Debunking 3 AI Myths for Life Sciences

Artificial intelligence (AI). It’s everywhere, and it’s changing the world as we know it. From chatbots to self-driving cars, AI is here to make our lives easier AND take our jobs. Right? Well…not exactly, our jobs are safe for now.

Like many other verticals, there is great promise attached to AI for the biopharmaceutical industry and reasonably, a healthy dose of hesitation. When it comes to leveraging AI technology, everyone from

HCPs to behind-the-scenes researchers or content authors can benefit from valuable time-saving assistance and automated capabilities. And, when you give life sciences writers a tool like structured component authoring (SCA), the possibilities are endless.

AI is intimidating, but it doesn’t need to be. Today, we’re exploring how SCA solutions will change the game for biopharmaceutical organizations and we will debunk three major life sciences AI myths.

What is SCA?

Structured component (or content) authoring solutions are software tools or platforms designed to facilitate the creation, management, and publication of structured content or components. With SCA, content can be broken down into discrete, reusable elements, such as paragraphs, sections, citations, images, tables, and more, and organized or combined in various ways to create different documents or outputs.

SCA can improve content creation processes

For biopharmaceutical organizations, SCA can replace traditional desktop platforms like Microsoft Word, for a writing or documentation process that is smoother and better suited for an environment with multiple authors, reviewers, revisions, and iterations. Designed specifically for use in life sciences, the right SCA solution will also enable better auditing and version control, not to mention help adhere to compliance/regulations.

SCA and AI just make sense for the biopharmaceutical industry

SCA is a step toward delivering consistent, compliant, and streamlined content for everyone, from health authorities and patients to health practitioners. The integration of AI technology will only further enhance the creation of life sciences content by streamlining human steps needed to reach final publication or distribution. So why are folks so resistant to the idea of AI?

Three myths about AI

In the field of life sciences, there are several myths and misconceptions about AI that can impact how technologies are perceived and adopted. Below are three common myths:

Myth 1: AI will replace human experts completely

Self-driving cars are cool (ok, depending on who you ask), but we are nowhere near replacing all our human-requiring cars. Technological hurdles, regulatory and legal complexities, necessary infrastructure upgrades, and the need to address human behavior in mixed traffic are significant factors. Additionally, high costs, ethical dilemmas, and unforeseen challenges further contribute to the gradual pace of adoption.

One common myth is that AI will completely replace human expertise in life sciences. While AI has the potential to enhance decision-making and streamline certain tasks, it’s unlikely to replace the critical thinking, creativity, and nuanced understanding that human experts bring to complex scientific research, diagnosis, and treatment. AI is more valuable as a tool that augments human capabilities — not fully replaces them.

Myth 2: AI can solve any problem instantly

AI is sort of like a magic 8 ball: outlook good, but vague when you think about it.

There’s a misconception that AI is a magical solution that can quickly and effortlessly solve any problem in life sciences. In reality, human intelligence is the basis of artificial intelligence. AI tools are only as good as the humans inputting the data! Systems require careful training, validation, and tuning to perform effectively and access to high-quality, relevant data. The process of training and refining these models can be time-consuming and resource-intensive. And not all problems are suitable for AI solutions! The effectiveness of AI depends on the quality of data and the problem’s complexity.

Myth 3: AI can make objective decisions without bias

Remember how we just touched on AI only being as good as the humans inputting the data?

The fact of the matter is AI isn’t ready to make unsupervised decisions. Though many people think because it’s driven by algorithms, AI is unbiased or objective; its systems are only as unbiased as the data they are trained on. If the training data contains biases or reflects systemic inequalities, the AI can perpetuate or even amplify those biases. In life sciences, this can have serious implications, such as biased diagnostic or treatment recommendations — human objectives will continue to play an important role in the future of biopharmaceutical work for many years to come.

SCA gives humans the best of both worlds

Science is — and always will be — a field where new discoveries and technologies deepen our understanding and abilities to understand the human experience. Though many have reason to be hesitant, it’s important to approach AI in life sciences with a balanced understanding of its capabilities and limitations. While AI has transformative potential to drive advancements, improve efficiency, and aid decision-making, it will not replace the value of human expertise in scientific research and healthcare.

Technologies like SCA that leverage automation and other AI capabilities can help life sciences to make those breakthrough discoveries or augment the work they are doing. At Docuvera, we believe that SCA has the power to revolutionize the content authoring process — especially when it is built to do just that. Ready to learn more? Schedule a call with an expert today.

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