Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Plano

Plano sits at a rare intersection of momentum and mandate: major healthcare IT players and local innovators are already building GenAI pilots and home-care agents in the city, while new Texas law TRAIGA - effective January 1, 2026 - raises the regulatory bar for transparent, accountable AI in clinical settings (Texas TRAIGA AI governance law details).

Local activity isn't hypothetical - NTT DATA's recent research and Plano-based partnerships show hospitals and vendors wrestling with data readiness and governance as they scale GenAI, and collaborative projects with Duke and Ellipsis Health are testing virtual agents and remote‑monitoring models that keep clinicians “in the loop” while shifting more care to the home (NTT DATA GenAI research on healthcare).

Real-world tech already helps clinicians in Plano: wound‑imaging AI is in use at Baylor Scott & White The Heart Hospital – Plano, proving that predictive tools can change bedside decisions.

For clinicians and administrators ready to lead responsibly, practical upskilling - like Nucamp's 15‑week AI Essentials for Work bootcamp - offers a concrete path to close skills and governance gaps (Nucamp AI Essentials for Work bootcamp syllabus).

Bootcamp	Length	Cost (early bird)
AI Essentials for Work	15 Weeks	$3,582

“By integrating our AI virtual agent with NTT DATA's solution, we enhance patient access to holistic, personalized care, empowering families and caregivers with vital data and insights while reducing the burden on healthcare professionals. This initiative offers significant support for our loved ones during their healthcare journeys, ensuring that compassionate, efficient, and comprehensive care is accessible to everyone, right in the comfort of home.”

Selection started with problems clinicians actually feel - documentation drag, diagnostic ambiguity, and patient access - and then layered in practical filters: specialty fit, workflow interoperability, data transparency, and measurable outcomes as advised in ModMed's decision checklist (ModMed's 8 essential questions for choosing healthcare AI).

Each candidate prompt or use case was vetted for prompt specificity and output format (the single most important control for reliable LLM behavior per prompt‑engineering best practices) so a request reads like a clinical order - clear context, role, and desired structure - rather than an open question (Prompt engineering best practices in healthcare).

To standardize evaluation we applied checklist frameworks - METRICS' emphasis on model, evaluation, timing, count, and prompt specificity plus broader reporting criteria from the 30‑item clinician checklist - so every prompt earned a score for reproducibility, bias assessment, and real‑world testability (METRICS checklist for transparent AI evaluation (PMC)).

The result: a short list of Top 10 prompts that are problem‑first, auditable, and tied to concrete success metrics - so that a pilot either shortens chart time or improves a measurable clinical outcome, not just generates nice prose.

Selection Criterion	Source	Purpose
Problem-first & workflow fit	ModMed	Ensure practical clinical value
Prompt specificity & format	HealthTech	Reduce LLM ambiguity and risk
Standardized evaluation (METRICS)	PMC METRICS	Reproducible scoring and transparency

“The more specific we can be, the less we leave the LLM to infer what to do in a way that might be surprising for the end user.” - Jason Kim, Prompt Engineer, Anthropic

Clinical diagnostics and differential‑diagnosis prompts - using models like Med‑PaLM 2 or GPT‑4 - are rapidly becoming the kind of clinical assistant Plano providers can test in pilots: Med‑PaLM 2 was tuned for medical Q&A, reached expert‑level performance on USMLE‑style benchmarks (around 86.5% on MedQA), and even explores multimodal image interpretation with Med‑PaLM M for chest X‑rays and mammograms (Med‑PaLM research paper and resources).

In practice, these systems are best deployed as structured, auditable prompts - think of a clinical order that specifies role, context, and desired output format - so the model returns a clear, evidence‑anchored differential and rationale rather than freeform prose (Vertex AI's MedLM docs emphasize iterative prompt design and careful review) (Vertex AI MedLM prompt guidance for clinical use).

Real promise for Texas lies in augmenting triage and second‑opinion workflows - especially in under‑resourced or high‑volume settings - while retaining a clinician in the loop, because the research also flags important limits: models can hallucinate, may reflect dataset biases, and require rigorous, setting‑specific validation before clinical use (Med‑PaLM 2 peer‑reviewed evaluation study).

The practical takeaway for Plano: start with tightly scoped, auditable prompts and human oversight so AI becomes a rapid, explainable diagnostic aide - not an unsupervised replacement.

Clinical documentation automation has moved from promise to plug‑in: Nuance's DAX Copilot (now bundled into Microsoft's Dragon Copilot) is fully embedded into Epic workflows - Haiku and Hyperspace - so clinicians can capture ambient, multiparty conversations, populate Epic's smart data elements, and review draft notes on mobile or desktop without breaking eye contact with patients; see the Epic announcement for details (Nuance DAX and Epic ambient documentation integration) and Microsoft's Dragon Copilot overview for features and U.S. availability (Microsoft Dragon Copilot clinical workflow overview).

