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

Indonesia's healthcare system is riding a fast-moving AI wave in 2025: near-universal coverage under JKN and national investments have combined with a booming AI market and data‑center buildout to make AI a practical lever for faster diagnoses, smarter triage, and lower costs.

Major reports show mass workplace adoption and large infrastructure pledges - backed by initiatives like Sahabat‑AI and cloud platforms - that are bringing language‑aware models and clinical decision support into radiology, pathology, and predictive care; see Introl's analysis of the country's AI market and infrastructure surge and the Philips Future Health Index 2025 for frontline perspectives on trust and workflow impact.

With SATUSEHAT and rising EMR coverage enabling real‑time analytics, AI is no longer hypothetical in Indonesia's hospitals - it's a tool for widening access and cutting patient backlogs, provided design, governance, and clinician trust keep pace.

“To unlock that potential, we must design with empathy, build trust, and ensure responsible implementation that truly serves the needs of both patients and healthcare professionals.” - Astri Ramayanti Dharmawan, President Director, Philips Indonesia

AI is already shifting clinical care in Indonesia from proof‑of‑concept to everyday impact: researchers at BRIN are using image‑analysis models to spot plasmodia in a thin patch of blood and to class histopathology for cancer studies, while deep‑learning work on microbial images is accelerating drug‑discovery pipelines - examples that show AI moving from lab notebooks into diagnostics (see the report on AI for enhanced diagnosis).

In hospitals, image‑first workflows are also changing: an AI deployment at a leading Jakarta hospital cut radiology image‑analysis time by roughly half, freeing radiologists to focus on complex cases and improving patient throughput (see the Jakarta radiology case study).

Those gains matter most for Indonesia's dispersed population - AI can extend specialist-level reads to remote clinics and power smarter telemedicine triage - but only if models are trained on local data, integrated into workflows, and governed to limit bias and privacy risk as experts have warned.

The net effect is pragmatic: faster, more consistent reads and new research tools that turn vast image and genomic datasets into usable clinical insights, provided hospitals pair technology with clinician oversight and quality data pipelines.

“Imagine we are in the future when a healthcare worker in a remote village on a distant island in Indonesia can detect eye diseases, brain tumors, dengue fever, or malaria with the help of a small device and then, within seconds, receive a recommendation for a precise diagnosis from an accurate algorithm.” - Professor Hanung Adi Nugroho, UGM

Indonesia's National AI Roadmap - laid out in a nearly 200‑page White Paper drafted by a 443‑member task force - turns abstract promise into a practical timetable and clear priorities that matter for hospitals and clinics: short‑term “quick wins” for 2025–2027 and longer horizons through 2045 focus resources on talent, research and infrastructure so AI can actually improve early disease detection, remote patient monitoring, and medicine and vaccine distribution.

The plan sets ambitious targets - produce 100,000 AI talents per year and make 20 million citizens AI‑literate by 2029 - while building a cross‑sector open sandbox and sovereign cloud infrastructure (HPC, GPUs/TPUs and green data centres) to keep patient data secure and enable local model training; read the roadmap summary for policy directions and the draft ethics framework for governance and safeguards.

Financing is staged too, blending state budgets, private investment and a Danantara‑led Sovereign AI Fund so pilots can scale into hospital workflows without leaving smaller clinics behind, and public consultation aims to convert high‑level principles into implementable rules that clinicians and IT teams can follow.

“The preparation of this White Paper serves as a foundation for formulating policy and regulatory strategies to guide the development and use of AI in Indonesia.” - MLex reporting on the White Paper and ethics framework

Indonesia's digital health backbone is taking shape around SATUSEHAT, a Platform‑as‑a‑Service national exchange that promises to stop patients from lugging paper records between facilities by integrating hospitals, clinics, labs and pharmacies into a single repository linked to PeduliLindungi; see the Ministry of Health's SATUSEHAT launch page for the official roadmap.

Built to streamline referrals and unlock real‑time analytics, the platform already includes a logistics module - SMILE/SATUSEHAT Logistics - that tracks vaccine and medicine stocks across more than 10,000 facilities and can manage hundreds of millions of doses, helping planners avoid stockouts in emergencies.

