The Complete Guide to Using AI in the Healthcare Industry in Virginia Beach in 2025

AI is moving from promise to practice across Virginia, and Virginia Beach is a local hotspot: the recent DOMA and Livanta merger AI-enabled health‑tech hub centers an AI-enabled health‑tech hub that plans facility expansion and will apply machine learning to quality oversight and vast unstructured records, while statewide conversations - like the UVA AI in Health Care Symposium on AI in health care - stress that these tools can cut clinician paperwork (ambient assistants that populate notes), speed diagnoses, and personalize care, but only with strong governance and human oversight; Virginia Tech's work on AI plus the Internet of Behaviors underscores real gains in remote monitoring and early detection.

For leaders and clinicians in Virginia Beach, practical upskilling matters: Nucamp's 15-week AI Essentials for Work course teaches prompt writing and hands‑on AI skills that help teams safely translate models into time saved and better patient encounters.

“Success in the AI age, the principles will be the same as in any era of human achievement. You need compassion, you need leadership, you need thoughtfulness, you need discipline and discipline in teamwork, and you also need luck.” - Dr. Girish Nadkarni

The market signal is urgent: AI spending and capability are surging, and that surge creates concrete opportunity for Virginia Beach health systems to cut costs, speed diagnoses, and reduce clinician paperwork now rather than later.

Global forecasts show AI climbing into the high hundreds of billions - Precedence Research estimates roughly USD 757.6 billion in 2025 - with North America already a major share of that growth, while generative AI (a key driver for clinical summarization and automation) was valued at USD 21.3 billion in 2024 and is projected to expand rapidly (Precedence Research artificial intelligence market forecast, GMI generative AI market report).

Stanford's 2025 AI Index underscores the pace and stakes - widespread adoption, record private investment (about USD 109.1 billion in U.S. private AI investment in 2024), and 223 FDA approvals of AI-enabled medical devices by 2023 - so the question for local leaders isn't whether AI will matter, but how quickly to pilot responsibly to capture workflow gains and protect patient safety (Stanford 2025 AI Index report).

Think of it like preparing for a storm: the infrastructure and training investments made this year determine whether a health system weathers disruption or rides the wave to better, faster care.

“Outlook for this market is being affected by rapid changes in trade relations and tariffs globally.”

Building the local pipeline for AI-ready clinicians and technologists in Virginia Beach leans heavily on Eastern Virginia Medical School's practical, ethics‑first approach: EVMS' AI Executive Advisory Workgroup has published comprehensive AI guidelines and resources from EVMS that cover clinician‑specific usage, data control, human oversight and training pathways, while the CareForward Curriculum redesign at EVMS (in partnership with Old Dominion University) accelerates hands‑on clinical exposure - earlier clerkships, more time for clinical skills and dedicated ultrasound and capstone experiences - to make AI‑augmented workflows meaningful at the bedside (CareForward Curriculum at EVMS).

Institutional listening has shaped these efforts: the AI readiness surveys found 143 responses with 78.2% overall satisfaction and a clear call - over half of respondents - for more role‑specific content, a reminder that training must be granular (clinician, student, researcher) not one‑size‑fits‑all.

For researchers and trainees, EVMS also supplies detailed research and student generative AI usage guidance from EVMS emphasizing transparency, privacy, and continual validation - tools that turn promise into safe, measurable practice rather than speculative hype.

High-impact, near-term AI use cases for Virginia Beach health systems cluster around smarter imaging, bedside decision support, and clinician unburdening: AI-driven ultrasound that automates landmark detection and guides needles - now cleared for U.S. clinical use in RIVANNA's Accuro 3S and SpineNav‑AI - can raise first‑attempt success and standardize spinal procedures across variable patient anatomies (RIVANNA Accuro 3S and SpineNav‑AI 510(k) clearance); AI guidance also lets non‑experts capture diagnostic‑quality lung ultrasound clips - 98.3% success in a multicenter study - opening triage and remote‑review workflows that expand imaging capacity without waiting for specialists (AI‑guided LUS study).

