Unlocking the Potential of AI in Healthcare: How Generative Pre-training Transformer Models (like ChatGPT) will Change Healthcare
These models have the potential to improve the speed and accuracy of medical diagnosis, accelerate drug discovery, and even generate personalised medical advice.
Generative pre-training transformer (GPT) AI models, such as ChatGPT, have a significant potential to impact the field of healthcare in several ways. These models have been trained on vast amounts of data, allowing them to generate human-like text and understand natural language. This makes them well-suited for a variety of natural language processing (NLP) tasks, including extracting information from electronic medical records, assisting with medical coding, and even generating personalized medical advice.
GPT AI is a type of artificial intelligence that is based on the transformer architecture and is trained using a technique called generative pre-training. This approach involves training the model on a large amount of data, and then fine-tuning it on a specific task. The transformer architecture, which was introduced in the 2017 paper "Attention Is All You Need," is a neural network architecture that is well-suited for processing sequential data, such as natural language.
GPT AI models are trained on vast amounts of data, allowing them to generate human-like text and understand natural language. This makes them well-suited for a variety of natural language processing (NLP) tasks, including language translation, question answering, and text generation. These models have been trained on large amounts of text data, such as books, articles, and websites, allowing them to learn the patterns of language.
Transformer architecture is a type of neural network used for processing sequential data, such as natural language. The main idea behind transformer architecture is the use of attention mechanisms, which allow the model to selectively focus on different parts of the input data. This allows the model to better understand the relationships between different words or sentences in the input data.
One of the key features of transformer architecture is the use of self-attention mechanisms, which allow the model to weigh the importance of different parts of the input when making predictions. This is done by computing a set of attention scores, which indicate how much each part of the input should be considered when making a prediction.
Another feature of transformer architecture is the use of something know as multi-head attention, which allows the model to attend to different parts of the input data simultaneously. This allows the model to better understand the relationships between different words or sentences in the input data.
One of the main advantages of generative pre-training transformer AI models is their ability to fine-tune on specific tasks with a small amount of labelled data. This allows the model to adapt to new tasks quickly and with high accuracy. This property makes them useful for a variety of tasks, such as language translation, question answering, and text generation.
However, there are challenges associated with GPT AI models. One of the main challenges is the sheer amount of data required to train these models. GPT AI models require vast amounts of data, which can be expensive and time-consuming to collect.
That said, in HealthTech there is an abundance of data, but the challenge here of course is it’s not easily accessible as it often sits in data silos within proprietary vendor systems. Interoperability and sharing of data is something that still has not be addressed sufficiently. Additionally, there are also concerns about the ethical implications of using AI, such as potential biases in the data used to train these models and the potential loss of jobs for human professionals.
One area where GPT AI models could have a significant impact is in drug discovery. These models can be used to predict the properties of potential drug compounds, such as their efficacy and potential side effects. This could greatly accelerate the drug discovery process and lead to the development of new treatments for a variety of diseases. Additionally, these models could be used to analyse large amounts of data from clinical trials, helping researchers identify new insights and potential biomarkers.
Another area where these models could have an impact is in medical diagnosis. These models could be trained on a vast amount of medical data, allowing them to assist doctors in making more accurate diagnoses. They could also be used to generate personalised medical advice, taking into account a patient's individual medical history and symptoms.
ChatGPT and other generative pre-training transformer AI models can be used to extract important information from electronic medical records, such as patient demographics, medical history, and treatment plans. This can help healthcare organisations more efficiently process patient data, leading to more accurate diagnoses and improved patient care.
Medical Coding: ChatGPT and similar models can be used to assist with medical coding, which is the process of assigning codes to medical diagnoses and procedures to facilitate billing and data tracking. These models can help healthcare providers more efficiently code patient information, reducing errors and making the billing process more accurate.
ChatGPT and other models can be trained on vast amounts of medical data, allowing them to assist doctors in making more accurate diagnoses. By analysing patient data and symptoms, these models can help doctors identify patterns and make more informed decisions about treatment.
ChatGPT and similar models can be used to predict the properties of potential drug compounds, such as their efficacy and potential side effects. This could greatly accelerate the drug discovery process and lead to the development of new treatments for a variety of diseases.
ChatGPT and other models could be used to generate personalised medical advice for patients, taking into account their individual medical history and symptoms. This could help patients better understand their condition and make more informed decisions about their treatment.
Recently, ChatGPT has received a lot of attention for its capabilities as a large language model (LLM) and its potential applications in AI. However, while ChatGPT has been in the spotlight, research teams at Google and DeepMind quietly released a paper on their development of an open-source LLM tool called Med-PaLM. Unlike ChatGPT, which is trained on a large variety of datasets to serve as a general natural language tool, Med-PaLM was specifically designed to answer medical questions, both from medical professionals and patients.
However, it is important to note that there is still much research to be done in these areas and it is not clear how widely these applications will be adopted in healthcare. For example, if large language models (LLMs) are not integrated with digital health infrastructure, such as electronic medical records (EMRs), it will greatly hinder their scalability and usage in clinical practice because:
EMRs store a wealth of information about patients including their medical history, demographics, medications, lab results, and more. Without integrating LLMs with EMRs, the models would not have access to this information, making it difficult for them to provide accurate and relevant information to healthcare professionals.
EMRs are widely used in clinical practice, and healthcare professionals rely on them to make informed decisions about patient care. Without integration with EMRs, LLMs would not be able to contribute to the decision-making process and would not be able to provide value to healthcare professionals.
EMRs are used to track and store patient information over time, which allows for the identification of patterns and trends in patient health. Without integration with EMRs, LLMs would not be able to analyse this data and would not be able to provide insights into patient health.
EMRs are used to track patient information across different healthcare providers, which allows for continuity of care. Without integration with EMRs, LLMs would not be able to access this information and would not be able to provide continuity of care.
EMRs are used to store patient data across different healthcare systems, which allows for the sharing of information between different healthcare providers. Without integration with EMRs, LLMs would not be able to access this information and would not be able to provide comprehensive care to patients.
Integrating LLMs with digital health infrastructure, such as electronic medical records, is crucial for their scalability and usage in clinical practice because it allows LLMs to access a wealth of patient data, contribute to the decision-making process, provide insights, continuity of care, and comprehensive care to patients.
However, there are several concerns about the ethical implications of using AI in healthcare which will impact the widespread adoption such as biases in the data used to train these models. These concerns stem from the fact that AI models are only as good as the data they are trained on, and if the data used to train the models contains biases, these biases will be reflected in the models' predictions and decisions.
One concern is that AI models trained on healthcare data may perpetuate existing biases in the healthcare system. For example, if a model is trained on data that contains a disproportionate number of patients from certain ethnic or socioeconomic backgrounds, it may be more likely to make incorrect predictions or decisions for patients from other backgrounds.
Another is that AI models may perpetuate existing disparities in healthcare access and outcomes, particularly for marginalised groups. For example, if a model is trained on data that contains a disproportionate number of patients from certain geographic areas, it may be less effective at making predictions or decisions for patients from other areas.
There is also concerns that AI models may perpetuate existing biases in the healthcare system, particularly in the areas of diagnosis and treatment. For example, if a model is trained on data that contains a disproportionate number of patients with certain conditions, it may be less effective at making predictions or decisions for patients with other conditions.
Despite these challenges, the potential benefits of using generative pre-training transformer AI models in healthcare are significant. These models have the potential to improve the speed and accuracy of medical diagnosis, accelerate drug discovery, and even generate personalized medical advice. As such, it is likely that we will see a growing number of healthcare organisations begin to adopt these models in the coming years.