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

Taiwan's healthcare AI story matters because public policy, payers and startups are converging: the market is projected to jump from NT$360 million in 2023 to NT$1.12 billion by 2030, a near threefold expansion that's fueling pilots across hospitals, insurers and national programs (Taiwan healthcare AI market projection (2023–2030)).

National initiatives - including NHIA's collaboration with Google to kick off AI-supported chronic care starting with diabetes - plus new MOHW AI centers that tackle real‑world deployment, regulation and reimbursement, are shifting AI from lab demos to clinical workflows (NHIA and Google AI-supported chronic care collaboration).

For professionals and beginners wanting practical skills to join this wave, the AI Essentials for Work bootcamp teaches promptcraft and workplace AI use cases to turn awareness into applied ability (AI Essentials for Work bootcamp registration), so local talent can help shape safe, scalable solutions.

Program	Details
AI Essentials for Work	15 weeks; Courses: AI at Work, Writing AI Prompts, Job-Based Practical AI Skills; Early-bird $3,582; AI Essentials for Work syllabus & AI Essentials for Work registration

To pick the top 10 prompts and use cases, selection focused on tangible Taiwan signals: strong market momentum and adoption trends, clinical readiness in imaging and monitoring, data and compute capacity, and governance/feasibility - each candidate had to promise measurable operational gains (faster diagnosis, fewer readmissions, or staff‑time saved) and a clear route to clinical validation or scaling.

Market strength and investment appetite guided the shortlist (Taiwan artificial intelligence in healthcare market projections and trends report), while on‑the‑ground capability - from vast EHR and imaging datasets to hospitals building local AI infrastructure - determined technical feasibility; note how NHRI and CGMH are expanding accelerated computing to train and deploy real clinical models (NVIDIA blog on NHRI and CGMH accelerated computing for clinical AI in Taiwan).

Risk filters (data privacy, algorithmic bias, rural connectivity gaps) and likely operational ROI completed the rubric, producing a practical, Taiwan‑centered list that favors deployable impact over academic novelty - imagine models trained on the country's “mountain” of imaging and EHR data being used in everyday clinics.

Selection criterion	Research basis
Market potential	National forecasts and investment trends
Clinical maturity	Early wins in radiology, pathology, monitoring
Data & compute readiness	Large EHR/imaging datasets; NHRI/CGMH accelerated computing
Governance & equity	Privacy, bias concerns, rural infrastructure limits
Operational impact	Reduced readmissions, staffing relief, workflow gains

“The use of AI in healthcare will fundamentally change the way we approach disease prevention and treatment,” said Dr. Hung‑Yi Chiou, director of the Institute of Population Health Sciences (IPHS) at NHRI.

Continuous respiratory monitoring is moving from bulky ward monitors to nimble, cloud‑ready auscultation tools in Taiwan, where makers and startups are turning digital stethoscopes into remote‑care enablers; IMEDIPLUS's Taiwan‑Excellence DS3011A pairs barcode‑linked recordings, an

organ map

, noise filtering, sound amplification and a visualized phonocardiogram so clinicians can see murmurs and share clips for consults or teaching (IMEDIPLUS DS3011A Taiwan Excellence product page).

Quanta's wireless Q‑steth (Q‑steth‑w1) and long‑standing Taiwanese instrument makers such as Spirit Medical and Asia Connection show local manufacturing depth for wireless and electronic stethoscopes, while telemedicine devices demonstrated at Medical Taiwan illustrate how lung and heart sounds can be streamed during home visits (Quanta Q‑steth‑w1 Wireless Digital Stethoscope Taiwan Excellence product page, Heroic Faith digital stethoscope dataset for Taiwan healthcare AI).

For Taiwan's clinics and community care teams the practical

so what?

is clear: digitized auscultation plus datasets and AI analytics can speed remote triage, support follow‑up after discharge, and shrink preventable readmissions by turning sound into searchable, shareable clinical evidence.

Device / Maker	Key capabilities
IMEDIPLUS - DS3011A	Patient barcode linkage, organ map, noise filtering, amplification, visualized phonocardiogram, recording storage/sharing
Quanta Computer - Q‑steth‑w1	Wireless digital stethoscope (Taiwan Excellence award)
SyncVision / Telemedicine devices	Modular telehealth tools that include stethoscope functionality for home and remote care

AESOP Technology's MedGuard (branded RxPrime) is shaping Taiwan's bedside decision support by catching the kinds of prescribing mistakes that “happen every two seconds”: its dual‑AI engine analyzes diagnoses, age, gender and hospital‑level patterns to flag wrong‑drug, look‑alike/sound‑alike and other prescribing errors in real time and offer alternative recommendations (AESOP Technology RxPrime prescription‑error detection and decision support).

