The Complete Guide to Using AI in the Healthcare Industry in Charlotte in 2025

Charlotte's health systems are treating AI as a practical tool, not sci‑fi - ambient scribing, image triage and chat assistants are already reshaping care: reporting shows DAX Copilot is licensed to over 1,500 Atrium clinicians and can save some doctors more than an hour a day, OrthoCarolina's Medical Brain cut post‑op messages by roughly 70%, and systems use models like Sepsis Watch and Viz.ai to prioritize life‑threatening cases; local coverage from North Carolina Health News article on AI in state healthcare systems catalogs these use cases while UNC Charlotte's new UNC Charlotte CLT AI Institute announcement signals a growing regional pipeline to train staff and govern tools - so Charlotte leaders can both reclaim clinician time and build accountable AI skills locally.

“It's imperative to have a human in the loop to make sure that we're properly evaluating these tools and making sure they are working for all different types of patients and doctors.” - Allison Koenecke, assistant professor of information science, Cornell University

Charlotte health systems are running AI across clinical and operational workflows rather than just piloting them: ambient scribing/autosummarization captures patient–clinician conversations so clinicians can focus on patients (Novant Health's DAX Copilot has been used by nearly 900 clinicians for more than 550,000 documented encounters and users report large after‑hours time savings and improved patient focus; see Novant's program details), AI now scans emergency images to flag life‑threatening findings (systems in North Carolina report tools that prioritize broken necks, brain bleeds and clots), surgical practices like OrthoCarolina use digital assistants to cut post‑op message volume by roughly 70%, and health systems including WakeMed and Atrium Health deploy AI to draft or triage patient‑portal messages and follow‑up tasks - reducing daily message load by a dozen messages per provider in some cases; these deployments aim to reclaim clinician time, speed triage for time‑critical conditions like stroke (Viz.ai alerts) and surface high‑risk patients (sepsis, suicide risk models), making a tangible difference in throughput and clinician well‑being across the region (coverage and examples summarized in the state reporting linked below).

“For me, the real life‑changer is the decreased burden of working memory. Most of us carry some part of 20 to 30 patient stories in our heads all day long… Not carrying this mental load is a game changer.” - Novant Health clinician

By 2025 AI in Charlotte is focused on high‑value clinical and operational tasks: image‑triage tools (Viz.ai and Aidoc) scan CTs and X‑rays in seconds to surface strokes, bleeds and fractures so neurologists and ER teams prioritize the sickest patients; cardiology algorithms (AI‑ECG/Viz HCM) flag occult hypertrophic cardiomyopathy earlier so patients reach imaging and specialists days to years sooner; ambient scribing and documentation assistants (Novant/Atrium DAX Copilot) and message‑drafting bots cut clinician after‑hours work and patient‑portal volume, while digital recovery assistants like OrthoCarolina's Medical Brain reduced post‑op messages roughly 70%; operational models predict OR times and optimize scheduling to lower per‑minute costs and reduce overtime.

These deployments matter because seconds and reduced inbox burden translate to concrete outcomes - faster stroke triage that can save minutes (and, per reporting, on the order of millions of brain cells) and measurable clinician time reclaimed for direct patient care (local examples compiled by North Carolina Health News report on NC health care harnessing AI, Novant Health's rollout of imaging AI and governance Novant Health announcement on Viz.ai stroke care deployment, and leader perspectives on time‑savings and outcomes in industry reporting)

“It's exciting to see the growing real‑world evidence showing how AI‑enhanced ECG analysis can play a pivotal role in identifying new patients with hypertrophic cardiomyopathy... By leveraging AI as a second set of eyes, we can expand the ability to diagnose more HCM patients earlier and across diverse populations...” - Milind Desai, MD, MBA (Viz.ai)

Regulatory pressure in North Carolina is shifting from experimentation to accountability: state leaders are preparing legislation and convening stakeholders while the North Carolina Medical Board already holds clinicians accountable for AI-driven decisions and documentation - providers “accept responsibility for responding appropriately to the AI's recommendations” and must review AI‑generated notes for accuracy, per the Board's medical records guidance (see the North Carolina Health News report on state oversight of AI in health care: North Carolina Health News report on state oversight of AI in health care).

