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

Buffalo is increasingly well placed to adopt healthcare AI because it combines research depth, clinical scale, and active investment conversations: the statewide AI summit at UB gathers system leaders to focus on product integration and care delivery (AI in Healthcare 2.0 summit at University at Buffalo), Roswell Park provides research, trials, and tech-transfer capacity that can host translational pilots (Roswell Park comprehensive research programs and clinical trials), and national coalitions are bringing cloud, compute, and governance models to multi-center work (Cancer AI Alliance national partnership for accelerating discoveries).

Metric	FY2023
Grants & Contracts	$108.5M
Funded Projects	480
License Agreements	121
U.S. Patents	91

These assets create immediate pilot opportunities - and practical workforce gaps that training like the Nucamp AI Essentials for Work bootcamp syllabus and course details can fill by teaching prompt writing and operational AI skills.

"Collectively, the data held by the nation's leading cancer centers has been an untapped source of new cancer discoveries..."

Our methodology prioritized practical impact for Buffalo health systems by scoring candidate prompts and use cases against four filters: clinical integration, pilot feasibility, investment readiness, and vendor validation.

We favored use cases that panels at the statewide summit framed as operational priorities - product integration into care delivery and deployable pilots (AI in Healthcare 2.0 Summit at University at Buffalo) - and those that mirror commercially viable partnership models exemplified by major pharma AI collaborations (Merck AI drug-discovery collaborations and partnership model).

Vendor readiness was validated by product launches and demonstrated research capabilities such as Modella AI's imaging agents, which signal immediate trial readiness (Modella AI biomedical imaging solutions (Judith agent)).

Selection emphasized measurable 6–12 month ROI, data governance fit, and workforce retraining pathways; the simple scoring table below guided final picks.

Criterion	Why it matters
Clinical integration	Enables safe deployment in hospitals and clinics
Investment readiness	Attracts partners/funding for scale
Vendor maturity	Reduces pilot setup time and risk

“With the convergence of science, data, and AI, we're determined to fast-track the development of new and truly innovative candidates, forging a path to previously unimaginable medical breakthroughs…”

Nuance's DAX Copilot (Dragon Ambient eXperience) and the DAX Express embed ambient, conversational AI into Epic workflows to automate clinical notes - an attractive pilot option for Buffalo health systems that already run Epic and are seeking to cut clinician after‑hours work and expand access to care.

The technology passively captures multi‑party encounters, generates specialty‑specific draft notes, and - when paired with Microsoft's Dragon Copilot - can extend to orders, referrals, and evidence summarization to support operational pilots in ambulatory and inpatient settings.

Reported outcomes from early adopters include large reductions in documentation time and measurable ROI; a concise snapshot for planning local pilots is below:

Measure	Reported Result
Documentation time reduction	~50%
Minutes saved per encounter	6–7 minutes
Program ROI (case study)	112% (Northwestern)

“Dragon Copilot helps doctors tailor notes to their preferences, addressing length and detail variations.”

For implementation guidance and integration nuance, see an independent analysis of the Epic–Microsoft–Nuance partnership that outlines how DAX maps to Epic smart data elements and mobile workflows - useful reading for Buffalo CIOs scoping pragmatic pilots: Nuance DAX Express ambient documentation integration with Epic workflows, Microsoft Dragon Copilot clinical documentation and ROI details, and an independent analysis of Epic–Microsoft–Nuance DAX Copilot integration and Epic mapping.

Oracle's GenAI stack on Oracle Cloud Infrastructure (OCI) is powering diagnostic imaging advances that matter for New York health systems by enabling rapid, image‑based molecular profiling and research-ready foundation models that can be piloted at centers like Roswell Park.

Imagene's CanvOI and LungOI - built and run on OCI Supercluster and OCI AI infrastructure - demonstrate practical U.S. adoption: LungOI analyzes small biopsy images in minutes, supports treatment selection for specific genotypes, and has CMS reimbursement codes that lower the financial barrier for Buffalo hospitals to run pilots; read more in the Imagene cancer case study on Oracle Cloud Infrastructure (Imagene OCI cancer case study on Oracle Cloud Infrastructure).

CanvOI is presented as a research foundation model intended to democratize oncology intelligence on OCI; details on the launch and collaboration are summarized in Pathology News (Imagene CanvOI foundation model launch on OCI - Pathology News), and OCI Vision offers hands‑on examples for breast and lung imaging research that Buffalo investigators can adapt locally via the OCI Vision demo (OCI Vision breast and lung cancer imaging research demo).

