Impact of Large Language Models in the Medical Industry

March 2, 2024 3 min read By Cogito Tech. 114 views

Large language models (LLMs) utilize computational artificial intelligence (AI) for producing language that is akin to human language. LLMs acquire training on vast quantities of text derived from the Internet for answering queries, providing summaries and creating stories or poems. The keywords are offered by the user for the LLMs to produce text on the given topics. One can also request a specific type of text which includes simplified language or poetry.

In the medical world, LLMs have immense potential as communication plays an important role in patient care. Interpreting human spoken language accurately is a key factor in influencing the success of communication. It plays a vial role in establishing rapport between patient and caregivers ensuring patient satisfaction and enabling optimal clinical outcomes. Written communication is also used for communicating between medical professionals regarding patients which include diagnostic reports and therapeutic procedures, results, etc.

Patient reports that lack clarity directly result in inferior quality of patient care. Moreover, ineffective communication among healthcare providers places a great deal of economic burden on clinical institutions and healthcare systems. Hence, LLMs can play a vital role in enhancing patient outcomes, medical research and medical education.

Five Key Ways LLMs Enhance the Medical Industry

1. Imparting Medical Knowledge: LLMs can offer medical students with personalized teaching assistance, interactive learning simulations, simplifying complex concepts and assistance with diagnoses and treatment.

2. Translating and Summarizing Text: LLMs help in communicating with patients by translating medical terms into various languages, helping with clinical decision-making, adhering to therapy, and clinical documentation. It can offer structured formats for unstructured notes resulting in reduction in workload for clinicians.

3. Simplifying Documentation: LLMs assist with scientific content production, summarization of scientific concepts and helping scientists and clinicians with testing hypotheses and visualizing large datasets. LLMs can simplify medical language and are very handy in addressing social concerns like sexually transmitted diseases.

4. Flagging Misinformation: LLMs can also flag issues like misinformation, privacy, biases in training data and potential misuse of it too.

5. Chatbots: Chatbots (First Derm and Pahola) offer guidance to doctors for assessing and guiding patients with skin issues and alcohol abuse.

Limitations of Large Language Models in the Medical Industry

Limitations of Large Language Models in the Medical Industry

1. Spread of Misinformation: The main limitation of LLMs is spread of misinformation especially in clinical settings. Hence, it is necessary that a legal framework is set up for its use in clinical practice.

2. Lack of Understanding: LLMs lack high-level problem-solving or logical reasoning as although they can imitate human conversations, their understanding is only limited to word associations. For example, a LLM might suggest ibuprofen and ibuprofen to patients with complaints of migraines, but it’s only because these medicine names and the word ‘migraine’ appear together in its training data.

3. Hallucinations: This happens where LLMs produce nonsensical or incorrect responses. Since LLMs produce responses based on training data and user’s prompts, the response generated can be misleading as the training data they are trained with can be of low quality (invalidated web and book corpora) and the prompts can be misleading resulting in hallucinations.

4. Lacks transparency: LLMs neither store or reference their training data when compared to traditional search engines or databases. In fact, they convert these data into mathematical representations which obscures the origin of their outputs making it impossible to trace back the source.

5. Inconsistency in output: Owing to the LLMs design there is a tendency to generate inconsistent response to the same prompt in various sessions. This makes it volatile leading to fluctuations in its efficacy. Also, this makes the process of translating results from academic studies to real world clinical settings.

Apart from the above, LLMs pose a world of ethico-legal implications which need to be carefully considered. For instance, there is ambiguity with respect to legal liability in scenarios where patient suffers harm due to LLM’s recommendations. There is also significant risks involved when it comes to patient data and privacy with publicly available LLMs. Hence, LLMs pose challenges in the medical field like bias, validity, safety, and ethics. These must be carefully attended to prior to its widespread adoption in LLMs in Clinical Trials.

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