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

Cambodia's healthcare sector can capture tangible benefits from AI trends now moving from conference demos to real pilots: diagnostic imaging, rapid triage and predictive analytics (spotlighted at HIMSS25) and tools that help spot fractures or prioritize ambulance needs (covered by the World Economic Forum) can expand access and stretch scarce clinical staff.

Practical steps - small, low‑cost pilots with clear ROI, strong data governance, and training - help ensure AI augments clinicians rather than adding burden, freeing time for bedside care instead of paperwork.

For a global view see HIMSS25 AI in Healthcare key trends and takeaways and the World Economic Forum analysis of AI transforming global health; teams wanting workplace AI skills can explore Nucamp's 15‑week AI Essentials for Work via the AI Essentials for Work syllabus (Nucamp) to build practical capabilities.

Bootcamp	AI Essentials for Work
Length	15 Weeks
Courses included	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost (early bird)	$3,582
Registration	Register for AI Essentials for Work (Nucamp) | AI Essentials for Work syllabus (Nucamp)

“One thing is clear – AI isn't the future. It's already here, transforming healthcare right now. From automation to predictive analytics and beyond – this revolution is happening in real-time.” – HIMSS25 Attendee

Selection focused on practical impact for Cambodian health systems: priority went to tools and prompts that score high on feasibility, ethical governance, and local capacity to train and validate - criteria drawn from the WHO's AI ethics and governance guidance and regional workshops that emphasized prompt engineering and hands‑on learning.

Solutions were screened for alignment with WHO recommendations on stakeholder engagement, post‑release auditing and clear task definitions (WHO AI ethics and governance guidance for large multimodal models (January 2024)), for adherence to the ethical concerns and developer practices flagged by researchers in Integrating ethics in AI development (Integrating ethics in AI development - BMC Medical Ethics study), and for usability and prompt‑engineering readiness highlighted in the UP Cambodia workshop (AI in Healthcare UP Cambodia workshop summary slides).

Emphasis was placed on low‑cost pilotability, measurable ROI, data protection, and clear prompt templates - down to specifying context, desired output and tone - so each shortlisted use case can be tested quickly and responsibly in Cambodian clinics and provinces.

Metric	Value
Total participants trained (UP Cambodia)	2,700
Workshop participants (AI‑AW, AI‑RE, etc.)	1,200
Brown Bag Series participants	750
Conference attendees (total)	250

“AI won't replace you, but someone empowered by AI undoubtedly will.”

AI-powered diagnostic imaging can be a practical, high‑impact entry point for Cambodian hospitals and provincial clinics: platforms that triage and prioritize scans free radiologists from routine backlog, trigger care‑team activation for time‑critical cases, and feed structured follow‑up recommendations into EHRs.

Solutions like Aidoc radiology AI platform (which automates quantification and bi‑directional care‑team alerts) and Viz.ai radiology real-time prioritization (real‑time flagging and worklist prioritization) show how a single mobile alert can move a suspected stroke from waiting room to CT and specialist faster than before - an operational change that's easy to pilot and measure locally.

For screening programs, DeepHealth AI diagnostic solutions report measurable gains - up to a 21% increase in cancer detection in large mammography reviews, earlier lung cancer detection in population screening, and improved prostate sensitivity and read‑time reductions - evidence that AI can extend scarce specialist capacity if paired with governance, training and clear referral pathways.

A well‑scoped pilot (one modality, one hospital, simple ROI) can make these benefits visible to funders and clinicians across Cambodia.

Outcome	Reported Result
Mammography cancer detection	~21% increase (study of 575,000+)
Lung screening earlier detection	76% caught at earlier stage (program data)
Prostate diagnostic sensitivity	97% with AI vs 92% without
MRI/MRI read workflow	~37% reduction in read time

“At DeepHealth, we are harnessing the transformative power of AI to create cutting-edge solutions that are deeply rooted in real-world clinical needs.” – DeepHealth press release

Telemedicine and remote patient monitoring are a practical way to stretch Cambodia's limited clinician workforce: connectivity upgrades and AI can help patients in rural provinces consult urban specialists without the cost and time of travel, while AI-driven triage and virtual assistants can prioritize care and flag patients who need in-person follow‑up.

Cambodia's telemedicine record isn't new - an early email‑based program linked volunteer Boston specialists with two remote village communities, showing the lasting value of simple, low‑bandwidth consults that also serve as on‑the‑job training for local staff; modern pilots can add AI triage, wearable data feeds and clear ROI metrics to make those consults faster, safer and easier to scale while protecting patient data and clinician workflows.