Metric	Value
Foundation model size	1.1 billion parameters
Pretraining images	>630,000 tissue images
Source sites	>100 provider sites
Organ coverage	>40 major organs/tissue types

“It's an evolution, a scaling up, of precision medicine.”

For Buffalo leaders, OCI‑hosted imaging AI offers an operational pathway: secure, GPU‑backed compute for local trials, faster pathology turnaround that reduces diagnostic waits, and an ecosystem that supports both diagnostics and translational research pilots.

AIDDISON, Merck's generative‑AI platform for medicinal chemistry, offers Buffalo health systems and local biotechs a practical bridge from hypothesis to actionable small‑molecule leads by enabling de‑novo design, ultra‑large virtual screening, molecular docking, and ML‑based ADMET prediction in a single cloud‑native workflow - making it a natural fit for translational pilots at Roswell Park and collaborations with UB researchers aiming to shorten discovery timelines and reduce early wet‑lab costs (Merck AIDDISON generative AI drug discovery platform).

The platform's integrated features (reaction‑aware library design, retrosynthesis links, and ISO‑grade data controls) support institutional requirements for IP protection and enable secure, shareable in‑silico campaigns that local CIOs and tech transfer offices can pilot with modest compute budgets; product and capability details are summarized by the vendor and distribution partners (AIDDISON AI drug discovery software features (Sigma‑Aldrich)).

Practical evidence from webinar and case studies shows AIDDISON can help teams break into novel chemical space and accelerate lead series development - useful when Buffalo labs seek competitive seed data for grant or industry partnerships (AIDDISON case study for AI‑driven drug discovery).

Capability	Representative value
Virtual compounds searchable	>60 billion
SA‑Space exclusive compounds	>25 billion
Generative engine	REINVENT 4.0 (de‑novo design)
Security / compliance	ISO 27001, cloud‑native SaaS

“AIDDISON™ is an integrated and easy-to-use tool for lead identification that brings together a suite of tools for modeling, docking and scoring molecules.”

Nuance's Dragon Ambient eXperience (DAX) brings ambient clinical voice assistants to Buffalo hospitals by passively capturing patient‑clinician conversations, drafting specialty‑specific notes, and feeding them into Epic workflows to shrink documentation burden and improve bedside engagement; practical pilots show rapid turnaround and measurable time savings when paired with Microsoft's broader Dragon offerings (Microsoft Dragon Copilot clinical documentation details).

A real‑world primary‑care rollout at UM Health‑West integrated DAX with Epic, provisioning licenses and mobile recording for ~100 clinicians and reporting notes returned in under an hour, average savings of ~10 minutes per clinician per day, and strong patient ratings - metrics Buffalo CIOs should weigh when scoping ambulatory and hospital pilots (Nuance DAX ambient listening case study (UM Health‑West)).

For smaller practices or teams evaluating speech accuracy, Dragon Medical One offers proven speech‑driven documentation and enterprise security that supports HIPAA workflows and EHR integrations - useful context for procurement and compliance planning (Dragon Medical One speech‑driven documentation features).

“I get asked all the time, ‘Can you do a demo of DAX?'” Owens said.

Measure	Result
Pilot clinicians	~100 primary‑care physicians
Note return time	<1 hour (typical)
Time saved per clinician	~10 minutes/day
Patient engagement	4.8 / 5

For Buffalo health systems, Storyline AI–style predictive analytics translates fragmented EHR, claims, and social‑determinant data into actionable population‑health workflows for risk stratification, targeted care management, and earlier intervention - especially valuable for post‑discharge planning and heart‑failure monitoring at centers like Roswell Park and UB. National examples show how combining neural nets, NLP, and federated learning produces timely risk scores that clinicians can operationalize: Mission Health's ML readmission predictor reached an AUC of 0.784 and delivered scores the morning after discharge, improving follow‑up and lowering readmissions (Mission Health ML readmission predictor case study (Health Catalyst)).

A growing literature of heart‑failure and readmission models supports local clinical validation and grant funding (Heart‑failure predictive analytics review (PMC)), while practical vendor guidance outlines measurable outcomes and deployment patterns for population health pilots (AI predictive analytics outcomes and use cases (Master of Code)).