The system is built on a massive knowledge base - 3.2 billion coded prescriptions and 2.4 billion association rules - and a federated clinical study with Harvard Medical School and Taipei Medical University reported accuracy between 79% and 85%, underscoring transferability across settings; a separate Taiwanese evaluation shows MedGuard already reviews over 250,000 prescriptions a year for more than 200 doctors with strong user satisfaction (PubMed study: ML‑based clinical decision support reduces alert fatigue and wrong‑drug errors, Taipei Medical University case study: MedGuard deployment and acceptability).

For Taiwan's hospitals and community clinics the payoff is concrete: real‑time EHR‑integrated safety nets that can turn a mountain of prescribing data into a point‑of‑care guardrail to prevent harm and lower downstream costs.

Metric	Value
Model accuracy (study)	79%–85%
Prescription database	3.2 billion records; 2.4 billion association rules
Real‑world use	>250,000 prescriptions/year; >200 doctors; acceptability >60%; satisfaction >85%
Funding (pre‑A)	US$2.95M

“Our solution is revolutionary and generalizable.” - Jim Long, CEO, AESOP Technology

DKABio (Dataa Development Co.) turns Taiwan's deep national datasets into everyday nudges: their AI-powered health management kit pairs a digital twin with an AI Health Manager to move users from long‑range risk forecasts to simple, actionable habits - for example, the app can “push” a daily badminton task that maps to long‑term cardiovascular or metabolic risk reduction.

Built on algorithms trained with more than 5 million Taiwanese records spanning two decades, DKABio offers whole‑body risk prediction across 15 common diseases and has evolved since its Medical Taiwan 2025 debut from a 10‑year risk model into a behaviour‑oriented manager that plans exercise, rankings and personalised goals (DKABio AI‑powered health management kit).

Available in Taiwan with plans to add dietary coaching and international expansion, the platform exemplifies how population‑scale NHI data can be turned into daily, preventive care that feels less like a report and more like a coach.

Feature	Detail
Diseases modelled	15 common diseases
Training data	>5 million Taiwanese records over 20 years (NHI, hospitals, exam centers)
Primary approach	Dual‑agent: digital twin + AI Health Manager
Horizon	Initially 10‑year risk forecasts → now daily personalised actions
Availability	Currently in Taiwan; global rollout planned

“Our data bio AI kit combines two AI agents - our digital twin and our AI Health Manager,” said Yung Ching (Yoshi), Software Product Manager at DKABio.

NHIA's AI-on-DM program with Google Cloud is scaffolding a national, data-driven approach to Type 2 diabetes by analyzing two decades of NHIA tests, claims and insurance records to create severity-based risk ranks and personalized care plans for roughly 1.3 million Taiwanese - aiming to reach over two million by 2026 - and embedding those risk categories into the Universal Family Physician Program 2.0 so clinicians can prioritize early intervention; a companion Gemini‑based agent will surface trusted, personalized guidance inside the Taiwan My Health Bank app to support patient self‑management.

Built on Google Cloud tools and Google Health models, the five‑year partnership also emphasizes privacy: NHIA will anonymize records into secondary data before cloud processing, with storage tied to Google infrastructure in Changhua County, a concrete reminder that national‑scale AI projects require both massive datasets and clear data governance.

For Taiwan this is more than tech for tech's sake - the work aims to shift funding toward value‑based care by matching resources to individual risk and turning population data into timely, actionable care plans (Google Cloud blog on AI for Type 2 diabetes in Taiwan, NHIA official collaboration details for the AI-on-DM program, Taipei Times coverage of Taiwan AI diabetes program).

Metric	Value
Current target population	~1.3 million (goal: >2 million by 2026)
Training data horizon	~20 years of NHIA tests, claims and insurance data
Partnership length	Five years
Platform / tools	Google Cloud, Gemini / MedLM on Vertex AI
App integration	Taiwan My Health Bank (Gemini agent)
Data handling	Anonymized secondary data; processing/storage in Changhua County data center

“AI has the power to transform healthcare in Taiwan by making it more personalised to individual needs,” said NHIA Director General Dr. Shih Chung-liang.

AILabs.tw is partnering with the Taiwan Centers for Disease Control to turn manual microscopy - the gold standard that requires experienced microscopists - into faster, more standardized automated malaria microscopy and computer‑vision diagnostics by tackling two practical problems: too few medical technologists and unlabeled clinical images.

Their two‑phase pipeline first uses unsupervised clustering to surface candidate infected red blood cells for expert review, then trains deep‑learning classifiers with labels curated through a bespoke annotation system that preserves the retiring master's expertise (AILabs warns there's effectively:

only one retiring master

able to confirm difficult diagnoses).

