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

Miami's health systems and medical schools are moving from curiosity to concrete use: the University of Miami now teaches prompt engineering as part of an AI elective that trains future clinicians to evaluate models and build AI agents for patient education, while UHealth's system-wide Aidoc rollout brings FDA-cleared imaging algorithms and real‑time triage - analyzing scans “within five minutes” at the point of care - to South Florida hospitals; those two developments show why local teams need practical prompt-writing and use‑case fluency, not just theory.

For clinicians, administrators, and tech-adjacent staff in Miami, short courses that teach prompt design and workflow integration - such as Nucamp's AI Essentials for Work - translate directly into saved clinician time, faster diagnoses, and safer patient-facing AI deployments.

Learn more about the University of Miami AI elective and UHealth's Aidoc program to match training with real hospital use cases.

Bootcamp	Length	Early Bird Cost	Syllabus
AI Essentials for Work	15 Weeks	$3,582	Nucamp AI Essentials for Work syllabus (15-week bootcamp)

“By combining our physician expertise with Aidoc's AI technology, our goal is to be able to provide even more timely and precise care to our patients, ultimately improving their overall health care experience. We intend to move AI up to the ‘point of care' and even the ‘point of scan,'” - Alexander McKinney, M.D.

Selection prioritized practical impact for Miami's hospitals and clinics by combining evidence on decision‑support scope, clinical readiness, and local regulatory fit: studies from a systematic review of AI in healthcare decision‑making guided the inclusion of tools that influence care pathways (Systematic review of AI decision-making in healthcare (PMC10916499)), while a validated 30‑item evaluation checklist - used as a quality gate - ensured candidate prompts and use cases reported transparent methods and measurable outcomes (30‑item AI/ML evaluation checklist for clinicians - JMAI, avg.

score 22.8/30). Practical utility (for example, triage or documentation workflows that reduce time‑to‑action) was weighted according to clinical reviews showing AI can prioritize high‑risk cases, and all selections were filtered for Florida‑specific privacy and deployment constraints using local guidance on HIPAA and state considerations (HIPAA and Florida-specific privacy considerations for AI in Miami healthcare).

The result is a shortlist of prompts and use cases that balance published evidence, implementation maturity, and regulatory safety so Miami teams can pilot tools with clear evaluation metrics.

Selection Pillar	Source / Key Data
Scope of decision‑support	Systematic review - PMC10916499
Quality & evaluation	30‑item checklist; avg. score 22.8/30 - JMAI
Clinical utility (triage)	Clinical review evidence - BMC Medical Education (2023)

Ambient clinical documentation from Abridge turns natural patient–clinician conversations into contextually aware, billable notes in real time, cutting clinician cognitive load and keeping attention on the patient rather than the keyboard; its enterprise-grade platform integrates directly inside Epic (Haiku to Hyperspace) and supports multilingual workflows - an important fit for Miami's Spanish‑ and Haitian‑Creole‑speaking clinics - so teams can pilot faster chart closure and measurable clinician wellbeing gains rather than speculative benefits (Abridge generative AI clinical documentation platform).

Before broad rollout, local teams should layer in Florida privacy and HIPAA deployment checks to match state rules and vendor BAAs (Florida HIPAA and privacy considerations for AI deployment resource); the vendor reports outcomes such as a 78% decrease in cognitive load and 86% of clinicians doing less after‑hours work, a concrete “so what” for retaining staff and improving clinic throughput.

Metric	Reported Result
Decrease in cognitive load	78%
Clinicians give more undivided attention	90%
Improvement in professional fulfillment	53%
Clinicians do less after‑hours work	86%

Nuance DAX (Dragon Ambient eXperience) streamlines the capture‑create‑review documentation loop by using ASR, NLP, and generative AI to turn clinician–patient conversations into specialty‑tailored clinical notes that extract diagnoses, medications, allergies, orders, and billing codes - then push encrypted, HIPAA‑compliant notes back into the EHR. Clinics report time savings (up to 7 minutes per encounter and roughly a 50% reduction in documentation time), a concrete benefit for Miami practices facing high patient volume and clinician burnout.

Capture happens via mobile apps or wearables, creation in a cloud AI engine that supports 40+ languages (including Spanish), and review in the clinician's EHR; DAX's HL7/FHIR integrations and large medical lexicon (90+ specialties, 550,000+ clinician users) make pilots practical for Florida systems.

Teams that must meet state privacy rules should map vendor BAAs and workflows to Florida HIPAA guidance before scaling, and evaluate the new DAX Copilot customization features that let organizations align templates and coding rules to local billing and quality needs (Nuance DAX clinical documentation overview: Nuance DAX clinical documentation overview and features, DAX Copilot customization and AI capabilities: DAX Copilot customization and AI capabilities for clinical productivity, Florida HIPAA and privacy considerations for AI deployment: AI Essentials for Work - Florida HIPAA and privacy guidance).

