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

Indianapolis has moved from speculation to practical AI projects in 2025: the Regenstrief Institute's in-person Healthcare AI Conference (Feb 11–12, 2025) gathered local researchers and clinicians to debate NLP, governance, clinical implementation and privacy, while Indianapolis-based Community Health Network publicly targets $10M in 2025 savings by using AI to close care gaps and automate notes; state lawmakers are answering with disclosure requirements such as Indiana's HB1620, and rural needs are acute - 30% of Hoosiers in rural areas face higher chronic illness burdens - making ambient listening, retrieval‑augmented generation and machine‑vision monitoring promising, high‑impact use cases for Indiana health systems.

For clinicians, administrators, and students who need workplace-ready AI skills, the AI Essentials for Work bootcamp - 15-week practical AI skills for the workplace teaches tool use and prompt writing to apply these technologies responsibly in local health settings.

Date	Location	Capacity	Parking
Feb 11–12, 2025	Regenstrief Institute, 1101 W 10th St, Indianapolis, IN	80 (at capacity)	Wilson Street Garage & Lockefield Garage (validation)

“We're not trying to replace humans,” Community Health Network Chief Transformation Officer Patrick McGill, MD, told the news outlet. “We're not trying to take the clinician or the clinical decision‑making out of it. [It's] how do we enable them to be more efficient and create that high quality?”

In Indianapolis in 2025 the AI trend is practical and targeted: local research and health systems are moving from proof‑of‑concepts to narrowly scoped, ROI‑driven pilots that pair clinical needs with governance and data strategy - from the Regenstrief Healthcare AI Conference convening clinicians and informaticists to debate NLP, imaging, privacy and clinical integration (Regenstrief Healthcare AI Conference 2025 details) to vendors and startups applying conversational AI to contact‑center quality and patient experience to surface empathy, compliance, and hidden safety signals (Conversational AI for healthcare contact-center quality (AuthenticX, 2025)).

State policy is catching up: Indiana's HB1620 requires disclosure when AI affects care or coverage decisions, pushing local organizations to build transparent pipelines and meaningful human oversight (Indiana 2025 AI and data‑privacy legislative overview).

The practical payoff for Indianapolis: better triage and earlier detection workflows (already in use at IU Health) and cleaner, healthcare‑specific training data mean AI projects can reduce administrative burden while delivering measurable clinical and financial improvements - so hospitals can prioritize pilots that protect patients, meet disclosure rules, and prove value quickly.

Policy	Key point
Indiana HB1620	Requires disclosure to patients and insured when AI is used in health care decision‑making or coverage determinations.

“As business and healthcare leaders, if we don't understand what's being said within conversations, then we're missing a critical source of intelligence - how customers view and experience the healthcare system.” - Amy Brown, Founder & CEO | Authenticx

Typical AI uses across Indianapolis health systems concentrate on image‑heavy, data‑heavy, and repetitive tasks: radiology tools that triage and flag findings (IU clinicians describe a CT composite of 656 images where AI highlighted a suspicious lung nodule, cutting the need to review hundreds of individual frames), real‑time speech‑to‑report transcription and generative summaries that shave minutes from each read, and algorithms that push high‑risk reports into staff workflows so follow‑ups actually get scheduled - all helping address a local shortage where radiologists juggle dozens of cases a day.

Pathology and genomics teams at IU are using AI to tally cells, mine terabytes of tumor and sequencing data, and generate research hypotheses faster, while ambient‑listening and NLP tools relieve clinicians of documentation burden.

These are paired with governance and safety work from Indianapolis bodies to ensure models match the populations they serve and perform reliably in clinic settings; together the result is measurable time savings and more consistent triage at scale.

Read the IU Health radiology examples and clinical pilots for AI in healthcare and the Regenstrief draft Code of Conduct for AI use in health care for local context and best practices.

“Computers can look at a billion images without fatigue. But when it finds something, it may struggle to figure out what it has found. In contrast, radiologists are experts at determining whether something is just a dot or potentially cancer.” - Kevin L. Smith, MD

AI is already embedded across Indiana's care landscape: Indianapolis systems use AI to find care gaps, summarize patient surveys and automate physician notes - Indianapolis-based Community Health Network even reported it is on track to save $10M in 2025 by deploying these operational tools (Community Health Network AI savings, Becker's Hospital Review); at academic centers like IU School of Medicine, AI accelerates imaging and discovery - algorithms triage CTs and mammograms, flag brain bleeds, and help pathologists count cells and sift genomics, where a single tumor model produced almost 30 terabytes of research data that only scalable AI pipelines can parse (IU Medicine: Unlocking the Power of AI).

Imaging informatics teams run de‑identification and federated‑learning projects to share cohorts securely, while teleradiology and remote‑read services - now paired with AI triage - extend specialist capacity to rural hospitals; early pilots of AI‑guided mobile clinics suggest generalists could perform specialist‑level tasks with AI coaching, shrinking access gaps.

