Sean Davis seandavi

Constructive Use of AI in Clinical Reasoning: Transitioning to Residency with Reflection and Safety

1. Why AI Matters

Artificial intelligence (AI), including large language models (LLMs), is becoming a daily companion in medicine — from documentation to diagnostic reasoning. Used thoughtfully, AI can enhance learning and efficiency. Used uncritically, it risks deskilling, never-skilling, and mis-skilling.

Opportunity	Risk
Off-loads routine cognitive work	Overreliance → weaker independent reasoning

Frontier of AI & Medicine, October 2025 Christina Olson [email protected]

Self-Directed Learning Exercises: AI Tools for Clinical Care

Clinical Decision Making

*For this exercise, ensure that you do not enter PHI or other details that potentially could identify specific patients or our institution in Open Evidence or ChatGPT, as these are not secure systems. It is ok to

Connecting

Start duckdb, either on the command line or in R, etc. To connect to and then use the biodatalake ducklake, do the following inside the duckdb connection:

install httpfs;
load httpfs;
.read 'https://store.cancerdatasci.org/ducklake_config/ducklake_ro_connect.sql'

prelim

To use the live-html (webr/pyodide) in quarto:

quarto add r-wasm/quarto-live

Template for use

Powerpoint from code

Using code for a powerpoint is a useful approach to have chatgpt and other LLMs create powerpoint slides from documents or a textual description.

Usage

pip install -r requirements.txt

Nextflow on Google Cloud at CU Anschutz

Nextflow is a powerful workflow management system designed for creating scalable and reproducible scientific workflows. It enables you to write workflows in a declarative language, making it easy to define complex pipelines that can be executed on various platforms, including local machines, clusters, and cloud environments like Google Cloud.

This short tutorial is meant for informatics users who are comfortable with a command line interface. It also assumes that the user is familiar with and has run nextflow on a local computer or HPC system.

Roughly, this document will walk through:

	#' Workshop cost calculator
	#'
	#' This cost calculator give the minimum cost for a workshop of the
	#' requested size, number or participants, and hours.
	#'
	#' @param vcpu numeric() amount of virtual cpu requested (can be fractional)
	#' @param memory numeric() in GB the amount of requested memory
	#' @param hours numeric() the number of hours for each instance to run
	#' The workshops will run for this amount of time even if a participant
	#' is not using it. So, every launch results in a cost.

	---
	title: "Github Repos for Topic"
	author: "Sean Davis"
	format: html
	params:
	gh_topic: "r01ca230551"
	---

	## Required packages

	# Install Bioconductor and required packages
	# This command installs the BiocManager package for managing Bioconductor packages
	install.packages("BiocManager")

	# Use BiocManager to install specific packages for data analysis and visualization
	BiocManager::install(c("GEOquery", "SummarizedExperiment", "ggplot2",
	"party", "ggparty", "partykit", "randomForest"))

	# Load necessary packages for modeling and visualization
	library(party) # For creating classification trees

	#!/bin/bash
	curl https://gis.cdc.gov/Cancer/DataVizApi/GetJSON/USCS_County \| sed -e 's/<string xmlns="http:\/\/schemas.microsoft.com\/2003\/10\/Serialization\/">//g' -e 's/<\/string>//g'\| jq -c '.[] \| .USCS_County[]' > output.jsonl