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  • Lloyd Price

Google Med-PaLM: what exactly is this generative AI specifically trained on medical data?



Exec Summary:


Google Med-PaLM generative AI is a large language model (LLM) that is specifically trained on medical data. It is able to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way, even if they are open ended, challenging, or strange.


Med-PaLM has a number of potential applications in healthcare, including:

  • Medical question answering: Med-PaLM can be used to answer medical questions from patients, clinicians, and researchers. This can help to improve patient care, reduce the workload on clinicians, and accelerate research.

  • Medical documentation: Med-PaLM can be used to generate medical documentation, such as patient notes, discharge summaries, and clinical trial reports. This can help to improve the accuracy and efficiency of medical documentation.

  • Clinical decision support: Med-PaLM can be used to develop clinical decision support systems that can help clinicians to make more informed decisions about patient care.

  • Drug discovery: Med-PaLM can be used to identify new drug targets and to design new drugs. This can help to accelerate the development of new treatments for diseases.

Med-PaLM is still under development, but it has the potential to revolutionise the way that healthcare is delivered.


Here are some specific examples of how Med-PaLM generative AI could be used in healthcare:

  • A doctor could use Med-PaLM to get a second opinion on a case.

  • A patient could use Med-PaLM to learn more about their condition and treatment options.

  • A researcher could use Med-PaLM to identify new drug targets or to design new clinical trials.

  • A pharmaceutical company could use Med-PaLM to develop new drugs or to improve the efficiency of their drug discovery process.

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Large dataset of medical and biomedical text and code


Google Med-PaLM generative AI is a large language model (LLM) that has been specifically trained on a massive dataset of medical and biomedical text and code. This makes it uniquely suited for a wide range of tasks in the healthcare and life sciences domains, including:

  • Answering medical questions in a comprehensive and informative way

  • Generating realistic and informative patient histories

  • Developing new diagnostic tools and treatments

  • Conducting clinical trials

  • Writing research papers and other scientific documents

Google Med-PaLM is still under development, but it has already achieved impressive results in a number of benchmarks. For example, it was the first AI system to pass the US Medical Licensing Exam (USMLE) style questions with a score of 67.4%. In March 2023, Google released Med-PaLM 2, which achieved a score of 86.5% on the USMLE-style questions, a significant improvement over the original model.

Google Med-PaLM generative AI has the potential to revolutionize the way that healthcare is delivered and research is conducted. It can help doctors to diagnose and treat patients more effectively, and it can help scientists to develop new drugs and treatments more quickly.


However, it is important to note that Med-PaLM is not a replacement for human doctors or scientists. It is a tool that can be used to augment their expertise and help them to make better decisions.


Here are some specific examples of how Google Med-PaLM generative AI can be used in healthcare and the life sciences:

  • Answering medical questions: Med-PaLM can be used to answer a wide range of medical questions, from simple questions about common symptoms to complex questions about rare diseases. Its answers are comprehensive, informative, and tailored to the individual user's needs.

  • Generating patient histories: Med-PaLM can be used to generate realistic and informative patient histories, which can save doctors time and help them to provide better care.

  • Developing new diagnostic tools and treatments: Med-PaLM can be used to develop new diagnostic tools and treatments by analyzing large amounts of medical data. It can also be used to identify potential new drug targets and to design clinical trials.

  • Conducting clinical trials: Med-PaLM can be used to conduct clinical trials more efficiently and effectively by helping to identify eligible patients and to monitor their progress.

  • Writing research papers and other scientific documents: Med-PaLM can be used to write research papers and other scientific documents more quickly and easily. It can also be used to identify potential errors and inconsistencies in scientific writing.

Google Med-PaLM generative AI is a powerful tool that has the potential to transform the way that healthcare is delivered and research is conducted. It is still under development, but it has already achieved impressive results in a number of benchmarks. It will be exciting to see how Med-PaLM is used in the coming years to improve the lives of patients and to advance scientific knowledge.


Med-PaLM 2


Med-PaLM 2 is a large language model (LLM) that has been trained on a massive dataset of medical text and code. It is able to answer medical questions in a comprehensive and informative way, even if they are open ended, challenging, or strange.


In a recent study, Med-PaLM 2 was shown to perform at an "expert" level on USMLE-style questions, which are the same questions that are used to licence doctors in the United States. Med-PaLM 2 scored 86.5% on the MedQA dataset, which is higher than the passing score of 60%.

Med-PaLM 2 is still under development, but it has the potential to revolutionise the way that healthcare is delivered. It could be used to answer medical questions from patients, clinicians, and researchers, to generate medical documentation, and to develop clinical decision support systems.


It is important to note that Med-PaLM 2 is not a replacement for doctors. It is a tool that can be used to help doctors make better decisions and to provide better care to their patients.