The bottom line: AI is used where data volume, repetitive detail, or workforce shortages create bottlenecks - turning weeks of manual review into hours and delivering actionable flags that clinicians can act on immediately.

Quick fact	Value / source
Community Health Network 2025 savings target	$10M (Becker's Hospital Review)
IU research imaging & pathology scale	~30 TB from a single tumor model; 19M+ radiology exams & 1.2M pathology correlations (IU imaging informatics)

“We mine large amounts of data - genomics data, imaging data, clinical data - and we generate hypotheses. We make predictions.” - Kun Huang, PhD

AI in healthcare is expanding at an unprecedented clip, with multiple market analyses pointing to high‑double‑digit growth that directly affects Indianapolis' buying and implementation environment: MarketsandMarkets reports the global AI in healthcare market jumped from about $14.92B in 2024 to $21.66B in 2025 with a projected ~38.6% CAGR (MarketsandMarkets AI in Healthcare market report 2025), Fortune Business Insights forecasts global market growth from $39.25B in 2025 to $504.17B by 2032 (44.0% CAGR) signaling rapid platform and vendor expansion (Fortune Business Insights AI in Healthcare market forecast 2025–2032), and Grand View anticipates the U.S. market alone growing at roughly a 36.1% CAGR through 2030 - meaning North American demand (about half the market in 2024) will drive more validated clinical tools and commercial pressure on hospitals to prove ROI (Grand View Research U.S. AI in Healthcare market outlook).

So what this means for Indianapolis: procurement cycles will be faster, more vendors will offer specialty solutions, and health systems must pair tight governance with clear ROI metrics to avoid costly pilots that don't scale.

Source	Key 2024–2025 figure	Reported CAGR
MarketsandMarkets	Global: $14.92B (2024) → $21.66B (2025)	~38.6%
Fortune Business Insights	Global: $39.25B (2025)	44.0% (2025–2032)
Grand View Research	U.S. market projected to reach ~$102.15B (2030)	36.1% (2024–2030)

“It's essential for doctors to know both the initial onset time, as well as whether a stroke could be reversed.” - Dr Paul Bentley

Key challenges for Indianapolis health systems in 2025 center on bias, data quality, privacy, and the special needs of rural Hoosiers: machine learning models inherit labeling and sampling shortcuts that can encode race, gender, or access patterns (IU researchers found imaging models can use pixel‑level signals to infer race, sometimes influencing who gets advanced scans), while national analyses warn that algorithms trained on uneven U.S. datasets - often concentrated in California, Massachusetts, and New York - risk misrepresenting Midwestern and rural patients and omitting social determinants that matter for care decisions (IU Medicine article: Reducing Bias in AI (Winter 2025); PMC research review: Addressing bias in big data and AI for health care).

The practical consequence in Indiana: models that aren't audited or governed can steer scarce follow‑up resources away from rural or underrepresented groups, worsening existing access gaps for the ~30% of Hoosiers living in rural areas.

Mitigations that local hospitals and vendors must adopt include rigorous data provenance and labeling standards, human‑in‑the‑loop review, post‑deployment monitoring, and diversifying developer and clinical teams so training labels and use cases reflect Indiana populations (Open‑science guidance for bias mitigation in healthcare AI).

These steps turn abstract risk into operational controls that protect patient safety, legal compliance, and the equity of AI‑driven triage and imaging workflows.

“If you have a diverse group of thinkers in your team and everybody has a chance to speak or contribute, many of the problems we're discussing get solved. Model biases reflect the lack of diversity in the thinking process.” - Saptarshi Purkayastha, PhD

Implement AI in Indianapolis health settings by pairing clear, measurable goals with rigorous local data governance: pick one high‑impact use case, define ROI and safety KPIs, and require a pre‑deployment “representativeness” validation on Indiana and rural cohorts so models don't misroute scarce follow‑up resources; operationalize this with documented data provenance, Business Associate Agreements, encryption, and real‑time auditing to meet HIPAA and state disclosure expectations.

Use privacy‑preserving techniques (federated learning, differential privacy) and least‑privilege architectures to enable collaboration without wholesale data sharing, and require vendors to disclose training data, bias‑mitigation steps, and explainability features before procurement.

Start with a phased pilot that embeds human‑in‑the‑loop checkpoints for every clinical decision, train frontline staff on exception workflows, and publish monitoring results so clinicians and patients can see performance over time - following the National Academy/Regenstrief principles for trustworthy deployment.

Local resources such as the Regenstrief draft Code of Conduct for AI use (Regenstrief Code of Conduct for AI in healthcare), TechPoint's practical privacy strategies (TechPoint article on privacy-preserving AI in healthcare), and Indiana's MPH Data Day guidance on governance and lifecycle management (MPH Data Day governance and lifecycle resources) give concrete templates - so what this yields: fewer harmful surprises, measurable efficiency gains, and AI tools that serve Hoosiers equitably rather than adding hidden risk.

