The Promise Of CRISPR GPT: Specialised ChatGPTs Could Transform Medicine

We’ve recently come across some fascinating developments in the field of gene editing: CRISPR GPT, a large language model agent designed to automate the design of gene-editing experiments. This got us thinking: what other ‘specialty GPTs‘ could we think of? What highly specific applications might there be for large language models (LLMs)?

CRISPR GPT is a large language model similar to ChatGPT, but it’s been trained on a very specialised dataset focused on gene editing and CRISPR technology. This makes it exceptionally effective in that specific area, understanding the nuances of gene editing, identifying potential errors or risks, and suggesting optimal experimental designs. However, unlike ChatGPT, which is designed to handle a broad range of general questions, CRISPR GPT may not perform well on general queries outside its gene-editing domain. 

The genius of this idea is using a large language model’s ability to handle vast amounts of data in gene editing. Researchers must sift through massive databases to identify suitable gene sequences, understand potential side effects, and optimise the CRISPR system. An LLM like GPT can analyse this data, identify patterns, and make suggestions, significantly accelerating and enhancing the research process.

So, what other fields could benefit from this technology? Where else in medicine do professionals need to interact with, analyse, and interpret enormous amounts of complex data?

At pharmaceutical companies

GPT for drug discovery

Drug-GPTs could quickly go through massive datasets of molecular structures, chemical properties, and biological activity to predict how different molecules might interact with potential drug targets. This could significantly speed up identifying promising drug candidates and optimising their structures, accelerating the development of new medications.

GPT for protein engineering

Protein engineering is another field ripe for innovation with GPT. These models can assist in developing new proteins with specific functions, which are crucial in biotechnology and therapeutic applications. By simulating protein folding and interactions, GPT can help design proteins that perform desired tasks, potentially leading to breakthroughs in medical treatments and industrial processes.

GPT for antibody design

Antibody-GPTs could generate novel antibody sequences for therapeutic use, enhancing the immune response or targeting specific pathogens with high precision. This capability could lead to the rapid development of new treatments for various infectious and autoimmune diseases.

GPT for clinical trial design

Trial-GPT can streamline the design of clinical trials by analysing biomedical literature and patient data to identify optimal trial designs, patient cohorts, and potential biomarkers. Which means faster development of new treatments and improved efficiency of clinical research.

In medical practices and hospitals

GPT for personalised medicine

Doctor-GPTs digest patient data in seconds, including genetic information, medical history, lifestyle factors, and environmental exposures. This can help identify individual risk factors, predict disease susceptibility, and tailor treatment plans to each patient’s unique genetic makeup. With such a personalised approach, patients could receive the most effective therapies based on their genetic and health profiles. Thinking a bit ahead, such LLMs could help us move medicine towards prevention, instead of treatments.

GPT for 3D printing in medicine

Special 3D-printing LLMs could help design intricate scaffolds for tissue engineering, optimize printing parameters for different materials, and even predict how printed structures will interact with biological tissues. This could lead to more precise and personalised medical devices and potentially even accelerate the development of bioprinted organs for transplantation.

GPT for cancer genomics

A Cancer-GPT could analyse large-scale genomic data from cancer patients to identify the specific mutations driving tumor growth and predict how tumors will respond to different treatments. This could lead to more targeted therapies and improve the chances of successful treatment for cancer patients.

GPT for surgical planning

Surgeon-GPTs could assist surgical planning for complex procedures. They could analyse patient data and medical imaging, simulate different surgical approaches, determine the best strategies, minimize risks, and improve patient outcomes.

For everyone

GPT for neural network simulations

LLMs could be used to create detailed simulations of neural circuits, helping researchers understand how neurons communicate, how neural networks process information, and how different brain regions interact. These simulations could also help identify how disruptions in neural circuits lead to neurological disorders like Alzheimer’s, Parkinson’s, and epilepsy. Ultimately, this could lead to new treatments for these debilitating conditions.

GPT for nutritional science

A Nutrition-GPT could assist in nutritional science by analysing dietary data, medical history, and genetic information to create personalised nutrition plans. This can help individuals achieve better health outcomes through tailored dietary recommendations based on their specific needs and conditions. What nutrigenomics currently lacks to deliver on its promises could maybe come from large language models. 

GPT for epidemiology

The Covid-pandemic was first spotted by Bluedot, an AI company in Toronto. As the AI arena just got more advanced in the past few years, the field of epidemiology can handsomely benefit from its development. Pandemic-GPT could analyse data from various sources to predict disease outbreaks, track the spread of infectious diseases, and evaluate the effectiveness of public health interventions. This can help public health officials make informed decisions and implement timely measures to control epidemics.

Specialised GPTs are easier to image than to create

These are just a few examples of the many ways in which large language models could help medicine. Of course, imagining these specialised GPTs is far easier than creating them and ensuring they work reliably enough for use in healthcare. Rigorous testing, validation, and regulatory approval will be essential before these models can be deployed in real-world medical settings.

However, thinking about potential use cases is never a waste of time. Such thought experiments highlight that there will hardly be a stone left unturned in healthcare by these changes. The new potential brought by specialised GPTs will improve the care patients can receive, enhance the work doctors can do, advance medical research, and streamline the logistics behind healthcare.