LLMs (Large language models) are changing medical landscape

It’s not the technology that is holding implementation back but rightfully the extensive regulatory constraints that mark any medical decision making and PII data.

𝗬𝗲𝘁 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗮𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗮𝗽𝗽𝗲𝗮𝗿. 𝗢𝗻 𝘀𝘂𝗰𝗵 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝗠𝗘𝗗𝗜𝗖, 𝘁𝗵𝗶𝘀 𝘁𝗶𝗺𝗲 𝗳𝗿𝗼𝗺 𝗨𝗔𝗘.

It measures 5 clinical dimensions for LLM for provide:
Medical Reasoning: This dimension focuses on the LLM’s ability to engage in clinical decision-making processes. This encompasses interpreting medical data, formulating potential diagnoses, recommending appropriate tests or treatments, and providing evidence-based justifications for its conclusions.

Ethical and Bias Concerns: This dimension addresses the crucial issues of fairness, equity, and ethical considerations in healthcare AI. It examines the LLM’s performance across diverse patient populations, assessing for potential biases related to race, gender, age, socioeconomic status, or other factors.

Data and Language Understanding: This dimension evaluates the LLM’s proficiency in interpreting and processing the variety of data and language found in clinical settings. This includes understanding medical terminologies and jargon, interpreting clinical notes, lab reports, imaging results, and handling both structured and unstructured medical data

In-Context Learning: This component examines the model’s adaptability and capacity to learn and apply new information within a specific clinical scenario. This includes incorporating new guidelines, recent research findings, or patient-specific information into its reasoning

Clinical Safety and Risk Assessment: This dimension focuses on the LLM’s ability to prioritize patient safety and manage potential risks inherent to clinical settings. This encompasses identifying and flagging potential medical errors, drug interactions, or contraindications.

Those dimensions were tested across 4 types of tasks:
Closed-ended questions: These assess the LLM’s comprehension of medical concepts and ability to provide specific answers. Examples include multiple-choice questions similar to those found in medical licensing exams

Open-ended questions: These evaluate the LLM’s reasoning and explanatory skills in more realistic clinical scenarios. They assess the model’s capacity to synthesize information and generate appropriate responses without relying on pre-defined answer choices

Summarization tasks: These gauge the LLM’s ability to process large amounts of medical data and generate concise, accurate summaries of clinical information

Note creation exercises: These test the LLM’s proficiency in generating coherent and accurate clinical documentation, including tasks like creating SOAP notes from patient dialogues or case information.

Ranking the models accordingly will derive a preference and benchmark.