SATUSEHAT's phased data plan (from registration and diagnosis to lab, radiology and genomics) and planned BPJS integration are designed to make EHRs interoperable nationwide, while a security partnership with BSSN and mandatory standards aim to protect patient data; InterSystems and others are positioning tools to speed hospital onboarding.

Implementation still faces real-world limits - surveys show a minority of hospitals have strong EMR systems and many primary care sites lack reliable internet - so the “cloud plus standards” approach must be paired with connectivity, training and incentives if SATUSEHAT is to deliver on its promise of one‑click, cross‑facility records and smarter AI‑driven care.

“Through this integration, we will integrate patient health data from all health facilities (Hospitals, Clinics, Labs, and Pharmacies) into PeduliLindungi. So that patients referred to hospitals do not need to bother sending medical documents containing lab results/diagnoses or repeating lab tests...” - Minister Budi Gunadi Sadikin

Indonesia's push into AI-powered decentralized clinical trials (DCTs) is turning research access into a nationwide capability rather than an urban luxury: the Ministry of Health's 2025 initiative pairs AI-enabled imaging and remote-monitoring pilots with mobile apps and continuous glucose monitor (CGM) integrations - Kakao Healthcare's “Pasta” diabetes pilot at RSUI is a concrete example - so patients can participate from home or a remote clinic, improving diversity of study populations and lowering trial costs; read the Ministry's announcement and coverage of the national DCT pilots on Inavimed for program details.

Central to scaling those pilots is a new Clinical Research Centre (CRC) network backed by more than 3,000 hospitals and research institutions that standardises protocols, supports AI integration for diseases from hypertension and diabetes to TB and cancer, and leverages SATUSEHAT for secure patient data flow so trials can recruit and monitor participants across islands without costly physical visits.

The combined model - regulated sandboxes for safe experimentation, hospital AI deployments, and a CRC‑coordinated DCT infrastructure - promises to make Indonesia a regional blueprint for inclusive, data-driven clinical research.

“With AI and mobile health platforms, we aim to decentralize participation in clinical trials and make them accessible to populations outside urban centers,” - Ministry spokesperson

Indonesia's 2025 pilots stitch together global tech and local hospitals into pragmatic tests: the Ministry's DCT announcement links an Australia‑based partner, Harrison.ai, to three national sites - RSPON Dr. Cipto Mangunkusumo (Jakarta), Dharmais Cancer Hospital, and Dr. M. Djamil (Padang) - to trial AI‑assisted radiology and pathology workflows, while Kakao Healthcare is piloting the Pasta diabetes app with CGM integration at RSUI to combine remote monitoring and AI lifestyle‑support; see the Ministry's DCT announcement for program details and Harrison.ai's clinical evidence and product updates for the company's radiology tooling.

These pilots aren't abstract pilots for PR: they respond to stark workforce gaps (Indonesia has about six radiologists per 1 million people) and aim to prove that AI + local integration can safely extend specialist reads, speed triage, and let patients join trials from home instead of long, costly hospital journeys.

“With AI and mobile health platforms, we aim to decentralize participation in clinical trials and make them accessible to populations outside urban centers,” - Ministry spokesperson

Ethics, trust and regulation are the linchpins that will determine whether AI in Indonesian hospitals improves care or merely shifts risk: a 54‑paper review found meaningful opportunity to raise patient outcomes and equalize services, but also flagged governance gaps that must be closed before scale IJSTM review of AI ethics in Indonesian hospitals.

That means tackling privacy, explainability and bias head‑on - global reviews note interpretability and bias as core ethical concerns - while shoring up the obvious attack surface of electronic medical record systems, where recent analyses expose security vulnerabilities that could turn helpful models into liability.

Practical steps include adopting PDPL‑aware synthetic data methods to enable model development without exposing identities, building explainability into clinical decision support, and training a new cadre of clinicians and technicians to act as independent model validators and auditors; Nucamp AI Essentials primer on synthetic data and the Nucamp Complete Software Engineering Bootcamp Path syllabus for career pathways offer concrete starting points.

Regulation should combine clear liability rules, mandatory security standards for EMRs, and regulated sandboxes so hospitals can test tools safely - because even a small, unexplained error in an AI read can erode clinician confidence faster than any policy can restore it.