Best Practice	Action
Define measurable goals	Pilot with KPIs for safety, equity, and financial ROI
Data governance	Provenance, BAAs, encryption, and local validation sets
Privacy‑preserving tech	Federated learning, differential privacy, least‑privilege access
Operational controls	Human‑in‑the‑loop, monitoring dashboards, vendor transparency

“The goal, as noted in the paper, ‘is that all decisions associated with, and actions taken, to develop and deploy AI in the health sector will be consisted with these Commitments to develop and foster trust.'”

Indianapolis health systems are moving governance from policy documents to clinic floors: Regenstrief's 2025 Healthcare AI Conference featured panels on “Practical AI Governance and Implementation in Clinical Settings,” signaling local consensus that clinical pilots must include clear oversight and monitoring (Regenstrief 2025 Healthcare AI Conference details).

Community Health Network has already operationalized that idea by hiring a director of AI and data governance and standing up an executive steering committee (CFO, CMO, chief physician executive, CIO and IT/analytics leads) to prioritize projects, allocate resources, and require vendor transparency and post‑deployment monitoring - concrete steps that reduce legal and safety risk while accelerating scalable pilots (Community Health Network governance capabilities - HealthLeaders analysis).

These local actions align with national frameworks calling for a Code of Conduct and learning‑health‑system principles to ensure AI is safe, equitable, and auditable; adopting those principles in procurement, human‑in‑the‑loop workflows, and real‑time performance dashboards is the practical path to trustworthy clinical implementation in Indiana (NAM AI Code of Conduct discussion draft and implementation guidance).

The payoff: fewer failed pilots, clearer clinician trust, and measurable patient protections when AI tools touch diagnosis, triage, or coverage decisions.

Governance function	Operational action
Prioritization	Executive steering committee ranks use cases by ROI and safety
Policies & procedures	BAAs, vendor disclosure, and pre-deployment validation
Resource allocation	Dedicated director of AI & data governance and technical staff
Monitoring & bias mitigation	Data reports, third‑party performance checks, post‑deployment audits

“You need to have the governance in place to make sure that you understand all of the tools that are being used, how the tools are being used, the intended outcome of usage, and how you mitigate bias. Having a governance structure in place from the beginning is helpful.” - Patrick McGill, MD, MBA

Students, researchers, and entrepreneurs in Indianapolis have a direct, practical entry into healthcare AI via local convenings and Regenstrief's training programs: the 2025 Healthcare AI Conference introduced a student poster session where trainees presented AI projects to leaders such as Drs.

Kun Huang, Shaun Grannis and Rachel Patzer, giving frontline feedback and networking with panels on governance, clinical implementation, and real‑world pilots (IU School of Medicine 2025 AI in Health Care Conference summary and highlights); nearby, Regenstrief's research and training ecosystem lists fellowships, Summer Scholars, and practice‑oriented programs that can turn poster conversations into supervised research, internships, or pilot collaborations with local health systems (Regenstrief Institute conference and training opportunities for healthcare AI).

So what: a concise poster plus follow‑up with a Regenstrief investigator or conference panelist is a realistic, high‑leverage way to move from idea to a funded pilot, co‑authorship, or vendor pilot conversations - an actionable pathway for anyone building applied AI solutions for Indiana health care.

Opportunity	How to access
Student poster sessions	Present at Regenstrief/Indianapolis conferences to get direct feedback from AI leaders and network with clinicians
Fellowships & training	Apply to Regenstrief fellowships, Summer Scholars, and training programs to gain supervised research experience
Entrepreneur pilot partners	Use conference panels and breakout sessions on governance/implementation to meet hospital data teams and propose small, measurable pilots

Indianapolis' AI moment in 2025 is no longer theoretical: local strengths - Regenstrief's convenings and IU clinical partnerships - plus clear guardrails from national efforts create a practical path to scale targeted, high‑value pilots that protect patients and rural communities; adopting the National Academy of Medicine AI Code of Conduct discussion draft and operational lessons from the Regenstrief Healthcare AI Conference gives hospitals a playbook for measurable ROI, bias mitigation, and post‑deployment monitoring, while workforce programs like the AI Essentials for Work bootcamp - 15‑week practical AI skills for the workplace provide the prompt‑writing and applied tool training clinicians and administrators need to run safe pilots; so what: by pairing local validation sets, human‑in‑the‑loop checks, and governance up front, Indianapolis systems can avoid costly failed pilots and translate AI into faster triage, lower admin burden, and equitable care for both metro and rural Hoosiers.

Bootcamp	Length	Core courses	Early bird cost	Register
AI Essentials for Work	15 Weeks	AI at Work: Foundations; Writing AI Prompts; Job‑Based Practical AI Skills	$3,582	Register for the AI Essentials for Work 15‑Week Bootcamp

“Public data may seem easier to use in the short term because of its accessibility. It takes time to sort, cleanse and properly categorize internal data. However, when the proper care is taken to prepare the internal data, it will typically produce more reliable and helpful results. Advice: Don't take shortcuts - do the hard work to incorporate your internal data, as it will pay off in the end.” - Amy Brown, Founder & CEO | Authenticx