI across all departments and business processes. Agencies that produce clinical study reports and other content for life sciences companies are also adopting AI tools, further highlighting its versatility.
Impact of AI on content creation cost
The cost of generating scientific content can be substantial. Developing a slide presentation can cost between $20,000 and $60,000 when outsourced to an agency. Life sciences companies spend millions annually on content development. AI can help mitigate these costs by automating many aspects of the content creation process. Experts estimate that generative AI tools can reduce the time to write a clinical study report by nearly half, improving the speed of regulatory submissions by 40% while significantly reducing costs across regulatory teams.
Moreover, AI enhances content quality by minimizing human errors and ensuring consistency across documents. This quality improvement can save costs by reducing the need for extensive revisions and rework.
Concerns about Using AI in scientific content generation
Several concerns must be addressed to ensure the effective use and adoption of AI tools in scientific content-generation workflows. These concerns include accuracy, data safety and privacy, the availability of fit-for-purpose solutions, the cost of implementation, and the learning curve associated with using AI tools effectively.
- AI accuracy: AI systems rely on algorithms and data inputs, which have the potential to lead to errors or misinterpretations. Ensuring the accuracy of AI-generated content is critical, particularly in fields requiring precision, such as biotech or pharma. With human oversight and guided prompts, AI can produce accurate outputs comparable to those of subject-matter experts.
- Data safety and privacy concerns: AI systems require access to large datasets, raising concerns about the safety of sensitive or proprietary information. Companies can mitigate risks by restricting AI use to non-sensitive data and employing models that do not train on proprietary information. Robust data protection measures, like encryption and compliance with privacy regulations such as GDPR or HIPAA, are essential for safeguarding data.
- Fit-for-purpose AI solutions: Generic AI models alone are often insufficient for creating life sciences content. Companies should collaborate with life science-specific AI vendors to develop tailored solutions that integrate into existing workflows. Thorough evaluations ensure AI tools align with organizational needs and effectively support content generation processes.
- Cost of implementation: Deploying AI involves expenses for software, infrastructure upgrades, and maintenance, requiring a cost-benefit analysis to assess ROI. Scalable and cloud-based AI solutions, along with pilot projects, can reduce upfront costs and test suitability before full implementation. Most companies cannot afford bespoke large language models, making scalable solutions more practical.
- Training and workforce development: Successful AI adoption requires employees to gain skills through comprehensive training programs. Fostering a culture of continuous learning with workshops, online courses, and seminars is key to equipping teams to leverage AI. Cross-functional collaboration and celebrating AI-driven successes can enhance adoption and effectiveness.
- Job displacement concerns: While AI may replace certain tasks, it cannot replicate human experience, strategic thinking, or judgment. Instead of replacing jobs, AI enhances professional capabilities and creates new opportunities. Workers proficient in AI are more likely to succeed than those who resist leveraging it effectively.
Key to revolutionizing content generation workflows
The integration of AI in the life sciences content generation presents a transformative opportunity to enhance efficiency, accuracy, and productivity. Leaders and scientists in this industry must take proactive steps to integrate AI into their content generation workflows.
This involves investing in the right AI solutions, ensuring data privacy and security, and fostering a skilled workforce ready to embrace technological advancements. By doing so, companies can increase efficiency, focus more on strategy and innovation, and maintain a competitive edge. The question is no longer whether AI should be used, but rather how to effectively integrate AI into content development workflows.
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