Attribute	Value / Note
Reported time savings	Up to 7 minutes per encounter; ~50% documentation time reduction
Languages	40+ languages (includes Spanish)
Clinical reach	90+ specialties; 550,000+ clinician users
EHR compatibility	HL7 / FHIR integrations; works with major EHR vendors

Miami research teams and hospital data scientists can bridge local imaging gaps and privacy constraints by using NVIDIA's Clara and synthetic data pipelines to create high‑fidelity, privacy-preserving training sets that reflect Florida's diverse patient mix; NVIDIA's synthetic data use case explains how generative AI plus GPU acceleration enables large‑scale 2D/3D image creation and digital twins for model validation (NVIDIA synthetic data generation for healthcare innovation), while the Clara platform packages imaging, genomics, and device toolkits for clinical AI workflows (NVIDIA Clara clinical AI platform).

For medical imaging specifically, the MAISI foundation model can produce high‑resolution 3D CT volumes (voxel sizes up to 512×512×768) with up to 127 anatomical classes and paired segmentation masks, and studies show adding MAISI‑generated images boosted segmentation Dice scores (for example, lung tumor) by about 4.5% - a concrete improvement that helps Miami hospitals train robust models for rare conditions without exposing patient data (MAISI synthetic medical imaging study by NVIDIA).

MAISI Metric	Value / Impact
Anatomical classes	Up to 127 classes (bones, organs, tumors)
Voxel resolution	Up to 512 × 512 × 768 (spacing 0.5–5.0 mm³)
Segmentation improvement (example)	Lung tumor Dice ↑ ~4.5% with synthetic augmentation

NVIDIA BioNeMo brings production‑grade generative AI and molecular simulation to drug discovery workflows that Miami labs and health‑tech startups can adopt for target ID, virtual screening, and lead optimization; the framework and its NIM microservices provide structure prediction, small‑molecule generation, and molecular‑property prediction while offering deployment choices - use the BioNeMo Framework for custom model training and fine‑tuning or BioNeMo NIMs for fast, scalable inference and API integration (BioNeMo Framework documentation and models, BioNeMo for biopharma overview from NVIDIA).

Industry adopters report concrete gains - Amgen saw up to 100× faster post‑training analysis and Receptor.AI cut costs by ~49% per instance‑hour after moving virtual screening and ligand‑pose prediction onto NVIDIA's accelerated stack - details that matter for Miami teams aiming to shorten hit‑to‑lead timelines without huge local compute investments.

Practical caution comes from benchmarking of generators (for example, MolMiM often yields higher novelty but needs careful validation), so city researchers should pair generative outputs with activity and ADMET scoring before experimental follow‑up (comparative study of molecular generators and their performance).

The net effect: accelerated hypothesis generation and lower inference costs - concrete levers for faster, more affordable translational research in Florida.

Model	Primary Use
MegaMolBART	Small‑molecule representation & generation
MolMIM	Generative chemistry (high novelty sampling)
DiffDock	Ligand pose generation / docking
OpenFold	Protein 3D structure prediction
ESM‑2nv / ESM‑1nv	Protein representation & property prediction
DNABERT	DNA sequence representation

GE HealthCare's AIR Recon DL brings deep‑learning MR reconstruction to Florida imaging centers by sharpening images up to 60% and cutting scan time by as much as half, a practical win for busy Miami and Jacksonville clinics that need faster throughput without hardware replacement; local evidence includes a Precision Imaging Center (Jacksonville, FL) case reporting ~50% less scan time for musculoskeletal exams, and a peer‑reviewed Korean study showing accelerated 3D T1 pediatric brain MRI with DL reconstruction reduced acquisition time by 29.3% (pre‑contrast) and 40.7% (post‑contrast) while improving signal‑to‑noise and reducing artifacts - concrete gains that translate into more same‑day slots and clearer exams for difficult cases.

Evaluate upgrades that preserve existing scanners, pilot with targeted anatomies, and measure patient‑throughput and image‑quality KPIs before scaling. Learn more about AIR Recon DL's reconstruction approach (AIR Recon DL deep‑learning MR reconstruction), supporting clinical data (DL‑based pediatric 3D T1 MRI study), and how the new Signa Sprint platform packages AIR Recon DL into a high‑performance 1.5T system for advanced cardiac and oncologic imaging (Signa Sprint high‑performance MRI).

