Generative A.I. in SPC: Unlocking New Potential while Tackling the Risks
Title: How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory studyAuthors & Year: Megahed, F.M., Chen, Y.J., Ferris, J.A., Knoth, S., and Jones-Farmer, L.A. (2023)Journal: Quality Engineering [DOI:10.1080/08982112.2023.2206479]Review Prepared by David Han Statistical Process Control (SPC) is a well-established statistical method used to monitor and control processes, ensuring they operate at optimal levels. With a long history of application in manufacturing and other industries, SPC helps detect variability and maintain consistent quality. Tools like control charts play a central role in identifying process shifts or trends, allowing timely interventions to prevent serious defects. Megahed, et al. (2023) explores how generative AI, particularly ChatGPT, can enhance the efficiency of SPC tasks by automating code generation, documentation, and educational support. While AI shows promise for routine tasks, the study also highlights its limitations in handling more complex challenges. For instance, ChatGPT’s misunderstanding of…
![Flow chart showing the steps of the LLM approach for clinical prdeiction. The top left is part a with a Lagone EHR box connected to 2 boxes, NYU Notes(clinical notes) and NYU Fine-Tuning (clinical notes and task specific labels). The top right is the pret-training section with NYU notes (clinical notes) in the far top right connected to a language model box which then connects to a larger masked language model box below it (fill in [mask]: a 39-year-old [mask] was brough in by patient (image representing llm replying) patient)). The bottom left has nyu fine tuning (clinical notes and task specific tasks) in the top right of its quadrant connected to a pretrained model box which connects down to a larger fine-tuning box, specifically the predicted p(label) ground truth pair (0.6, 0.4) which connects to a small box inside the big on lageled loss which goes to another small box labeled weight update which goes back to the pretrained model box. The last quadrant in the bottom right has two boxes on the left of fine-tuned model and hospital ehr(clinical notes) connected to inference engine in the top right of the quadrant that connects down to the email alert (physician) box](https://mathstatbites.org/wp-content/uploads/apollo13_images/msbHanNyu1-7ipft2sg6himqf4p6irp5l74aclvk4e4w6.png)
The A.I. Doctor is In – Application of Large Language Models as Prediction Engines for Improving the Healthcare System
Predictive Healthcare Analytics
Physicians grapple with challenging healthcare decisions, navigating extensive information from scattered records like patient histories and diagnostic reports. Current clinical predictive models, often reliant on structured inputs from electronic health records (EHR) or clinician entries, create complexities in data processing and deployment. To overcome this challenge, a team of researchers at NYU developed NYUTron, an effective large language model (LLM)-based system, which is now integrated into clinical workflows at the NYU Langone Health System. Using natural language processing (NLP), it reads and interprets physicians’ notes and electronic orders, trained on both structured and unstructured EHR text. NYUTron’s effectiveness was demonstrated across clinical predictions like readmission (an episode when a patient who had been discharged from a hospital is admitted again), mortality (death of a patient), and comorbidity (the simultaneous presence of two or more diseases or medical conditions in a patient) as well as operational tasks like length of stay and insurance denial within the NYU Langone Health System. Reframing medical predictive analytics as an NLP problem, the team’s study showcases the capability of LLM to serve as universal prediction engines for diverse medical tasks.