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

AI matters for Chilean healthcare in 2025 because the technology is already moving from pilots into everyday care - from telemedicine and connected-care platforms that shrink wait times to government systems that must balance speed with fairness.

Chile's national API-first strategy for health data lays the groundwork for scalable AI-driven workflows (Chile national API-based connectivity strategy), while on-the-ground governance lessons - like SUSESO's work to speed ~200,000 medical-claims decisions - show why procurement rules and human oversight matter (AI procurement and governance in Chile's SUSESO).

International partners also stress responsible adoption as essential to turn promise into safer outcomes (Health AI Agency mission and responsible AI in Chile and Uruguay), creating opportunity for clinics, startups and teams to upskill for practical, ethical AI use.

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“AI can save lives, but we must get the regulations right.”

Chile's HealthTech AI landscape in 2025 is defined by three interlocking forces: a fast‑growing cloud services backbone, a surge in SaaS‑style clinical apps (notably for behavioral health and teletherapy), and a national push toward interoperability that focuses AI where it can actually help clinicians and patients.

Market studies show robust demand for cloud professional services as hospitals and startups migrate EHRs, imaging and analytics to flexible platforms - creating the compute and data lakes AI models need (Chile cloud professional services market report).

Separate research highlights a rising market for interoperable healthcare solutions as organizations invest in semantic and structural interoperability to stitch together those systems (Chile interoperable healthcare solutions market report).

On the delivery side, teleconsultation and the

“move away from WhatsApp”

to formal, ISO‑aligned telemedicine platforms reflect real change in workflows and patient access (Chile Digital Health program market intelligence).

Persistent challenges - data privacy, regulatory gaps, high up‑front costs and a cloud/AI skills shortage - mean the biggest wins will go to teams that pair pragmatic SaaS pilots with clear governance; picture a regional clinic swapping chaotic chat threads for a cloud EHR that feeds an AI triage system - small change, big difference.

Common AI product types in Chilean healthcare tend to be highly practical: cloud‑native EHR/SaaS platforms that centralize records and billing for clinics, AI‑assisted telehealth and diagnostic support that speeds virtual consults, and smart scheduling/optimization tools that cut waste in high‑cost services.

Local SaaS EHRs such as MEDILINK already package clinical records, appointments and admin workflows for small and mid‑size practices (Top EHR companies in Chile - MEDILINK and HealthAtom), while platforms like MediLink combine teleconsultation, NLP symptom triage and wearables integration to tighten the virtual waiting room and support AI‑assisted diagnosis (MediLink AI‑assisted telehealth platform).

Scheduling is a standout use case: Chilean hospital studies show two‑stage optimization approaches for chemotherapy reduced operational costs by 17–20% and improved care‑slot and lab‑slot utilization by roughly 9–22%, a vivid example of “small data science, big time savings” when beds, staff and medicines must align (Chemotherapy scheduling optimization study (Chile)).

From prescription auditing and no‑show prediction to automated billing and image analysis, the sweet spot for Chilean teams is pairing interoperable EHRs with narrow, measurable AI pilots that deliver cost and access wins fast.

Product type	Chile example	Typical benefit
EHR / Health admin SaaS	MEDILINK / HealthAtom	Streamlines records, appointments and billing for clinics
AI‑assisted telehealth & diagnostics	MediLink	Faster virtual consults, symptom triage, wearables integration
Scheduling & optimization	Chemotherapy scheduling study (Hospital Salvador, PUCC)	Reduced operational costs 17–20%; improved slot usage 9–22%

Representative companies to watch in Chile skew toward practical, clinic‑focused SaaS: HealthAtom - best known for its Dentalink and Medilink suites - is a standout example, offering cloud EHR, scheduling, telehealth and billing tools that already serve thousands of small and mid‑size clinics across Latin America; the company's site describes its platform and product family (HealthAtom Dentalink and Medilink EHR platform), while coverage from TechCrunch highlights that a clinic can be migrated to the cloud “within three hours” and that the platform processes tens of millions of appointments annually (TechCrunch article on HealthAtom cloud migration).

Backed by a $10M Series A to scale embedded payments and insurer partnerships, HealthAtom's reach (thousands of clinics, millions of annual appointments) makes it a practical bellwether for which operational problems - interoperability, payments and patient financing - are being solved first in Chile's HealthTech scene (LatAmList coverage of HealthAtom $10M Series A), a useful signpost for startups and clinics evaluating where to integrate new AI workflows down the line.

Metric	Value
Founded	2009 (sources vary)
Headquarters	Las Condes, Santiago, Chile
Products	Dentalink, Medilink (SaaS EHR, scheduling, telehealth)
Series A funding	$10M (Jan 2023)
Clients / scale	~6,500 clinics across LatAm; ~42M appointments processed/year
Employees / Revenue	Reported 120–259 employees; revenue listed ~$42M (sources vary)

“We are at the point where the sales tension occurs, at an information level,” said co‑founder and CEO Roberto León.

