Google's strongest big language model in the medical field, Med-PaLM 2 PK ChatGPT-4

1. Introduction

With the development of artificial intelligence technology, its application in the medical field is becoming more and more extensive. Large Language Models (LLMs) such as Med-PaLM 2 and ChatGPT-4 play a key role in this area, providing high-quality answers to medical questions. Both models are designed with different intentions: Med-PaLM 2 focuses primarily on answering medical questions, while ChatGPT-4 is a general-purpose language model that can handle all types of problems. In this article, we will take a closer look at Med-PaLM 2 and compare it to ChatGPT-4.

Introduction of Med-PaLM 2

2.1 Design and objectives of Med-PaLM 2

Med-PaLM 2 is a large-scale language model designed by Google to provide high-quality answers to medical questions. It leverages the capabilities of Google's large language models that have been adapted for the medical field and evaluated through medical exams, medical research, and consumer queries.

2.2 Performance and implementation of Med-PaLM 2

Med-PaLM 2 is Google's Large Language Model (LLM) specifically designed to provide high-quality answers to medical questions. In the medical field, the performance of this model is excellent, and its answers are judged to be accurate and useful by the evaluation panel of professional doctors and users. On U.S. Medical Licensing Test (USMLE)-style questions, Med-PaLM 2 achieves 86.5% accuracy.

The development and evaluation of Med-PaLM 2 involves multiple steps. First, Google uses the capabilities of its large language models and aligns them with the medical field, evaluating them through medical exams, medical research, and consumer queries. Med-PaLM 2 is then evaluated through a benchmark called MultiMedQA, which combines a dataset of seven question responses covering professional medical examinations, medical research, and consumer queries. In addition, the model's ability to answer long answers was tested, including scientific facts, accuracy, medical consensus, reasoning, bias, and likelihood of possible harm, all assessed by clinicians and non-clinicians from a variety of backgrounds and countries.

During the training phase, the model learns from experts by answering a long list of medical questions and scenarios, mimicking the way a panel of clinicians from the UK, US, and India responds. The clinicians then cross-referenced the model's answers based on a range of criteria, including low probability of medical harm, degree of scientific consensus compliance, precision, and lack of bias.

Still, the model has its limitations. For example, although the model understands the logic of science and medicine, its understanding of ethics or morality is not complete. To improve this, the model was further trained to help align it with human ethical values.

The practical application of Med-PaLM 2 is still in its early stages. Google plans to first seek user feedback on its performance through the Google Cloud open model. More features may be added in the future, such as understanding medical records, CT scans, or genomic data.

2.3 How to evaluate the quality of answers to Med-PaLM 2

The evaluation of the quality of the answers for Med-PaLM 2 is very positive. In the clinician's review, the Med-PaLM 2 answers reflect clinical and scientific consensus, low likelihood of misunderstanding, accurate reading comprehension, correct knowledge recall, correct reasoning, only relevant content, no missing important information, and no demographic bias. It's worth noting, however, that the technology is still in its early stages, and despite its impressive performance, doctors don't need to worry about losing their jobs yet.

2.5 Potential impact and significance of Med-PaLM2

The potential impact and significance of Med-PaLM 2 lies in its ability to answer medical questions with medical expert-level knowledge. This capability could be used in a variety of applications, such as helping doctors make diagnoses or giving the average consumer more accurate medical information. However, this also poses some challenges, such as how to ensure that AI's answers are accurate and safe, and how to deal with ethical issues, which are not very good at dealing with moral or ethical issues.

3. Comparison of Med-PaLM 2 and ChatGPT-4

Training data and objectives: Although ChatGPT-4 and Med-PaLM 2 are both large language models based on the Transformer architecture, their training data and objectives are different. Med-PaLM 2 is specifically designed and trained to answer medical questions. To achieve this, it uses a series of specialized medical question answer datasets for training, including specialized medical exam questions, medical research, and consumer queries. In contrast, ChatGPT-4's training data is derived from a large amount of text from the internet, and it is not specifically trained for a certain domain, but is designed to deal with a variety of topics and types of problems.

2. Ways to answer questions: Both Med-PaLM 2 and ChatGPT-4 understand and generate language to answer questions. However, due to the different training data and areas of focus, they answer questions differently. The strengths of Med-PaLM 2 are understanding symptoms, interpreting patient test results, and performing complex reasoning to determine possible diagnoses, tests, or treatments. ChatGPT-4, on the other hand, can handle a wider range of issues and reason in more context.

Performance and accuracy: Med-PaLM 2 achieves an accuracy rate of 86.5% on USMLE (United States Medical License Examination) style questions, which is a very high accuracy higher than that of ChatGPT-4. That's because the Med-PaLM 2 is specifically trained to answer medical questions. However, the performance of these models is not just about accuracy. For example, ChatGPT-4 generates meaningful and relevant responses in a broader context, which is also an important performance metric.

4. Application areas: Although Med-PaLM 2 and ChatGPT-4 are both large language models, their application areas are different. Med-PaLM 2 is primarily designed as a medical question answering system, while ChatGPT-4 is designed as a more general question and answer and conversation system that can be used on a variety of topics and domains.

4. Conclusion

Overall, both Med-PaLM 2 and ChatGPT-4 are powerful large-scale language models capable of providing high-quality answering of questions. However, they differ significantly in design and application, with Med-PaLM 2 outperforming ChatGPT-4 in dealing with medical problems, while ChatGPT-4 is a general-purpose language model capable of handling various types of problems. Therefore, when choosing which model to use, it should be decided according to the specific application scenario and needs.