The real-world applications of AI in healthcare - Med-PaLM

The real-world applications of AI in healthcare - Med-PaLM

We have seen the potential, the role, and the application of AI in healthcare in the previous blogs. In this series, we will see the real-world applications of the Generative AI (GAI) models that are customized for the healthcare domain. Advanced AI systems called Large Language Models (LLMs) have been trained to understand and produce language in a way comparable to that of humans. These models process and analyze text using deep learning techniques, enabling them to produce consistent and pertinent responses to the context. LLMs offer the potential to improve human-computer interactions and automate jobs in numerous sectors where language understanding and generation are essential.

Med-PaLM

Med-PaLM is a large language model (LLM) created to offer excellent responses to medical queries. It stands for Medical Pre-trained Language Model. It is trained using extensive medical literature, academic publications, electronic health records, and other healthcare information. Med-PaLM has the capacity to comprehend medical jargon, decipher intricate medical ideas, and produce pertinent comments or insights. In addition, Med-PaLM produces precise, beneficial long-from responses to consumer health issues, as determined by panels of licensed doctors and users. Medical documentation, electronic health records, medical education and research, and information retrieval are only some of the applications that Med-PaLM can be used for, in the healthcare industry.

Med-PaLM has the potential to improve efficiency, accuracy, and knowledge availability in numerous areas of healthcare delivery and research by making use of its extensive medical knowledge and language -generating capabilities. Recently, Google launched an upgraded model of Med-PaLM called Med-PaLM 2, which has an 18% leap in accuracy compared to its predecessor. Med-PaLM 2 achieved a staggering 86.5% accuracy rate on the United States Medical Licensing Examination (USMLE) questions, which is on par with the ‘expert’ test takers. Med-PaLM 2 could surpass the 60% passing threshold required for the examination.

Key capabilities

Med-PaLM has several key capabilities that make it valuable in the medical sector. Anatomy, illnesses, symptoms, treatments, drugs, and medical procedures are all included in the Med-PaLM encoding system for medical knowledge. Because of this expertise, the model can interpret and produce text unique to medical themes. By doing so, Med-PaLM plays a vital role in representing medical knowledge. The area of medical documentation is another area where Med-Palm is widely employed. Med-PaLM aids in producing thorough and accurate medical records. It can construct reports, automatically extract pertinent data from patient contacts, and help keep standardized terminology in Electronic Health Records (EHRs).

Additionally, Med-PaLM is useful in medical research and education. Medical students, researchers, and instructors can use the model’s extensive medical knowledge base and language-generating skills. To help in studying and comprehending complex medical ideas, Med-PaLM can offer definitions, justifications, and responses to medical questions. Condensing research papers, highlighting pertinent information, and extracting essential conclusions can help with literature reviews. Med-PaLM may also create hypotheses, recommend research topics, and promote evidence-based practice by giving users access to the most recent medical literature. Information retrieval in the medical industry may benefit from the use of Med-PaLM. It can efficiently search for and retrieve pertinent research articles, guidelines, clinical trials, and other sources of medical knowledge because of its capacity to process and comprehend medical content. This enables medical practitioners to obtain the most recent, scientifically supported information, fostering informed decision-making and improving patient care.

Limitations

Despite the fact that Med-PaLM 2 achieved state-of-the-art performance on several multiple-choice benchmarks for medical question answering and that human evaluation shows answers compare favorably to physician answers across several clinically important axes, more work needs to be done to ensure it is used safely and effectively. The ethical application of this technology will require careful thought, including thorough quality assessment when utilized in various clinical contexts with safeguards to reduce hazards in such circumstances. For instance, utilizing an LLM to determine a patient’s diagnosis or course of treatment carries significantly more risks than using an LLM to learn about a condition or drug. More research is required to evaluate LLMs used in healthcare for homogeneity and amplification of biases and security vulnerabilities inherited from base models. Another drawback is the potential for Med-PaLM to produce plausible yet inaccurate or deceptive information.

Sometimes, language models can produce responses that appear sensible but lack medical precision or evidence-based backing. Healthcare practitioners should use prudence and cross-reference the data supplied by Med-PaLM with reliable sources and their own experience. Additionally, Med-PaLM can have trouble handling sensitive patient data and upholding privacy. When adopting Med-PaLM or any other language model, appropriate safeguards must be in place to secure sensitive information because patient privacy and data security are crucial considerations in the healthcare industry.

In the next blog, we will explore additional applications like BioGPT and IBM Watson for Oncology further.

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