Beginners should treat Chile's emerging AI law as a practical playbook, not a distant theory: the draft bill (introduced in May 2024) uses a European‑style, risk‑based framework that can reclassify healthcare tools - diagnostic imaging, treatment‑recommendation systems and patient‑monitoring AIs - as “high risk,” triggering stricter obligations like clinical validation, robust risk‑management plans, clear documentation and meaningful human oversight (Chile AI bill analysis - pioneering policy and local limits).

At the same time, Chile's rules align with international practice - expect transparency duties, prohibited categories (social‑scoring, manipulative systems, mass biometric ID) and a push to map AI governance to standards such as ISO/IEC - for a practical summary of those compliance components see a concise framework overview (AI regulation in Chile: framework and high-risk requirements).

Reality checks from local analysts warn of gaps - unclear liability rules, privacy law limits and institutional capacity constraints - so start small: inventory every AI, run a basic risk assessment, document data and decision flows, and build an ethics/oversight channel that ties AI controls into existing cybersecurity and data‑protection obligations (an approach the AI Law project recommends as it links AI rules with Chile's cybersecurity and data laws) (Chile AI Law Project integrated compliance framework).

Think of compliance like a clinical chart: if a tool can affect diagnosis or treatment, you'll need evidence, audit trails and a named human reviewer before deployment - concrete steps that turn regulation from a cost into a trust‑building advantage.

Start with a short, practical checklist that maps directly to Chile's risk‑based AI framework: 1) conduct a Regulatory Gap Analysis to classify every AI by risk tier and spot missing controls (Chile AI regulation overview - Nemko); 2) inventory all deployed and draft systems (include models used in sourcing or procurement) and flag anything that can affect diagnosis, treatment or benefits - remember real Chilean projects are already deciding outcomes for roughly 200,000 medical claims, so small oversights scale fast (SUSESO procurement and governance lessons - World Privacy Forum); 3) stand up an AI governance structure (AI committee, named owner, and an ethics channel for reporting) and tie it to existing cybersecurity and data‑protection roles; 4) require documented clinical validation, testing, explainability and audit trails for any high‑risk tool; 5) bake in meaningful human oversight and vendor due diligence (procurement templates must weight vendor capabilities on bias, transparency and data protection, not just price); and 6) train staff, monitor models in production and keep a living compliance record so audits, incident reports and regulatory updates are easy to produce - these concrete steps turn Chile's emerging AI law from a compliance burden into a trust advantage (Chile AI Law project framework - Janus GRC).

Step	What to do	Chile note
Inventory & Classification	Map systems to the four-tier risk scheme	Prioritise healthcare diagnostics and treatment AIs
Regulatory Gap Analysis	Compare controls to Chile's draft AI law and ISO/IEC standards	Use as baseline for remediation plans
Governance & Ethics Channel	Assign owners, create reporting/escalation workflows	Start simple (ethics channel) then scale to GRC
Validation & Documentation	Clinical testing, audit trails, model versioning	Required for high‑risk healthcare tools
Procurement & Vendor Due Diligence	Require bias, transparency and security evidence	Balance cost with responsible‑AI criteria

Chile's national push to pair AI with real computing muscle has moved from policy talk to bricks‑and‑rack reality - a coordinated CORFO and Ministry of Science program funneled a national package (14 billion pesos) into two purpose‑built AI supercomputing centres and a competitive co‑funding line that offers up to US$7 million per project, explicitly to boost model training and industrial AI across sectors including health (Chile supercomputing program and co-funding for AI projects).

The University of Chile will host the SCAI‑Lab at the NLHPC - part of a 65‑member consortium - where more than half of the initial Corfo support goes straight to high‑performance machines for large‑scale training and inference, making advanced imaging, population‑scale analytics and faster model validation realistic for hospitals and startups (SCAI‑Lab University of Chile consortium and goals).

This builds on recent upgrades - Leftraru Epu quadrupled national compute and includes thousands of cores and petabytes of storage - so Chilean teams can move from prototype to production without shipping sensitive data abroad; one vivid sign of that leap is the massive cooling needed to run these racks (Leftraru Epu requires over 100 litres of water per minute at 10°C), a reminder that compute scale brings practical infrastructure needs as well as capability (Leftraru Epu national supercomputer specifications).

For healthcare, the net result is clearer: domestic training and inference capacity, funded access for universities and companies, and an institutional pathway to turn clinical AI pilots into scalable, auditable services inside Chile's legal and data‑sovereignty boundaries.

Facility / Program	Location	State support	Primary focus
SCAI‑Lab (Supercomputing Laboratory for AI)	NLHPC, Universidad de Chile (Santiago)	US$7M initial Corfo support; part of 14B CLP national package	Large‑scale model training and inference; research & industry (incl. healthcare)
CSIAA (Supercomputing & Applied AI Centre)	Tecnoera / Valparaíso Region	State investment from Corfo (part of 14B CLP)	Accessible, scalable inference services, validation, industry collaboration
Leftraru Epu (NLHPC)	NLHPC, Universidad de Chile	National investments (ANID, CMM contributions)	7,360 cores, 260 TFLOPS, 4 PB storage - national HPC for research and applied AI

“AI and high‑performance computing (HPC) have a symbiotic relationship: modern AI…could not have emerged without the power of HPC,” said Ginés Guerrero, executive director of NLHPC.