That workflow maps tightly to clinical needs: microscopy must deliver results within hours to guide immediate treatment, and AI that speeds detection, flags species, and narrows confirmation tasks can help Taiwan CDC and hospitals maintain rapid, accurate care while training the next generation of lab staff.

Learn more about AILabs.tw's Malaria Diagnostics Project and Taiwan CDC's malaria guidance to see how automated detection and expert‑in‑the‑loop labeling are being used to protect Taiwan's public health (AILabs.tw Malaria Diagnostics Project - Automated Malaria Microscopy, Taiwan CDC Malaria Overview - Malaria Guidance).

To scale hospital operational AI from pilots into routine care, the Ministry of Health and Welfare recently approved subsidies for 16 medical institutions to establish three types of AI centres - responsible‑AI implementation centres, clinical AI verification centres, and AI impact research centres - after a competitive round that drew 30 applications by August 1; the move is explicitly aimed at easing medical manpower shortages, improving long‑term care policy design and sustaining the National Health Insurance system while advancing certification and reimbursement pathways (Euroview analysis of Taiwan MOHW AI centres, Taiwan Ministry of Health and Welfare 2025 AI centres announcement).

Backed by leading hospitals such as National Taiwan University Hospital and Taipei Veterans General Hospital, these centres create practical infrastructure for verification, responsible deployment and impact measurement so that operational tools - everything from model validation pipelines to post‑deployment monitoring - can be tested and reimbursed in a coordinated way; think of a national experiment in safe, hospital‑grade AI rather than isolated demos, with implications for workforce relief, system efficiency and even Taiwan's international competitiveness.

Item	Value / Role
Approved institutions	16 hospitals (selected from 30 applicants)
AI centre types	Responsible AI implementation; Clinical AI verification; AI impact research
Goals	Address manpower shortages, optimize long‑term care, support certification & reimbursement, sustain NHI

The Executive Yuan's Office of Science and Technology has been the linchpin for turning Taiwan's scattered biobanks into an R&D powerhouse: the National Biobank Consortium, launched in 2019, harmonizes more than 30 biobanks and close to 4.5 million samples from roughly 460,000 participants to give researchers and industry a legally compliant, high‑volume resource for precision medicine (Launch of the Taiwan Precision Medicine National Biobank Consortium).

That vertical‑and‑horizontal integration - outlined in the Taiwan Biobank 3.0 transformation framework - aims to link biospecimens with other health data (NHI claims, EHRs, cancer and rare‑disease registries) while operating under the Biobanks Act and strict information‑security rules so privacy and ethics are baked into access and use (Taiwan Biobank 3.0 transformation study, Biobanks Act and information‑security regulation in Taiwan).

The so‑what: this coordinated dataset gives Taiwanese clinicians, startups and multinational partners a rare, interoperable substrate for accelerating translational studies and AI‑driven drug discovery without sacrificing participant protections.

Item	Value
Biobanks in consortium	Over 30 (National Biobank Consortium)
Samples	~4.5 million
Participants	~460,000
Next integration targets	NHI database, EHRs, cancer registry, rare disease database
Governance	Biobanks Act; Human Biobanks information‑security regulation

Taiwan's push to marry nationwide policy with carrier know‑how is visible in Executive Yuan–backed, public‑private 5G telemedicine efforts that pair Far EasTone's 5G Telemedicine Platform with hospital partners to bring specialist care, real‑time remote monitoring and even ambulance HD video streams to hard‑to‑reach communities - an approach the Ministry of Digital Affairs highlights as a joint development between FET and medical institutions (Ministry of Digital Affairs 5G Telemedicine Platform (Taiwan)).

Far EasTone's rollout showcases practical benefits: virtual health‑card integration, interdisciplinary video consults, and a 5G ambulance program that completed over 500 cases with 100% remote‑guidance activation and >90% communication satisfaction, while telemedicine logged 40,742 consultations in 2023 (25,606 video; 15,136 serving rural areas) as operators aim to cover all 15 counties and extend services to 52 rural health centers and 22 hospitals (Far EasTone 5G telemedicine and ambulance program).

Analysts note 5G's ultra‑low latency and massive connectivity make these advances credible foundations for next‑step scenarios such as remote diagnosis, real‑time multidisciplinary collaboration and, ultimately, latency‑sensitive procedures that require surgeon‑grade video and device control (LabInsights: How 5G enables telemedicine in Taiwan), turning a national network into a practical lifeline for islandwide emergency and specialty care.

