Over the past year, there has been a surge of interest in advanced artificial intelligence tools such as ChatGPT. However, it is crucial to recognize the key advantage of Large Language Models (LLMs) in enhancing natural language processing capabilities, a field with a long history, despite the recent surge in medical applications involving these models.
Healthcare institutions necessitate AI solutions capable of comprehending a patient’s complete medical record or an entire Electronic Health Record (EHR) system. Generative AI tools are well-suited to support these requirements.
We had a discussion with Kim Perry, the Chief Development Officer at emtelligent, a medical Natural Language Processing (NLP) company, regarding the efficacy of generative AI, large language models, their correlation with Neural Learning Networks (NLP), the potential of “medical-grade AI” in improving patient outcomes, and its role in mitigating clinician burnout.
Q. There is misinformation circulating about the role of relational AI and large language models in powering natural language processing, a distinct form of AI. Can you elaborate on this relationship?
A. The widespread unfamiliarity with ChatGPT and relational AI just a year ago is astounding. Presently, over 100 million individuals, spanning consumers and various industries, have embraced ChatGPT globally. However, misconceptions often stem from initial impressions and superficial research on emerging technologies. Despite this, many individuals and organizations lack awareness of generative AI and significant language models, leading to inevitable confusion.
Generative AI and the latest advancements in LLMs are not as sophisticated as traditional natural language processing. Nevertheless, the emergence of generative AI and LLMs is shaping what we term “medical-grade AI” or NLP. In contrast to conventional NLP, medical-grade AI can unlock the vast 80% of medical data currently buried in unstructured text, with LLMs being specifically trained on scientific content.
The potential of medical-grade AI to process and interpret millions of records is poised to revolutionize healthcare delivery at the point of care and across the entire medical spectrum, benefiting not only providers but also payers, pharmaceutical firms, life sciences, and scientific researchers.
Q. While AI tools like ChatGPT excel in responding to specific queries, you argue that healthcare organizations require systems capable of understanding comprehensive patient charts or EHR systems. Could you provide further insights on this?
A. GenAI is proficient in generating rapid, albeit speculative responses. However, tools like ChatGPT often fabricate information or “hallucinate,” which is unacceptable in critical decision-making scenarios at the point of care.
Providers cannot afford to rely on untrustworthy information, as it could lead to a loss of confidence in the system. Moreover, genAI inundates users with keyword-driven data, complicating data retrieval for clinicians.
What healthcare providers truly need are tools that can not only retrieve and understand patient data but also contextualize it within a single chart or across an EHR system. Traditional NLP has fallen short in this aspect, failing to grasp the entirety of unstructured data.
Medical-grade AI leverages the advancements in LLMs and machine learning to empower healthcare organizations to extract value from unstructured data like free-text notes. For instance, medical-grade AI can decipher medical terminology that standard NLP systems struggle with, including acronyms and specialized jargon.
Furthermore, the scalability of medical-grade AI in processing vast amounts of health data is crucial as the volume of healthcare information continues to expand.
Q. How does “medical-grade AI” contribute to enhancing patient outcomes?
A. Medical-grade AI plays a pivotal role in improving patient outcomes by providing clinicians with timely and easily digestible information.
When clinicians lack access to organized patient data, such as details on prior procedures, critical conditions, or severe medication allergies, diagnostic and treatment errors may occur, leading to adverse outcomes.
By generating automated patient background summaries, medical-grade AI equips clinicians at the point of care with comprehensive insights into a patient’s health status, particularly beneficial during initial patient consultations.
To address clinicians’ trust issues with AI tools, medical-grade AI links individual summaries to the original data in the hospital’s records, allowing clinicians to verify information sources and contextualize data accurately.
Q. How does “medical-grade AI” help mitigate professional burnout?
A. Healthcare professionals often spend excessive time sifting through patient information, causing stress and detracting from patient interaction.
Medical-grade AI, with features like context-sensitive search and integrated summaries, significantly streamlines workflows, enabling practitioners to deliver more effective and efficient care while striking a balance between thoroughness and productivity.
By alleviating the burden of information retrieval, better workflows contribute to reduced stress among professionals, allowing them to focus on patient care rather than administrative tasks. With enhanced tools at their disposal, clinicians can operate more efficiently and effectively, ultimately benefiting both healthcare providers and patients alike.
