AI language models are transforming the medical writing space – like it or not!

In an era of rapid technological progression, artificial intelligence (AI) language models have emerged as transformative forces, significantly altering traditional workflows and methodologies across many fields, including medical writing.

It will allow us to truly leverage their potential, comprehend their capabilities and limitations, and integrate them effectively into our writing processes.

The next-generation AI language model will be trained on x amount more data or are tailored towards a more specific subject, additionally claiming enhanced speed, relia - bility, and accuracy.

Understand AI language models as behind the scenes – Terminology

A large language model (LLM) is a deep learning technique, and a subset of machine learning, that uses artificial neural networks to patterns, and guide decision-making. Through extensive training on massive datasets, LLMs develop an unparalleled capacity to recognise, comprehend, predict, and generate novel content spanning myriad domains.

The term generative AI (genAI) refers to all AI tools that use LLMs to primarily create content such as images (e.g., Midjourney or Stable Diffusion), text (e.g., GPT-4, PaLM, or Claude), code (e.g., Copilot), or audio (e.g., VALL-E or resemble.ai) in response to short prompts.

Tokens can be as small as individual characters or punctuation symbols, or as large as words or even whole sentences, depending on the model and tokenisation method (e.g., rule-based, statistical, neural). This process of breaking down text into individual units is called tokenisation.

Modern LLMs and hence genAI tools successfully apply transformer architecture. Two key features define transformers: the encoder-decoder structure and the attention mechanism. The encoder processes the input data and generates a set of context vectors. Using these vectors, the decoder generates the output by selecting the token with the highest probability in a sequence of tokens. The attention mechanism, a crucial element in transformers, assigns a weight to an input token, guiding the model on where to focus during output generation

Sophisticated prompt engineering, which involves giving specific and detailed instructions to guide the model in its decision-making or prediction process, can enhance a model’s performance. Depending on the task, methods such as few-shot prompting, which provides a few input-output examples, chain of thought prompting (CoT), which uses sequential prompts to encourage reasoning or guide the model through complex tasks, and prompt iteration, have been found to be most successful in elevating a model’s performance.

Limitations of AI language models

performance consistently improves, it is important to remember that their output lacks true comprehension, critical thinking, or consciousness.

generated texts have a tendency to be lengthy and articulate replies that could potentially include plausible but inaccurate or biased information.

It is also essential to address privacy concerns when using text generation.

Embracing an open mind and adopting a trial-and-error approach will facilitate exploration, learning, and the development of an AI-driven mindset.

Future-proof yourself and let AI amaze you!

AI language models and applications are set to reshape the medical writing space, redefining traditional workflows and methodologies in the process. Navigating this shift requires an open attitude, curiosity, and a commitment to continuous learning

Our unique human abilities – understanding context, strategic thinking, critical evaluation, and conveying nuanced emotions – remain invaluable.

Harness the potential of these AI tools to boost your productivity and elevate the quality of your work instead of fighting them

Can genAI assist medical writing?

Yes it can. GenAI, and ChatGPT in particular, can be used to assist with many tasks, including:

Paraphrasing and reformatting references to different styles?

Rewriting materials or methods sections and explaining statistical tests

Rewriting abstracts and extracting article highlights

Summarising scientific articles or medical information for various audiences

Repurposing available information for different formats and various audiences

Creating educational and other training materials such as courses, webinars, presentations, hand-outs

Generating responses to customer inquiries and scripts for chatbots/virtual assistants and much more

Conclusions

In simple terms, these models are mathematical functions supported by powerful computing capabilities, but they cannot think.

These applications can truly make a difference by saving time, streamlining workflows, and potentially enhancing the quality of the resulting outputs.

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