Federal legislation remains limited, so local laws and system governance matter: Sen. Jim Burgin plans to introduce a bill addressing AI accountability while large systems (Duke, UNC) use internal vetting committees to mitigate bias risks highlighted by past incidents like the Duke sepsis algorithm review; the practical takeaway for Charlotte leaders is clear - documented review workflows and a governance committee aren't optional if institutions want to reduce liability and protect equity (see the North Carolina Medical Board guidance on documentation and AI: North Carolina Medical Board guidance on medical records, documentation, and AI).

“If it makes the wrong decision, where's the liability? Who's responsible?” - Sen. Jim Burgin (R‑Angier)

Charlotte's rapid uptake of predictive tools brings clear clinical gains but also concrete risks: training data that mirror unequal care can teach models to repeat delays, as the Duke review and reporting showed when slower blood‑test ordering for Hispanic children threatened to skew a pediatric sepsis predictor - an error that could erase early‑warning gains like Duke's Sepsis Watch, which reported a median 5‑hour lead time and an estimated 8 lives saved per month in early pilots (Duke Sepsis Watch implementation overview: Duke Sepsis Watch implementation overview).

Real‑world deployment studies reinforce that staged implementation, continuous monitoring and governance are essential to catch disparate performance before harm occurs - see a systematic review applying the SALIENT framework to sepsis predictors (Systematic review of SALIENT deployment for sepsis predictors: Systematic review of SALIENT deployment for sepsis predictors).

Tradeoffs reporting documents how algorithms trained on billing or delayed‑care patterns can entrench racial inequities and why transparency ('ingredient lists'), dedicated bias testing, and investment in smaller hospitals' monitoring capacity are needed to prevent downstream privacy and equity harms (Tradeoffs series on AI racial bias in healthcare: Tradeoffs series on AI racial bias in healthcare).

The practical takeaway for Charlotte leaders: early‑warning wins mean little unless accompanied by rigorous bias audits, data‑handling safeguards, and a staffed governance process that can intercept harmful model behavior before it affects a single patient.

“If you mess this up, you can really, really harm people by entrenching systemic racism further into the health system.” - Mark Sendak, lead data scientist, Duke Institute for Health Innovation

Charlotte's research ecosystem now pairs practical hospital partnerships with a deep university pipeline: UNC Charlotte's newly formed Charlotte AI Institute (CLTAI2) coordinates interdisciplinary centers and degree pathways that feed local health systems with trained talent and vetted research - see the UNC Charlotte AI Institute overview and leadership UNC Charlotte Charlotte AI Institute overview.

Key hubs include the AI4Health Center, which develops human digital twins and “hospital‑at‑home” monitoring for older adults and publishes advances accepted to top conferences, and the Center for TAIMing AI, which leads work on trustworthy, model‑risk frameworks for safe clinical deployment; both centers actively connect students, faculty and industry to translate models into care (AI4Health Center human digital twin research, Center for TAIMing AI trustworthy AI research).

Education and reskilling options - from an Applied AI graduate certificate and an AI Prompting certificate to an immersive Continuing Education AI Bootcamp and expanding computing/data‑science degrees - create a measurable pipeline so that Charlotte hospitals can staff AI governance committees and hire clinicians trained to audit models, not just operate them.

“UNC Charlotte‘s long-standing AI expertise positions the Charlotte AI Institute to draw assets from virtually all disciplines to meet rapidly expanding and competitive needs of the greater Charlotte region and beyond.” - John Daniels, vice chancellor for research

Begin with a tight, accountable plan: form an AI governance committee that includes clinical champions, data science, compliance, and an equity representative; run a single, high‑value pilot (documentation, in‑basket triage, or a chatbot for protocol lookup) with clear success metrics and a clinician feedback loop built into the contract and deployment.

Use established frameworks to govern the AI lifecycle - select vendors that provide transparency and an “ingredient list,” require logging and version control, and schedule continuous fairness and performance audits rather than one‑time checks (see practical governance guidance in the AI governance framework).