Metric	Reported Result / Source
Image sharpness	Up to 60% improvement - GE HealthCare
Scan time reduction	Up to 50% faster; Precision Imaging Center (Jacksonville) ~50% musculoskeletal time reduction - GE HealthCare
Pediatric 3D T1 MRI time reduction	Pre‑contrast 29.3% / Post‑contrast 40.7% - KJR study

“We are driven to push the boundaries of what's possible in MRI with our ultra‑premium segment, as our goal is to set a new standard in diagnostic research and precision care that allows for earlier clinical detection and treatment response,” - Kelly Londy, CEO, MR, GE HealthCare

Tempus brings multimodal clinical, genomic, and imaging data into actionable, EHR‑integrated decision support that helps Miami teams build personalized care plans and deploy predictive diagnostics at the point of care: genomic profiling, algorithmic tests, and clinical‑trial matching combine in Tempus' provider tools (Hub, One, and EHR integrations) so oncologists can surface targeted therapies and trial options faster and more safely (Tempus AI-enabled precision medicine platform).

Life‑sciences‑grade algorithmic tests (IPS, HRD, Tumor Origin, and newer assays like DPYD that screen for 5‑FU toxicity) let clinicians in Florida flag patients at risk for adverse chemo effects or stratify patients for immunotherapy - without extra tissue and with EHR‑friendly reports (Tempus algorithmic tests and workflows; Tempus DPYD predictive diagnostic test).

Practically, Tempus' scale - ~65% of U.S. academic medical centers connected, 50%+ of U.S. oncologists in network, ~8M de‑identified research records and 30,000+ patients identified for trial enrollment - translates in Miami to faster trial access, data‑driven care pathways, and measurable reductions in treatment risk when predictive tests are used.

• Academic Medical Centers connected: ~65%
• Oncologists connected: 50%+
• De‑identified research records: ~8,000,000
• Patients identified for trial enrollment: 30,000+
• Data footprint: 350+ petabytes

“Our xT broad-panel assay is technology-enabled to support numerous clinical insights derived from the deep molecular data generated by the assay,” - Joel Dudley, Chief Scientific Officer, Tempus

Conversational AI is already shifting how Miami hospitals handle triage and mental‑health access: safety‑first agents from Hippocratic AI can run frequent post‑discharge check‑ins that catch deteriorations early (Universal Health Services' pilot reports thousands contacted and an average patient rating of 9.0/10), while scalable CBT‑based companions such as Wysa expand 24/7 support and anonymous check‑ins for mood and crisis screening - tools that help clinics reduce unnecessary ED visits and keep follow‑up workloads manageable for overloaded teams (UHS pilot of Hippocratic AI post‑discharge agents, AI agents in healthcare: mental‑health companions like Wysa and Woebot).

Practical caution is required: low‑cost “AI nurse” models raise trust and oversight questions, so Miami systems should pilot with human‑in‑the‑loop escalation, HIPAA BAAs, and clear KPIs for readmissions and patient satisfaction before scaling.

Vendor	Primary Use	Notable Result / Feature
Hippocratic AI	Post‑discharge outreach & care coordination	UHS pilot: thousands contacted; avg. patient rating 9.0/10
Wysa	Mental‑health companion (CBT‑based)	24/7 scalable, anonymous support for mood tracking and therapy exercises
Woebot	Mental‑health conversational support	CBT frameworks for on‑demand coping strategies

“Following an ER visit, I received a call from ‘Daisy.' As a hospital employee, I knew it was AI, but as a patient, I found the voice friendly, welcoming, and therapeutic. It allowed pacing with no rush and was a good listener. I support this AI technology.”

FDA's Elsa is already reshaping how agency reviewers handle submissions - summarizing adverse events, flagging cross‑document inconsistencies, and spotting high‑risk patterns across dossiers - changes that matter to Miami hospitals, medtech startups, and local regulatory teams because cleaner, machine‑readable filings will shorten query cycles and raise the bar for pre‑submission quality; Elsa's agency‑wide rollout (scaled by June 30, 2025) and GovCloud operation mean sponsors should treat metadata, CDISC alignment, and traceability as operational priorities rather than optional upgrades (Clinical Leader coverage of FDA's Elsa prompting pharma to rethink regulatory filings, TechTarget report on FDA launching Gen AI Elsa for clinical and regulatory tasks).

Practical cautions from device reviewers - Elsa can “confidently hallucinate” and still needs human sign‑off - underscore that Miami teams must build internal AI‑QC engines, adopt Good Machine Learning Practices, maintain human‑in‑the‑loop workflows, and map vendor assurances to Florida HIPAA/BAA requirements before scaling submissions (Medical Device Network analysis: AI at the FDA - help or hindrance?).