Done right, ethics and regulation won't block AI in Indonesia; they'll make it trustworthy, useful, and fair for patients across the archipelago.

Bringing AI into Indonesian hospitals is as much an operational project as it is a technical one: models need high‑quality, representative data, stable cloud and EMR integration, clear validation pathways, and clinicians who trust outputs - yet fragmented records, gaps in explainability, and weak governance keep many promising pilots on the sidelines.

National and international reviews highlight familiar pain points - explainability, algorithmic bias, workflow fit and the need for clinical validation - while local market studies add a sharper, practical edge: Indonesia faces a large skills gap (an estimated 15,000 AI specialists short) and troubling data security pressure with over 1,000 reported healthcare breaches, which together make secure data pipelines and staff training urgent priorities (see the Ken Research market overview).

Adoption research also stresses that trust depends on governance and regulated evaluation: a scoping review in JMIR Human Factors maps 18 barrier/facilitator themes that hospitals must tackle (from transparency to funding), and an IJSTM 54‑paper ethics review flags governance gaps that can widen inequities if left unaddressed.

The combined takeaway is concrete: hospitals need interoperable data, explainable models, funded validation pathways, and clinician co‑design - otherwise even fast, accurate models will struggle to move from pilot to everyday patient care; a single vivid risk to remember is that poor data governance can turn a helpful diagnostic aid into a liability overnight.

Practical first steps for hospitals, developers and students in Indonesia begin with tight, local priorities: pick one high‑value use case - telemedicine, diagnostics or trial recruitment - that helps the 42% of Indonesians living in rural areas and leverages Indonesia's rich demographic data (see the EastAsiaForum roadmap), then build with local data and PDPL‑aware synthetic datasets to reduce bias and privacy risk (learn how to generate compliant synthetic cohorts).

Partner early with national pilots and the new Clinical Research Centre/DCT network so trials and hospital deployments have governance, interoperability and real‑world testbeds (the Ministry's AI‑powered DCT rollout shows how pilots, hospitals and industry can coordinate).

Invest in practical training - local instructor‑led courses or bootcamps for clinicians and data teams - to close the ~15,000 specialist gap and make implementation teams as strong as the models they deploy, and align models with trusted clinical content and validation pathways rather than treating AI as an oracle.

Start with low‑risk, high‑impact workflows (e.g., administrative automation or AI‑assisted reads with clinician oversight), build auditable evaluation metrics, harden data security against the 1,000+ reported healthcare breaches, and scale only after clinicians trust the outputs; imagine a rural clinician receiving an evidence‑backed diagnostic suggestion on a tablet within minutes - small pilots can make that everyday.

For concrete entry points, explore training providers and the Ministry's pilot partners to join existing sandboxes and trials.

“With AI and mobile health platforms, we aim to decentralize participation in clinical trials and make them accessible to populations outside urban centers,” - Ministry spokesperson

Indonesia's AI moment is real: massive infrastructure commitments, from Microsoft's headline investments to NVIDIA's $200M AI centre in Surakarta, and a booming market outlook mean hospitals can stop treating AI as an experiment and start folding it into everyday care - provided policy, skills and trust keep pace.

The white‑hot investment and language‑model work (Sahabat‑AI) give hospitals the compute and localisation needed to scale diagnostics and decentralised trials, but the IJSTM 54‑paper ethics review warns governance gaps and explainability shortfalls that could widen inequities if left unaddressed; hospitals will need regulated sandboxes, clear liability rules, and stronger data stewardship to translate momentum into safer patient outcomes.

Practical levers are education and hard security: public optimism and national strategy create demand, while targeted training programs - such as short professional pathways like the AI Essentials for Work bootcamp - can upskill clinicians and IT teams to validate and co‑design tools.

The road ahead blends bold infrastructure with methodical governance: build the data pipes, train the workforce, legislate the safeguards, and Indonesia can turn its $‑scale AI ambition into measurable health gains for the archipelago.

“Indonesians are not just users of AI, but creators and innovators.” - Vikram Sinha, quoted in Introl's report on Indonesia's AI revolution (Introl report on Indonesia's AI revolution)