Turn strategy into repeatable steps by combining Chile's health‑IT roadmap with a pragmatic AI implementation playbook: start with a short readiness assessment and an executive sponsor to

develop public health capacity

(RAND's first objective) and map every use case to risk and value so early pilots target high‑impact, low‑complexity problems; next prioritise interoperable EHRs and a pharmaceutical/device tracking backbone so data flows are consistent across FONASA/ISAPRE settings (RAND's ten‑year vision), then build scalable infrastructure and data pipelines (the HP six‑phase approach to infrastructure, data and MLOps) so models can be trained and audited locally within Chile's growing compute ecosystem; once the data foundation exists, deploy narrow clinical pilots (teletriage, scheduling, prescription auditing) with clear clinical validation, human oversight and vendor due diligence guided by Chile's National AI Strategy and regulatory principles; finally, treat governance and continuous monitoring as ongoing phases - document audit trails, measure ROI and scale only when safety, explainability and connectivity gaps are closed.

These steps respond to Chilean realities (in 2013, roughly 80% of records were paper‑based and rural connectivity gaps persist) and turn high‑level goals into a practical, phased path for clinics and startups to move from prototypes to auditable production systems (see RAND's roadmap and HP's implementation phases for detailed checklists and timelines).

Step	What to do	Chile note / source
1. Strategic alignment	Readiness assessment, executive sponsor, priority use cases	Aligns with RAND objective to develop capacity; HP Phase 1
2. Interoperable records & tracking	Implement EHR standards + pharma/device tracking	RAND: interoperable EHR and tracking (2016–2023 targets)
3. Infrastructure & data	Deploy scalable compute, data lake, quality pipelines	HP Phase 2–3; ties to Chile AI enabling factors
4. Pilot AI services	Small, measurable pilots (telehealth, scheduling, auditing)	Start with low‑complexity, high‑impact pilots per HP guidance
5. Deploy & MLOps	Production rollout, monitoring, CI/CD, retraining	HP Phase 5; ensure clinical validation and oversight
6. Governance & scale	Audit trails, ethics channel, regulatory compliance, scale plan	Matches Chile National AI Strategy and RAND governance advice

Concrete Chilean examples show how real-world projects translate strategy into usable tools: local SaaS platforms such as MEDILINK (HealthAtom) are already framed as comprehensive EHR and admin suites that streamline patient journeys and clinic operations (MEDILINK EHR software - Top electronic health records in Chile), while telehealth vendors like Careyou and home-care platforms such as ECR Salud demonstrate how virtual care and remote monitoring fit into everyday workflows; another notable strand is interoperability experiments - HealthChain's blockchain approach promises immutable medication records and faster record sharing between providers.

Lessons from very large rollouts, like the MHS GENESIS program, underline the operational requirements - training, cybersecurity and staged deployment - needed to make EHR projects safe and sustainable (MHS GENESIS EHR implementation lessons and operational requirements).

For targeted AI pilots, prescription‑auditing tools are a natural starting point to catch dosing errors and drug interactions in real time, delivering clear patient‑safety wins without requiring full-scale replacements of legacy systems (AI prescription auditing use case for medication safety in Chile); the practical takeaway is simple: pair a focused AI pilot with an interoperable EHR and clear governance, and small, verifiable improvements become the bridge from prototypes to trusted production services.

Project / Company	Focus	Key takeaway
MEDILINK (HealthAtom)	SaaS EHR & healthcare administration	Comprehensive EHR that improves patient journeys and operations
Careyou	Telehealth software	Supports patient and clinical management for virtual care
ECR Salud	Digital home-care platforms	Improves access to health services delivered at home
HealthChain	Blockchain for health data	Immutable medication records and interoperability

For beginners in Chile, the fastest path from curiosity to impact is pragmatic: start small, map what your clinic or team already uses, and run a tightly scoped pilot that pairs an interoperable EHR with a single, measurable AI use case (scheduling, prescription‑auditing or teletriage) while keeping a human in the loop - lessons from SUSESO's procurement experience show why balancing vendor cost with robust bias, transparency and data‑protection criteria matters when decisions affect thousands (SUSESO processed roughly 200,000 claims last year) (SUSESO procurement and governance lessons).

At the same time, align pilots with Chile's National AI Strategy and the draft AI Bill so risk classification, clinical validation and meaningful human oversight are baked in early (Chile's National AI Strategy and draft AI Bill).

Practical upskilling matters too: a short, applied course such as the AI Essentials for Work bootcamp teaches promptcraft, tool use and workplace AI workflows that turn pilots into auditable, repeatable services.

In short: inventory your AI, prioritise low‑complexity/high‑impact pilots, require vendor evidence on bias and explainability, and invest in hands‑on skills so small, safe wins build trust and create the footing to scale within Chile's evolving legal and compute ecosystem.

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“AI can save lives, but we must get the regulations right.”