Complementary advances - AI that enhances cardiac MRI images without contrast and point‑of‑care tools that compute real‑time ejection fraction - make faster, safer cardiac assessment possible, while ambient and workflow AI reduce documentation load so clinicians can focus on patients (regional examples of ambient AI and workflow gains).

Put simply: guided imaging, AI‑assisted acquisition, real‑time quantification, and ambient documentation form a practical, testable playbook for Virginia Beach leaders seeking measurable safety, access, and time‑savings.

“This research demonstrates AI's potential to close the skills gap in lung ultrasound acquisition, making it more accessible to operators with limited training.” - Christiana Baloescu, MD

Deploying AI in Virginia Beach health systems means pairing rigorous data governance and stewardship with strong security practices and pragmatic vendor selection: advisory firms like Divurgent analytics consulting for healthcare specialize in building buy‑in, reusable data assets, operating models, and governance frameworks that turn analytics into repeatable value, while platform vendors such as Commence healthcare data platform focus on making data more accessible, discoverable, and interoperable across federal, state, and commercial health settings.

Practical steps include assessing analytics maturity, right‑sizing an operating model, and baking in MLOps/model‑validation and continuous verification so models stay safe as they scale - especially important given real‑world phishing and impersonation risks highlighted in local recruiting and security notices like Sentara's enterprise analytics posting and cybersecurity alert (Sentara enterprise analytics job posting and cybersecurity notice).

The goal is simple and memorable: keep the harbor open for data flows that improve care, but lock the lifeboat hatch - governance, zero‑trust controls, and vendor partnerships should let clinicians use insights without exposing patients or the organization.

Ethics, regulation, and patient safety must be the backbone of any AI rollout in Virginia Beach health systems, and the good news is a clear playbook already exists: a detailed governance blueprint - framed as an ethical framework for AI in healthcare - lays out systematic steps for validation, oversight, and accountability that translate directly into local policy and procurement decisions (ethical framework for artificial intelligence in healthcare (PubMed)); complementary university resources reinforce practical standards - UVA's research‑integrity page collects principles like human accountability, transparent disclosure, verification of AI outputs, and continuous monitoring that clinicians and administrators should adopt as everyday practices (UVA research integrity and AI standards for healthcare).

Virginia Tech's guidance on AI and the Internet of Behaviors underscores what this looks like in practice - privacy by design, informed consent, data anonymization, and active bias mitigation - so teams can treat model deployment with the same checklists used for sterile procedures, protecting patients while unlocking faster, fairer care (Virginia Tech ethics guidance for AI, Internet of Behaviors, privacy, and fairness).

Together, these sources point to a simple operating rule for Virginia Beach: require documented validation, insist on clear disclosure and user training, and monitor models continuously so patient trust - like the region's busy harbor - remains open and secure.

Launch pilots that solve a single, measurable pain point - start with administrative relief or a tight clinical workflow - and stitch together local partners who already know the landscape: for administrative scribe or summarization trials, emulate the VA's ambient‑scribe pilots that prioritize clinician review and “time saved” as a success measure (VA ambient‑scribe pilots and administrative burden reduction); for knowledge capture and workforce enablement, pair clinical teams with homegrown startups like Kilsar, which is using AI to preserve “tribal knowledge” for trades and operations in a city now bolstered by undersea fiber cables and startup infrastructure (Kilsar AI knowledge capture in Virginia Beach); and tap enterprise capability and record‑processing expertise from the DOMA/Livanta/Commence lineage when pilots need scalable data intake or quality‑oversight workflows (Commence AI clinical data solutions and record processing).

Coordinate with the city's IT roadmap and the Office of Performance & Accountability as you define an AI maturity assessment, a clear validation plan, and success metrics; the result should be small, fast pilots that either graduate to scale or teach a hard lesson - no sunk‑cost drama, just repeatable learning in a region built to host digital experiments.