Early adopters report dramatic wins - ambient capture and specialty‑specific drafting can cut documentation time roughly in half and, in outcomes studies, drive measurable operational gains (Northwestern Medicine reported a 112% ROI and service‑level improvement) - findings that make DAX a practical option for Texas health systems running Epic to pilot with governance, consent, and human review baked in.

The concrete upside: clinicians reclaim after‑hours “pajama time” (one study showed a 17% drop) while notes flow into the chart in near real time, turning documentation from a nightly chore into a team‑enabled part of care.

Metric	Reported Result
Documentation time reduction	~50% (ambient voice capture)
Northwestern outcomes	112% ROI; 3.4% service‑level increase
U.S. availability date	May 1, 2025

“Since we have implemented DAX Copilot, I have not left clinic with an open note... In one word, DAX Copilot is transformative.” - Dr. Patrick McGill, Chief Transformation Officer (Microsoft DAX Copilot report)

Precision medicine in oncology is becoming operationally practical for Texas providers thanks to Tempus's multimodal platform: comprehensive genomic profiling that combines tissue and liquid biopsies, DNA/RNA sequencing, and MRD monitoring lets clinicians surface actionable biomarkers, while Tempus One brings those insights into the workflow as a generative AI assistant that can build a patient timeline, surface trial matches, and draft prior‑authorization support Tempus genomic profiling.

For busy oncology teams juggling large caseloads, the promise is concrete - Tempus' tools turn hundreds of scattered reports into a single, queryable timeline and match patients to trials in days - capabilities Texas health systems can evaluate alongside local governance and integration plans - and Tempus' EHR integration and Hub simplify ordering and results review so molecular data becomes actionable at the point of care Tempus One generative AI assistant.

Pilots should focus on auditable prompts, clinician oversight, and measurable endpoints (trial enrollment, time‑to‑treatment, or trial‑match rate) so AI aids decisions without replacing clinical judgment.

Metric	Value
Oncologists using Tempus	6.5K+
Patients identified for trials	30K+
De‑identified research records	8M+
Operational countries	40+

“Asking Tempus One a verbal question about the status of a test, results for a specific patient, or to look at trends across several patients is super intuitive and easy to use. It has helped with efficiently getting the real-time NGS answers I need in the clinic without the burden of digging through the chart or individual report.” - Thomas George, MD, FACP

For Plano radiology teams aiming to shrink waits and sharpen diagnoses, GE Healthcare's AIR Recon DL shows how deep‑learning image reconstruction can be a practical upgrade rather than a rip‑and‑replace: the algorithm removes noise and ringing to boost signal‑to‑noise and image sharpness (reports cite up to a 60% improvement), while cutting scan times by as much as 50%, which translates into faster throughput and a calmer experience for pediatric, neurodegenerative, claustrophobic, or larger patients (GE Healthcare AIR Recon DL MRI deep-learning image reconstruction product page).

The tech is deployable as an upgrade across GE's installed base and - according to coverage of the rollout - has already benefited more than two million patients globally, with real clinical wins such as a Houston center adding four daily MR slots after going live (news coverage of AIR Recon DL global impact and clinical benefits), a vivid reminder that faster, crisper scans can directly improve access, revenue, and diagnostic confidence in Texas hospitals.

Metric	Reported Value
Image sharpness / SNR improvement	Up to 60%
Scan time reduction	Up to 50%
Patients scanned since launch	More than 2 million
Compatibility	Works on GE MR scanners across field strengths and legacy installs

“It's not just about doing a five minute knee exam, it's doing a high quality five minute knee exam.” - Dr. Hollis Potter

Plano health systems that want to collaborate on imaging AI without moving sensitive records offsite can leverage NVIDIA Clara's federated learning tools to train shared models while keeping patient data local: Clara's server‑client architecture lets each hospital train locally and only exchange partial model updates over secure gRPC channels with tokens and SSL certificates, and provisioning tools simplify package distribution so sites can join, train, or leave a run with minimal operational friction (NVIDIA Clara federated learning guide for medical imaging).

The practical payoff is real - federated approaches in Clara have produced global models that match centralized training (for example, Dice ≈ 0.82 on BRATS2018 tumor segmentation), so Texas research consortia or hospital networks can pool statistical power across diverse patient populations without sharing raw images (NVIDIA developer blog on federated learning with Clara; peer-reviewed multi-center study on federated learning performance).

For Plano pilots, that means accelerated model quality, preserved local control of PHI, and a repeatable deployment path - provision, issue certificates, run rounds, aggregate updates - and a clear audit trail for governance and validation.