Prioritize education and reskilling so clinicians can audit outputs and raise issues; AMA programs and toolkits emphasize that feedback loops, data privacy, and thoughtful integration are top physician priorities and link adoption to reduced administrative burden and faster, safer workflows.

Start small, iterate quickly on clinician feedback, and codify monitoring and remediation steps so a pilot scales only after bias tests and operational controls pass.

The so‑what: a deliberate, governed pilot that centers clinician input converts early excitement into measurable time savings and safer care - without swapping one set of unchecked risks for another.

"This is when I say that the tech is there. It has moved way faster than we, as humans, have moved. And so over the next year or two, it's going to be time for us to slow down and really thoughtfully integrate." - Margaret Lozovatsky, MD, American Medical Association

Three concrete ways AI will change Charlotte healthcare by 2030 are already visible: first, automation of routine cognitive work - internal staff chatbots and documentation assistants will move protocol lookups and inbox triage out of clinicians' heads and into searchable, auditable workflows (internal staff chatbots for clinical workflows in Charlotte healthcare); second, operational AI will squeeze waste from costly processes - operating room duration prediction and smart scheduling can lower per‑minute OR costs and reduce overtime, directly improving margins and access (operating room duration prediction and smart scheduling savings in Charlotte); and third, the workforce will shift toward oversight and verification roles as hospitals hire clinicians who audit models and run governance programs rather than only using tools at the bedside, a transition that requires targeted reskilling and new career ladders (see pathways to reskilling into AI oversight roles for Charlotte healthcare workers).

The so‑what: Charlotte's large capital projects - like the planned 500‑bed NC children's hospital expected to open in the early 2030s - will amplify demand for scalable, audited AI workflows and trained model‑auditors so care expansions don't replicate the inbox chaos and staffing bottlenecks of today (Duke Health system timeline and project updates).

Charlotte practitioners and leaders can move from pilots to production by pairing proven vendors, local research hubs, and hands‑on training: clinical imaging and coordination platforms like Viz.ai enterprise imaging and stroke coordination now serves thousands of hospitals and offers clinical modules to speed stroke and cardiovascular workflows, while local success stories - OrthoCarolina's post‑op digital assistant reduced patient messages roughly 70% - demonstrate the operational payoff documented in state reporting (North Carolina Health News article on how NC providers harness AI).

For governance, workforce and translational projects, UNC Charlotte's AI4Health Center connects clinicians, students and vendors to build human digital twins, privacy‑preserving models and wearable sensing pilots that Charlotte systems can adopt and audit locally (UNC Charlotte AI4Health Center research page).

The practical takeaway: choose vendors with clinical evidence, pair them with local academic partners to run bias and safety audits, and enroll staff in targeted reskilling so pilots convert to reproducible time‑savings and safer, auditable care.

Charlotte health leaders should close the year by adopting a phased, accountable roadmap that pairs baseline audits and impact‑tiering with a formal AI governance body, staged clinician‑led pilots, continuous fairness and performance monitoring, and targeted reskilling - centering racial equity at every step rather than rushing deployments.

This approach reflects the “strategy‑over‑speed” playbook used in major health systems: start with data and workflow audits, classify tools by clinical risk, require vendor “ingredient lists” and explainability, pilot in a controlled setting with clinician feedback loops, then scale only after bias tests and real‑world monitoring pass.

Governance must specify who owns model outcomes and updates, mandate logging/version control and post‑deployment audits, and fund smaller hospitals' monitoring capacity to avoid widening inequities; these are the practical guardrails highlighted in governance roadmaps and equity plans that North Carolina systems are already adapting.

For workforce readiness, invest in short, practical reskilling so clinicians can audit models and run governance (for example, the AI Essentials for Work 15‑Week Bootcamp - Nucamp Registration trains staff to use AI tools and write effective prompts), and link that training to seats on the governance committee so pilots produce measurable time savings and safer care instead of hidden harms.

Execute this plan to preserve patient trust, reduce liability, and turn early AI wins into durable, equitable improvements in Charlotte.

“Leaders must shift from urgency to intentionality: adopt strategy‑over‑speed to unlock AI's potential sustainably, ethically and with global impact.” - World Economic Forum