Metric	Value
2023 telemedicine consultations	40,742 total (25,606 video; 15,136 rural)
5G ambulance cases (by end 2023)	>500 cases; 100% remote‑guidance activation
Communication satisfaction (ambulance)	>90%
Target coverage	All 15 counties; 52 rural health centers; 22 hospitals

Taiwan's recent wave of hospital cyberattacks has pushed cybersecurity monitoring from checklist item to mission‑critical practice: after the CrazyHunter ransomware strikes that crippled systems (one attack paralyzed over 600 computers at Mackay Memorial Hospital), the Ministry of Digital Affairs teamed with MOHW to roll out countrywide Endpoint Detection and Response (EDR) across all medical centers, run 11‑hospital cyber‑defense drills, and invest in talent, institutional guidance and tighter inspections to harden anomaly detection and incident response (MODA announces nationwide hospital protection measures).

Practical defenses now emphasize behavioral monitoring, real‑time alerts and Zero Trust patterns to spot insider misuse and lateral movement early - shifting detection from retrospective forensics to live anomaly hunting - and technical analyses of CrazyHunter show why continuous monitoring and AD‑focused detection are essential (CrazyHunter ransomware hospital technical case study).

The takeaway for Taiwan's hospitals: layered telemetry and anomaly detection can mean the difference between a brief outage and a systemic shutdown that forces paper‑based care and risks patient data being sold online.

Measure	Detail
EDR deployment	Installed at all medical centers
Drills	11 hospitals to participate in end‑of‑year cyber‑defense exercises
Workforce	Talent development and white‑hat engagement
Governance	Institutional guidance and enhanced inspections

“Under [ransomware] attacks, we are concerned that hospitals could be paralyzed, posing a major risk to Taiwan, and that personal data within them could also be leaked.”

Taiwan's Ministry of Health and Welfare is steering a cautious, rules‑forward path for clinical AI: healthcare AI that touches diagnosis, treatment or workflows is treated under existing medical‑device rules and the Medical Devices Act, with the TFDA already issuing AI/ML software guidance and checkpoints for inspection and registration to ensure clinical validation and patient safety (Taiwan Ministry of Health and Welfare AI in healthcare official updates).

At the same time data and privacy rules are tightening - PDPA reforms and the incoming Personal Data Protection Commission mean developers must bake consent, minimization and secure handling into pipelines before models ever see live patients.

National coordination is evolving too: the NSTC's draft AI Basic Law and government action plans codify human‑centred principles (privacy, transparency, fairness, accountability) and ask ministries to adapt sectoral rules rather than leave healthcare to a legal void, so hospitals and startups should expect device‑style approvals, clinical trials, vendor oversight and clear liability expectations rather than permissive sandboxes (practical legal overview and sector guidance on Taiwan AI regulations).

In short: clinical impact in Taiwan requires technical rigor plus regulatory choreography - think validated models, TFDA‑aligned evidence, PDPA compliance, and documented governance before an AI can move from pilot to the bedside.

Authority / Initiative	Role
Taiwan Ministry of Health and Welfare (MOHW) AI in healthcare official updates	Healthcare supervision; policy and approval coordination for AI in medical settings
TFDA (Taiwan FDA)	Regulates medical devices & AI/ML medical software; published registration and inspection guidance (AI/ML checkpoints)
PDPA / PDPC	Personal data protection framework; PDPA amended May 2023 and PDPC preparatory office established for stricter data governance
NSTC Draft AI Basic Law and Taiwan AI regulatory guidance	Sets national AI principles (privacy, transparency, fairness, accountability) and a risk‑based coordination framework for sectoral regulators

For beginners eager to join Taiwan's AI‑health revolution, start small and practical: learn the workplace basics of promptcraft and model oversight, experiment with hands‑on pilots, and connect with the institutions actually deploying tools today.

NHIA's AI‑on‑DM national diabetes program with Google Cloud shows how an integrated pipeline and a Gemini‑based agent in Taiwan My Health Bank can nudge millions toward better care (NHIA and Google Cloud AI‑on‑DM national diabetes program details), while Medical Taiwan 2025 proved many vendors already have clinic‑ready solutions - from pre‑consultation LLMs to edge imaging systems - so lightweight experiments can rapidly surface high‑value use cases (Medical Taiwan 2025 AI showcase and clinic‑ready vendor solutions).

Balance technical learning with ethics and security, and get practical fast: the AI Essentials for Work bootcamp teaches prompt use, workplace AI applications, and real projects to build credibility with hospitals and public programs (AI Essentials for Work bootcamp registration).

Picture the payoff as a single trusted agent in a patient's pocket - personalized guidance that once seemed futuristic but is now being rolled out nationally.

Program	Length	Early‑bird cost	Registration
AI Essentials for Work	15 weeks	$3,582	Register for AI Essentials for Work · AI Essentials for Work syllabus