Capability	Researched Detail
Privacy mechanism	Only partial model weights shared; TLS/SSL + token authentication
Demonstrated model quality	Dice ≈ 0.82 on BRATS2018 (comparable to centralized)
Server‑client protocol	Register | GetModel | SubmitUpdate | Quit (gRPC commands)

Drug discovery in Plano and across Texas is moving from bench‑science fantasy to testable pilot work thanks to generative chemistry and molecular‑simulation prompts: Insilico's Pharma.AI stack (PandaOmics for target ID, Chemistry42 for molecule design) combines large life‑models and GPU‑accelerated workflows - including Nvidia's BioNeMo - to compress preclinical cycles, cut cost, and increase hit rates (Insilico's Pharma.AI drug discovery platform; NVIDIA blog on accelerating drug discovery with AI).

Real benchmarks underscore the point: 22 developmental candidate nominations from 2021–2024 with 10 programs reaching human clinical stage by the end of 2024, average time to candidate nomination roughly 13 months and roughly 70 molecules synthesized per program - concrete evidence that tightly scoped prompts for target, scaffold, and ADME optimization can turn months of lab work into a structured, auditable pipeline that Texas research groups and health‑tech startups can study for local pilots (Insilico preclinical benchmarks report).

Metric	Value
Developmental candidate nominations (2021–2024)	22
Programs reaching human clinical stage (by 12/31/2024)	10
Phase I completed	4
Phase IIa completed	1
Average time to candidate nomination	~13 months
Molecules synthesized per program (avg)	~70
Shortest / Longest time to DC	9 months / 18 months

“This first drug candidate that's going to Phase 2 is a true highlight of our end-to-end approach to bridge biology and chemistry with deep learning.” - Alex Zhavoronkov, CEO, Insilico Medicine

Conversational AI and virtual health assistants - exemplified by symptom‑checking platforms such as Ada - are becoming a practical “digital front door” for Plano and Texas health systems that need faster triage, 24/7 guidance, and lower‑cost access in suburban and rural catchments; Ada's research shows nearly half (46.4%) of assessments happen outside primary‑care hours and models suggest symptom checkers can cut triage nurse waiting times by more than half, making the benefit tangible for clinics stretched thin (Ada symptom checker research and studies).

Clinical evaluations paint a balanced picture - accuracy varies by specialty (one orthopedic study found Ada's top suggestion accuracy around 54.2% ± 4.2%), so these agents are best deployed as structured, auditable triage and history‑gathering tools that route patients to the right level of care rather than replace clinicians (orthopedics chatbot accuracy study (PMC)).

Thoughtful pilots in Plano should pair symptom checkers with clear escalation paths, inclusive question design, and measurement plans so the technology relieves bottlenecks without creating new safety or equity gaps.

Metric	Value
Assessments outside clinic hours	46.4% (Ada)
Top‑suggestion accuracy (orthopedics)	54.2% ± 4.2% (study)
Potential triage waiting time reduction	~54% (simulation/real‑world study)

“Healthcare chatbots are like having a knowledgeable, tireless medical assistant in your pocket, ready to help at a moment's notice.” - Dr. Emma Thompson, Digital Health Innovator

Early diagnosis and predictive‑analytics prompts are a concrete way Plano health systems can grab the narrow “golden” window when sepsis is still reversible: Mayo Clinic's work underscores that sepsis can progress rapidly and that early recognition saves lives, and AI models have started to surface reliably actionable alerts by combining vitals, labs, and clinical notes into in‑workflow warnings (Mayo Clinic Platform: Using AI to predict sepsis).

Real-world algorithms show meaningful gains - Johns Hopkins' TREWS flagged 82% of cases early and, when confirmed by clinicians within three hours, shortened time to first antibiotic by nearly two hours; nurse‑centered systems like COMPOSER cut in‑hospital sepsis mortality in prospective studies; and research models such as SERA can predict onset ~12 hours ahead with AUC ≈ 0.94 - capabilities Texas hospitals can pilot by embedding auditable prompts into EHR workflows, pairing alerts with sepsis‑response teams, and using phenotype‑aware logic to limit alert fatigue.

Emerging FDA‑authorized tools and clinic‑driven trials make a clear operational point: predictive prompts must be tightly scoped, human‑in‑the‑loop, and measured against time‑to‑antibiotic and mortality endpoints before scaling in Plano (NEJM AI: FDA‑authorized sepsis prediction).