Use clear performance targets and governance (AUC, readmission reduction, adherence uplift, privacy controls) when scoping pilots; benchmark metrics for planning are shown below.

Measure	Benchmark
Readmission model AUC	0.78 (Mission Health)
Readmission reduction via ML	15–20% (reported ranges)
Medication‑adherence uplift	Up to 30%
Diagnostic/forecast accuracy	>90% for select models

Ada Health's clinically validated AI symptom‑assessment and care‑navigation platform can strengthen Buffalo's telehealth and triage capacity by providing 24/7 symptom assessment, directing patients to the appropriate level of care, and producing structured handovers that improve telehealth conversion and clinic efficiency (see the Ada digital triage CUF case study for real‑world outcomes).

“Ada helps patients to access the highest-quality care according to their clinical needs. It smooths the whole journey to care by guiding the patients to take the right steps.” - Dr Micaela Seemann Monteiro

Measure	Result
Patients more certain of care	66%
Assessments outside clinic hours	53%
Patients feel prepared for consultation	80%
Same‑day telehealth conversion	13%
Physicians reporting time savings / preparedness	64% / 78%

For New York systems - where after‑hours access and EHR interoperability shape pilot feasibility - Ada's consumer app and enterprise tools can reduce unnecessary specialty referrals, lower patient anxiety, and increase same‑day telehealth uptake; explore the Ada symptom assessment app and a practical guide to building compliant medical chatbots to plan local pilots aligned with Roswell Park and UB care pathways.

Moxi from Diligent Robotics is a socially intelligent clinical assistant that automates non‑patient‑facing logistics - running patient supplies, delivering lab samples, fetching meds and PPE - so nursing teams spend less time walking and more time at the bedside; hospitals can deploy pilots in weeks without heavy infrastructure, using existing Wi‑Fi and Diligent's implementation support (Moxi healthcare robot features by Diligent Robotics).

For Buffalo systems such as Roswell Park and UB‑affiliated hospitals, Moxi fits operational pilots like Meds‑to‑Beds, central supply runs, and routine lab shuttles that improve throughput and staff retention while supporting surge workflows.

Key benefits and real‑world impact are summarized below in straightforward metrics:

Measure	Result
Care‑team hours saved (2024)	284,000 hours
Labs delivered (example site)	9,900+ deliveries
Nurse time returned (site)	595+ days
Pharmacy hours saved	6,350 hours

Moxi's human‑guided learning and mobile manipulation let it adapt to busy, semi‑structured hospital floors, reducing interruptions that research links to burnout and task overload (Moxi case study - Moving With Moxi; AI in nursing benefits review - PubMed Central).

“Moxi stands out for being a socially intelligent robot that can aid nurses without making humans feel uncomfortable.”

Operational leaders in Buffalo should consider small, measurable pilots that track minutes saved per shift, supply‑chain latency, and staff satisfaction to quantify ROI and scale adoption.

Doximity GPT is a practical, HIPAA‑aware option for Buffalo clinicians who need secure, AI‑assisted documentation and patient communication - its free clinician tier and integrations can help Roswell Park and UB‑affiliated practices reclaim clinician time by drafting letters, summarizing charts, and generating instant notes.

Doximity describes platform safeguards (SOC 2 Type II, encryption, and BAAs for institutional partners) that make vendor negotiation and hospital pilots more straightforward for local CIOs; see Doximity security SOC 2 and BAA information for implementation details via the vendor's security page Doximity security SOC 2 and BAA information.

Operational best practices mirror national guidance: avoid public LLM endpoints for PHI, use de‑identification or enterprise BAAs, and keep clinicians in the review loop - principles summarized in practical HIPAA guidance for LLMs Paubox guidance on HIPAA‑compliant LLM use in healthcare.

For product specifics and clinician‑facing workflows, review Doximity's product brief and clinician resources on Doximity GPT Doximity GPT product brief and clinician resources.

Key platform capabilities useful for Buffalo pilots are summarized below:

Capability	Value
Access & cost	Free, unlimited access for clinicians
Privacy & compliance	HIPAA compliant; institutional BAAs available
Clinical features	Instant Notes, chart summarization, patient correspondence

"This tool has been a game-changer for my charting process, whether it's creating a plan for congestive heart failure or an HPI for atrial fibrillation."

