Initially, AI emerged as a novelty in medical and healthcare sciences; however, it has gone way beyond the cognitive bias of human beings. This makes tech more ethical and apt for review, as it sticks to the guidelines. This takes us back to the origin of guidelines, when they were introduced to speed up the screening process; as people with different interpretations and various opinions claim authenticity, AI has standardised the official requirements for scientific content.
Why is MLR important?
MLR is important to check scientific accuracy, legal compliance, and regulatory requirements before it is made available to the public. Any interpretation, promotion, or statement that is beyond the boundaries of scientific, legal, and regulatory terms would be eliminated, as mentioned in the guidelines. This is obviously to hasten the whole process instead of restricting innovation and scientific temperament.
AI vs Human: Who is better at reviewing?
AI and humans can err, whether it's a typo or a concept, but the gap between AI’s occasional error and humans’ blunders is too huge. That said, comparing an LLM (Large Language Model) to a human brain is folly because you don’t expect a machine to be as sophisticated as a human brain; moreover, all existing AIs were built by humans.
The difference always lies in the consistency, accuracy, and exhaustive working nature of machines. A human brain, no matter how concentrated it is, would gradually fall short of precision because it is not made for endless tasks; rather, it is made for incisive creativity and innovation. By the same token, AIs are meticulous enough to replace humans in screening enormous amounts of data.
But when you see a firm reviewing its own science with its small medical affairs team, it is missing the advantage of real tech, or too fastidious about every detail to commit to a firm timeline. Honestly, it is just that they are wasting their brains on something wrong.
Is AI a hassle in scientific communications?
For many, it is. It is more common for business owners to utilise their keen eye when reviewing scientific articles. With constant meetings, numerous corrections and recurring revisions, owners and scientists hesitate to use an AI made by a tech company. However, it is not exactly what working with an AI sounds like.
With customised LLMs, you can be as flexible as collaborating with your own team, significantly more than your team!