Elsa Capability	Practical Implication for Miami Teams
Adverse‑event summarization	Faster safety reviews; prioritize clean AE coding and source linkage
Automated consistency checks	Require metadata tagging and cross‑file validation before submission
Pattern recognition across submissions	Focus internal QC on historically high‑query dossier sections

“Following a very successful pilot program with FDA's scientific reviewers, I set an aggressive timeline to scale AI agency‑wide by June 30,” - FDA Commissioner Marty Makary, M.D., M.P.H.

Moxi is a socially intelligent mobile manipulator built to lift routine hospital logistics off busy Miami floors - running patient supplies, distributing PPE, fetching from central supply, and completing “last‑mile” pharmacy deliveries that otherwise pull nurses away from bedside care; Diligent reports Moxi's fleet completed more than 300,000 pharmacy deliveries (with high‑volume sites doing 900+ deliveries/month) and has the elevator autonomy and tracking needed for secure Meds‑to‑Beds workflows, a concrete lever for reducing discharge delays and off‑hour burdens on small Florida hospitals (Moxi hospital robot product page - Diligent Robotics).

The robots require no new infrastructure, use existing Wi‑Fi, and onboard with vendor guidance in weeks, not months - practical for Miami health systems that can pilot a single unit on a med/surgery floor to measure time‑reclaimed and fewer handoffs (Diligent Robotics U.S. pharmacy robotics milestone - July 16, 2025).

That combination of social design, autonomous mobile manipulation, and measurable delivery volume answers “so what?”: Moxi turns repetitive last‑mile tasks into scalable automation so clinicians spend more of their shift on patients, not transport runs.

Metric	Value / Source
Pharmacy (last‑mile) deliveries	>300,000 (Diligent Robotics, Jul 2025)
High‑volume site throughput	>900 deliveries/month (Diligent Robotics)
Autonomous elevator rides	125,000+ (Diligent Robotics)
Nurse time on non‑value tasks	Up to 30% of shift (reported nursing metric referenced by vendor materials)

Paige's FDA‑cleared pathology tools offer Miami hospitals a concrete path to faster, more reliable slide review: Paige Prostate received de novo FDA authorization for in‑vitro diagnostic use and, in study reads, raised per‑slide sensitivity from 89.5% to 96.8% while cutting false negatives by 70% and false positives by 24% - data drawn from hundreds of biopsies and slides collected across more than 150 institutions, showing the system works across staining and scanner variability (Paige Prostate FDA authorization and clinical study).

Deployment is easier for Florida labs because Paige's FullFocus viewer earned FDA 510(k) clearance for common scanners (Leica, Hamamatsu), improving scanner compatibility and allowing smaller pathology services to sign out digitally without costly hardware overhauls (FullFocus FDA 510(k) scanner compatibility and clearance).

For Miami systems facing pathologist shortages and high biopsy volumes, those regulatory milestones translate into fewer missed cancers, faster second reads, and clearer ROI when piloting digital‑first workflows.

Metric	Reported Value / Source
Sensitivity (per‑slide)	89.5% → 96.8% - clinical study (Paige Prostate)
False‑negative reduction	70% - Paige Prostate study
False‑positive reduction	24% - Paige Prostate study
Study slides / institutions	527 slides; samples from >150 institutions - regulatory submission

“FDA approval allows pathology laboratories to introduce this diagnostic tool into their clinical workflow to help make pathologists more accurate, more reproducible, and more efficient, which will allow them to focus their attention on the most critical aspects of establishing the diagnosis.” - David Klimstra, M.D.

Next steps for Miami healthcare teams and beginners: start small, stay legal, and measure everything - pick one high‑impact pilot (for example, ambient documentation or automated post‑discharge outreach), assemble a mixed team, and run a short, instrumented pilot that tracks clinician time, patient‑safety KPIs, and data‑governance checkpoints.

Use practical roadmaps to avoid common missteps: follow the Dialzara AI Patient Data Access 9‑Step Implementation Checklist (Dialzara AI Patient Data Access 9‑Step Implementation Checklist), adopt the Vector Institute Health AI Implementation Toolkit for safe deployment and continuous monitoring (Vector Institute Health AI Implementation Toolkit for Health AI Deployment), and train non‑technical staff in prompt design and governance so clinicians can oversee AI outputs - Nucamp's 15‑week AI Essentials for Work program prepares teams to write effective prompts, evaluate vendor BAAs, and map workflows to Florida HIPAA concerns (Nucamp AI Essentials for Work bootcamp - 15‑Week Syllabus & Registration).

That combination - checklist, toolkit, and targeted staff training - turns experimental pilots into measurable improvements in throughput, clinician burden, and patient safety for Miami systems.

Program	Length	Early Bird Cost	Link
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work - 15‑Week Syllabus & Registration