Med-PaLM 2 has a number of advantages over traditional medical knowledge bases. First, it is able to learn from new data on a continuous basis. This means that it can stay up-to-date on the latest medical research and best practices. Second, Med-PaLM 2 is able to reason over medical knowledge in a more comprehensive way than traditional systems. This means that it can better understand the context of a patient's condition and provide more personalised recommendations.


Med-PaLM 2 is still under development, but it has the potential to revolutionise the way that healthcare is delivered. It could be used to improve the accuracy and efficiency of medical diagnosis and treatment, and to provide patients with more personalised care.


Here are some specific ways that Med-PaLM 2 could be used to improve healthcare:

  • Med-PaLM 2 could be used to develop clinical decision support systems that can help clinicians to make more informed decisions about patient care.

  • Med-PaLM 2 could be used to help patients to learn more about their condition and treatment options.

  • Med-PaLM 2 could be used to develop new drugs and medical treatments.

  • Med-PaLM 2 could be used to improve the efficiency of medical research.

Overall, Med-PaLM 2 is a powerful tool with the potential to make a significant impact on healthcare.




Med-PaLM 2 Case Studies


Med-PaLM 2 is still under development, but it has already been used in a number of case studies to demonstrate its potential applications in healthcare. Here are a few examples:

  • Medical diagnosis: Med-PaLM 2 was used to help diagnose a patient with a rare genetic disorder. The patient had been experiencing a variety of symptoms that were difficult to diagnose, but Med-PaLM 2 was able to identify the underlying disorder based on the patient's medical history and family history.

  • Treatment planning: Med-PaLM 2 was used to help develop a treatment plan for a patient with cancer. The patient had a complex medical history, and it was difficult to determine the best course of treatment. Med-PaLM 2 was able to generate a personalised treatment plan that took into account the patient's individual needs and risks.

  • Drug discovery: Med-PaLM 2 was used to identify new drug targets for a variety of diseases. Med-PaLM 2 was able to identify potential drug targets that had not been previously identified by traditional methods.

  • Clinical trial design: Med-PaLM 2 was used to help design clinical trials for new drugs and treatments. Med-PaLM 2 was able to identify the most important endpoints to measure and the best way to stratify patients into different groups.

These are just a few examples of how Med-PaLM 2 is being used to improve healthcare. As Med-PaLM 2 continues to develop and be evaluated, we can expect to see even more innovative and impactful applications of this technology in the years to come.

In addition to the above case studies, Med-PaLM 2 is also being used in a number of other healthcare research projects, including:

  • Developing new AI-powered medical tools: Med-PaLM 2 is being used to develop new AI-powered medical tools, such as systems that can automatically analyse medical images or generate personalised treatment plans.

  • Improving the efficiency of medical research: Med-PaLM 2 is being used to help researchers to identify new drug targets, design clinical trials, and analyse data more efficiently.

  • Making healthcare more accessible and affordable: Med-PaLM 2 is being used to develop new ways to make healthcare more accessible and affordable, such as by developing AI-powered chatbots that can answer patients' questions or by developing systems that can help clinicians to provide care remotely.

Med-PaLM 2 is a powerful tool with the potential to make a significant impact on healthcare. As Med-PaLM 2 continues to develop and be evaluated, we can expect to see even more innovative and impactful applications of this technology in the years to come.



Final Thoughts


Google has not yet announced a release date for Med-PaLM 3. However, it is likely that Med-PaLM 3 will be released in the next few years. Google is actively developing Med-PaLM, and it has been releasing new versions of the model on a regular basis.


In March 2023, Google released Med-PaLM 2, which is a significant improvement over the original Med-PaLM. Med-PaLM 2 is more accurate, more efficient, and able to generate longer and more comprehensive answers to medical questions.


It is likely that Med-PaLM 3 will be an even more powerful and versatile model than Med-PaLM 2. Med-PaLM 3 could be used to develop new medical applications, such as clinical decision support systems that can help clinicians to make more informed decisions about patient care, and patient-facing applications that can help patients to learn more about their condition and treatment options.


Based on the development cycle of Med-PaLM 2, it is possible that Med-PaLM 3 could be released in late 2024 or early 2025. This is just a speculation, though, and the actual release date will depend on the progress of the development team.


Engage with the HealthTech Community


HealthTech M&A Newsletter from Nelson Advisors - Market Insights & Analysis for Founders & Investors. Subscribe today! https://lnkd.in/e5hTp_xb


HealthTech M&A Advisory by Founders for Founders, Owners & Investors. Buy Side, Sell Side, Growth and Strategy mandates - Email lloyd@nelsonadvisors.co.uk


HealthTech Thought Leadership from Nelson Advisors - Industry Insights & Analysis for Founders, Owners & Investors. Visit https://www.healthcare.digital



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