Metric	Reported Result
Global annual sepsis cases / deaths	~49M cases; ~11M deaths
TREWS early identification	Identified 82% of sepsis cases early
Time to first antibiotic (TREWS)	~1.85 hour reduction when alert confirmed within 3 hrs
COMPOSER mortality impact	1.9% absolute reduction (17% relative)
SERA performance	Predicts ~12 hrs ahead; AUC 0.94; Sens 0.87; Spec 0.87

“Sepsis is a potentially life‑threatening complication related to an infection. It becomes very important that this is recognized early.” - Dr. Kannan Ramar, Mayo Clinic News Network

Plano's teaching hospitals and residency programs can treat immersive simulation as a practical, low‑risk rehearsal space - FundamentalVR's “flight‑simulator for surgery” approach combines VR with haptic feedback so trainees practice rare, high‑stakes procedures (from orthopedics to pediatric ophthalmology) without putting patients at risk, accelerating skill acquisition and reducing complications in the real OR; the American Academy of Ophthalmology's new VR Education program - built with FundamentalVR and supported by a $5M grant - illustrates how simulation can scale access to specialty training for procedures like ROP screening and laser therapy, and a desktop version expands inclusion for centers that lack headsets (FundamentalVR surgical VR training overview, American Academy of Ophthalmology VR Education program announcement).

For Texas teams juggling OR schedules and growing case complexity, the value is concrete: haptics improve tactile realism, studies report roughly a 30% faster speed of skills acquisition and up to a 95% gain in accuracy, and usage patterns show clinicians even logging training sessions between cases - making rehearsal a daily habit rather than an occasional lab exercise (Medical Device Network coverage of VR and haptics surgical training benefits).

Metric	Reported Value
Competency sessions conducted (FundamentalVR)	15,000+
Haptics impact on acquisition	~30% faster
Haptics impact on accuracy	Up to 95% increase
AAO VR program grant	$5 million
ROP incidence in U.S. preterm infants	5–8%

“We can now offer increased access to specialized training for ophthalmologists across the globe. And thanks to the generous funding from the Knights, it will be available for free.” - Stephen D. McLeod, MD, CEO, American Academy of Ophthalmology

Plano hospitals and life‑science vendors should watch the FDA's Elsa rollout as both a roadmap and a warning: Ember‑bright efficiencies like AI‑driven template population and automated QC - already flagged as likely to “rethink regulatory filings” in industry analysis - can dramatically shrink the manual burden of authoring and cross‑document checks, but they also raise the stakes for traceability, metadata tagging, and human‑in‑the‑loop validation (FDA Elsa may prompt pharma to rethink regulatory filings - Clinical Leader).

Equally important for Texas pilots is the emerging evidence that Elsa can hallucinate and produce false citations, a failure mode that undercuts trust and makes lightweight procurement risks cascade into clinical or compliance harm unless rigorously governed (FDA Elsa accuracy and oversight concerns - Applied Clinical Trials).

The pragmatic takeaway for Plano: pursue coding automation and prior‑authorization templates only behind validated pipelines - modular content stores, audit trails, GMLP‑aligned model checks, and clear human sign‑off - so that the time saved by automation doesn't become a hidden liability but a verifiable operational gain that regulators and clinicians can rely on.

“One of the challenges that came out from the initial release of the Elsa model for FDA is that it was prone to hallucination. By that, I mean it was making stuff up. … We can't have our AI do that when it comes to critical analysis of core ingredients and component structures that are required. These are elements where a slight deviation makes something safe or not.” - Marcel Botha, CEO, 10XBeta

Plano healthcare leaders ready to move from pilots to safe, scalable use should treat governance as the operational priority: Texas' TRAIGA (effective Jan 1, 2026) not only mandates transparency and patient disclosures but creates a regulatory sandbox and gives the Attorney General enforcement power with penalties up to $200,000 per violation, so early investment in oversight is insurance as much as compliance (Texas TRAIGA AI governance law summary and details).

Practical next steps - establish an interdisciplinary AI governance committee, codify policies for vendor risk management and auditing, run documented AI risk assessments, and stand up continuous monitoring and incident playbooks - mirror recommended industry frameworks and reduce deployment surprises (Key elements of an AI governance program for healthcare).

Pair that work with role‑based training so clinicians and staff recognize AI limits and escalation paths; short, focused courses (for example, Nucamp's 15‑week AI Essentials for Work bootcamp) can build prompt literacy and practical controls that make pilots auditable and repeatable (Nucamp AI Essentials for Work syllabus and course details).

Start small, measure time‑to‑impact and safety endpoints, and use the sandbox to prove value - because in regulated care the clearest win is an AI tool that saves clinician hours while leaving a robust audit trail for regulators and patients.

“TRAIGA marks a significant milestone in artificial intelligence (AI) regulation” that “establishes a comprehensive framework for the ethical development, deployment, and use of AI systems in Texas.”