“We help companies that manage equipment, aviation fleets, or large-scale systems capture tribal knowledge - the deep technical understanding that only comes from years of experience.” - Zach Casey, Kilsar's Chief Product Officer

Workforce training in Virginia Beach should blend short, practical courses with role‑specific curriculum changes so clinicians, IT staff, and administrators gain usable AI skills quickly: local offerings range from intensive one‑day, module‑based programs like the Introduction to AI Course in Virginia Beach (topics include NLP, neural networks, fuzzy logic and cloud‑based agent design) to statewide, no‑cost pathways surfaced by the new VirginiaHasJobs AI Career Launch Pad that curates Google AI Essentials, prompting fundamentals, and certificates to make entry and reskilling affordable and fast; combine those with hands‑on, clinic‑focused exercises - semantic search over a clinical knowledge base using private embeddings is a concrete example - and training can move from theory to bedside use in weeks rather than months.

Tight alignment with job roles (clinician, scribe, analyst), multiple delivery formats (online instructor‑led, self‑paced, onsite), and short, repeatable modules create a learning pipeline that turns curiosity into capability - picture a nurse consulting a five‑minute AI prompt checklist at the bedside like a pocket‑sized consultant.

“AI is increasingly part of every aspect of work, and we're excited to launch this opportunity for Virginians to take part in this future.” - Governor Glenn Youngkin

Measuring ROI for AI in Virginia Beach health systems starts with discipline: define the problem, pick tight success metrics, and treat pilots like operational investments rather than tech toys - steps spelled out in Vizient's playbook for “From hype to value,” which warns that 36% of health systems lack a formal AI prioritization framework and shows how focused efforts (Nebraska Medicine's roadmap even drove a 2,500% jump in discharge‑lounge use) turn pilots into systemwide gains (Vizient report on aligning healthcare AI initiatives and ROI (From Hype to Value)).

Start with a total cost of ownership that counts software, integration, training, and ongoing tuning, then track both short‑term “trending ROI” signals (time saved, clinician adoption) and longer‑term “realized ROI” (reduced readmissions, reclaimed revenue) as Propeller recommends (Propeller guide to measuring AI ROI and building an AI strategy).

Don't forget the concrete wins in revenue cycle and scheduling - regional examples show 4x ROI on algorithmic OR scheduling and big drops in claims‑review time - so fund small, measurable pilots, embed finance in governance, and require clear go/no‑go criteria and payback timelines before scaling (Healthcare IT News analysis of revenue cycle AI tools delivering measurable ROI); the memorable test: if the pilot can't show clear time‑saved or capacity gains in a year, stop it, capture lessons, and redeploy the funds to the next high‑value experiment.

“Being able to view available room time in seconds while scheduling in minutes is everything for my staff and patients.” - Dr. Keith Nord, chairman of orthopedic surgery, West Tennessee Healthcare

Leaders in Virginia Beach can turn momentum into measurable change by treating AI like a city project: start with a clear maturity assessment, run tight pilots with defined go/no‑go dates, and pair governance with rapid skills development so clinicians and staff can use tools safely and confidently; the Office of Performance & Accountability already lists “Expand the use of Artificial Intelligence” alongside IT initiatives and is the natural partner for an AI roadmap and milestone tracking (Virginia Beach Office of Performance & Accountability - IT initiatives and AI expansion).

Use an 18‑month, pilot‑to‑scale playbook - benchmarking time‑saved, clinician adoption, and short‑term ROI - so experiments either graduate cleanly or free up funds for the next test, as outlined in regional implementation guidance (Healthcare AI implementation roadmap and ROI guidance).

Finally, make training a requirement, not an afterthought: a practical 15‑week program like Nucamp's AI Essentials for Work teaches prompt writing, real use‑case labs, and role‑specific practice that converts pilots into daily time savings (and helps protect jobs through MLOps basics) - register early and tie enrollment to pilot teams (Nucamp AI Essentials for Work - 15‑week bootcamp).

The memorable test for 2025: if a pilot can't show clear time‑saved or capacity gains inside a year, capture the lessons, stop it, and redeploy resources to the next high‑value experiment.