Integrate Doximity GPT into a phased Buffalo pilot (de‑identified test cases → BAAed environment → supervised clinical rollout) to realize documentation time savings while meeting New York regulatory and institutional privacy expectations.

Hootsuite's OwlyGPT and Hootsuite for Healthcare give Buffalo health systems a practical way to turn social signals into measurable patient‑outreach and recruitment actions: real‑time social listening can flag post‑discharge concerns, surface local access issues, and amplify clinician thought leadership to improve trust across Western New York communities.

OwlyGPT generates on‑brand captions, audience insights, and crisis comms from live conversations so small communications teams at Roswell Park or UB‑affiliated clinics can scale engagement and spot misinformation before it spreads; see the OwlyGPT real-time social AI for healthcare features for feature details.

Its healthcare product suite is explicitly HIPAA‑friendly, supports employee advocacy to expand reach, and includes analytics that tie posts to outcomes - important for New York compliance and grant reporting; learn more on the Hootsuite for Healthcare HIPAA-compliant social platform.

For teams building monitoring playbooks, Hootsuite's social listening guide to set SMART goals shows how to set SMART goals and queries that map to operational KPIs.

Key platform outcomes from vendor case studies are shown below:

Measure	Result
Equivalent ad value saved	$1.5M
Negative sentiment reduction	50%
Engagement increase (YoY)	129%

“OwlyGPT is quickly becoming our secret sauce.” - Nausheen Alam

For Buffalo pilots, prioritize HIPAA workflows, local language tuning, and employee advocacy to expand clinic reach and improve appointment conversion while tracking sentiment and referral metrics.

BioMorph positions Buffalo as a practical hub for AI‑accelerated R&D by combining molecular design, federated learning, and privacy‑enhancing pipelines that link Roswell Park's trials, UB's research labs, and local biotech incubators - shortening early discovery timelines, improving candidate triage, and lowering wet‑lab spend for seed studies.

By operationalizing the ITIF recommendations on data sharing, PETs, and public–private partnerships, BioMorph pilots can use federated models on regional EHR/genomic datasets to improve target selection while keeping PHI local; see the ITIF report for policy and pipeline benchmarks (ITIF report: Harnessing AI to Accelerate Innovation in the Biopharmaceutical Industry).

Local value propositions include faster IND‑ready lead series for Roswell Park tech‑transfer, smaller early‑stage trials for UB spinouts, and clearer grant narratives for NYS economic development.

Practical Buffalo playbooks and workforce steps are covered in our local guidance - how AI helps hospitals cut costs and a getting‑started roadmap for pilots (Buffalo healthcare AI playbook: How AI helps hospitals cut costs and improve efficiency, Complete guide to using AI in Buffalo healthcare (2025): roadmap for pilots and implementation).

AI has the potential to transform drug development by enhancing productivity across the entire development pipeline, boosting biopharmaceutical innovation, accelerating the delivery of new therapies, and fostering competition to help improve public health outcomes.

Metric	Value
Estimated R&D cost per new drug	Up to $2.83B (uncapitalized)
Phase I → approval success rate	~7.9%
All of Us funding (diverse genomics)	>$3.1B

Buffalo healthcare leaders should convert local assets into measurable pilots by sequencing three priorities: 1) prepare and protect data, 2) launch low‑risk, high‑value pilots, and 3) train the workforce to operate and govern models - practical guidance for steps 1–2 is in the Sphere Partners “How to Prepare Healthcare Data for LLMs” guide (Prepare Healthcare Data for LLMs - Sphere Partners guide), while HIPAA‑specific governance, BAAs and privacy‑preserving techniques are summarized in a strategic compliance playbook (HIPAA and AI: A Strategic Guide to Healthcare Compliance - AI Exponent); use the Dialzara 9‑step checklist to operationalize pilots and staff training plans (AI Patient Data Access 9‑Step Implementation Checklist - Dialzara).

Simple, time‑boxed priorities for Buffalo:

Step	Timeline
Data inventory & governance	0–3 months
Low‑risk pilot (de‑identified)	3–9 months
Scale, audit & workforce training	9–18 months

“The intersection of AI and HIPAA represents one of the most complex regulatory challenges in healthcare today.”

Pair these actions with targeted upskilling (e.g., Nucamp AI Essentials for Work bootcamp - practical AI skills for the workplace) and vendor BAAs so Buffalo systems move from concept to compliant, measurable impact within a year.