AI Researcher

Generative AI has become a driver of productivity throughout the country as a quick reference tool, but perhaps its capabilities of research can be utilized in a professional environment. Since the dawn of the information era and the introduction of the internet, research has become ever more accessible. Notably though, this access and connectivity has created new issues. 

While fifty years ago it was difficult to find applicable research because it was not available, today it is difficult to find research in an objective flood of information. With upwards of two million research articles published each year, it is a wonder academia is able to collect the information crucial to the continued and collaborative building of any field of study. Take, for example, the field of medicine. Research is fundamental to the existence of medical care. Study and advancement of the field is the driving force behind most all medical care institutions in the world. You may think that in the United States, a country with largely private healthcare coverage, that most hospitals are similarly for profit. In fact, the 

 are either government run or non-profit organizations. These hospitals conduct an incredible amount of research, driving the miracles of technology we witness in modern day: robotic surgery, corrective laser eye procedures, cutting edge drug invention. Yes, these products are developed privately, but without the infrastructure of research hospitals and academic institutions, there would be nowhere to deploy these new technologies. 

Zachary Vachal

The next medical miracle may be AI driven

The Role of Generative AI in Advancing Medical Research

Generative artificial intelligence is at its most basic only a predictive algorithm, but with modern improvements in complexity, it can be used for non-trivial problems. One of these areas of use is data analytics. Chat bots such as chatGPT can be given input data such as text, numbers, or even a pasted excel file and are able to output accurate and insightful analysis. There is little doubt that this function of generative AI is of great use in the medical field, perhaps most obviously in the practice of diagnosis.

What is a diagnosis? Although a seemingly trivial question, it prompts a perhaps more interesting question, what produces a diagnosis? When a patient exhibits symptoms of illness, a physician is charged with determining the most likely illness at hand and this is all a matter of data. Collecting information from a physical examination, assessing patient and family health histories, and considering current common health conditions are all in an effort to build a more complete database of information about a patient. After acquiring this data, it is a physician’s job to determine the most likely ailment, essentially a process of analyzing the available data. As it turns out though, this process of analysis is something generative AI can do remarkably well. Thus, there is clearly an opportunity for this technology to have a place in the diagnostic procedure.

In the past, doctors have been alone in their efforts to accumulate patient data into a diagnosis, but AI offers new insight.

The Doctor’s New Assistant: AI Analytics for Medicine

The advent of generative AI could be a game-changing development for the field of medicine. Advanced capabilities of artificial intelligence to summarize text and generate high-level abstractions may offer assistance in an age where information overload is a major impediment to providing the care. The field of medicine is on the cusp of a transformative era. The remarkable capabilities of artificial intelligence to summarize text and generate abstractions hold immense potential to address the challenges posed by information overload. Healthcare professionals and patients alike are grappling with an overwhelming volume of data, hindering efficient care delivery. In this article, we explore how the power of generative AI can alleviate these burdens, enhance patient interaction, and augment decision-making in the medical domain.

One of the most exciting outcomes of generative large language models such as GPT-3, underlying ChatGPT, is an astonishing ability to summarize text. Though this may seem like one of the most obvious uses of generative AI, the specific abilities of modern models offer unexpected capabilities such as contextual understanding. Given a complete text, ChatGPT can understand a text holistically, drawing connections between words, sentences, and paragraphs, but also between concepts and themes. The same model can perform these tasks in multiple languages and when fine-tuned (specifically trained) on data from a certain field, such as medical histories, produces summaries which are concise, informative, and of high quality.

Generative AI can provide incredible opportunities for advancement in the medical field via context-drive summarization

Posts by Zachary Vachal

AI Researcher

The Role of Generative AI in Advancing Medical Research

The Doctor’s New Assistant: AI Analytics for Medicine

Medical Information in the Era of AI