5G and AI's Bold Leap in Healthcare - Cambodianess article

Mercer: The future of AI in telemedicine - AI triage, remote monitoring, and personalized virtual support

World Telehealth Initiative - Cambodia telehealth program

Study	Key details
Telemedicine by email in remote Cambodia	Volunteer physicians (Brigham and Women's Hospital); J Telemed Telecare, 2005; DOI:10.1258/135763305775124858; PMID:16375794

Predictive analytics can shift Cambodia's dengue response from reactive scrambling to targeted preparedness: models applied to national surveillance data have shown they can forecast outbreak timing and size with striking accuracy - one tool

“predicte[d] 90% of the variance in peak magnitude by April”

even when fewer than 10% of seasonal cases have been reported - giving planners weeks to act rather than hours (Predicting Dengue Outbreaks in Cambodia - Emerging Infectious Diseases, 2019).

Complementary work applying an algorithm to Cambodia's routine surveillance likewise demonstrates early‑detection viability for major outbreaks, which can be translated into practical resource allocation: staged bed and supply prepositioning, targeted vector control in high‑risk districts, and surge staffing where models flag rising risk (Early detection algorithm for dengue outbreaks - PLOS ONE, 2019).

Study / Source	Key finding
Predicting Dengue Outbreaks in Cambodia (Emerg Infect Dis, 2019)	Model predicted ~90% of variance in peak magnitude by April when <10% of seasonal cases reported
An algorithm applied to national surveillance data (PLOS ONE, 2019)	Algorithm enabled early detection of major dengue outbreaks using routine surveillance
Review of dengue vectors in Cambodia (Parasites & Vectors, 2024)	Comprehensive synthesis of vector distribution, bionomics and control considerations

Pairing these proven analytic approaches with local entomological insights from recent reviews of Cambodian vectors helps ensure alerts are actionable, not just predictive - imagine knowing weeks ahead which district will likely need extra beds and insecticide teams, not merely a headline about

“more cases.”

Clinical decision support powered by ChatGPT and other large language models (LLMs) can be a practical, low‑cost way to boost diagnostic capacity in Cambodia - especially in provincial clinics that lack specialists - by rapidly summarizing long histories, suggesting broader differentials (LLMs often list 4–5 possibilities vs.

~1–2 from busy clinicians), and serving as a 24/7 “second opinion” for triage and care pathways; rigorous reviews show LLMs now approach clinician-level performance on many text‑based tasks but remain uneven on image‑heavy specialties, so any pilot should keep a clear human‑in‑the‑loop, local validation, and reporting plan (see the JMIR systematic review on diagnostic accuracy and the Mass General Brigham summary of ChatGPT performance).

Practical steps for Cambodia include small shadow‑mode pilots tied to measurable ROI and patient consent, clinician training to spot hallucinations, and using LLM outputs to standardize patient education and referral notes rather than replace final judgments - an approach Nucamp recommends in its guidance for low‑cost AI pilots.

Metric	Reported Result
LLM primary diagnosis accuracy (range)	25%–97.8% (systematic review)
Med‑PaLM 2 (MedQA)	~86.5% accuracy
Triage accuracy (best studies)	up to ~98%
GPT‑4 (text) vs radiology resident	~43% vs 41% (musculoskeletal cases)

“considerable diagnostic capabilities”

Operational automation - scheduling, billing, claims and fraud detection (Markovate) - is a practical, high‑leverage way for Cambodian clinics and provincial hospitals to cut administrative drag and redirect staff time to patients: Robotic Process Automation (RPA) bots can handle appointment booking, SMS reminders, eligibility checks and claims submissions while keeping EHRs in sync, and simple pilots often show rapid ROI (start with one workflow and measure).

Real results from RPA vendors underline what's possible in low‑resource settings: automated reminders can lower no‑shows by 15–20% and smooth front‑desk traffic (CareCloud robotic process automation for automated scheduling and reminders), bots dramatically reduce data‑entry errors and speed up revenue cycle steps (1Rivet robotic process automation for billing, claims, and accuracy), and end‑to‑end billing automation has cut denials and backlogs in published case studies while boosting collections (Auxiliobits RPA solutions for medical billing - MD TruCare case study).

For Cambodia this means low‑cost pilots that automate scheduling plus claim validation, paired with simple governance and monitoring, can free nurses from paperwork, speed reimbursements, and surface suspicious claim patterns for early fraud detection - turning bots into a steady, audit‑ready back office that runs 24/7.

Outcome	Reported result	Source
Reduced no‑shows	15–20% fewer no‑shows via automated reminders	CareCloud robotic process automation - automated scheduling & reminders
Improved data accuracy	Up to ~99% accuracy in reporting and reduced entry errors	1Rivet RPA in healthcare - billing, claims, and accuracy
Billing & collections impact	Denial rates cut sharply; collections up ~30% in case study	Auxiliobits RPA for medical billing - MD TruCare case study

Safe prescribing in Cambodia starts with simple audits: EHRs and EMR‑CDSS integrations can automatically flag drug‑drug and drug‑nutrient hazards, suggest safer alternatives, and surface dosing or lab‑value conflicts before a prescription leaves the clinic - turning a risky guess into a documented check (and catching surprising risks like grapefruit‑juice interactions).

Practical steps for provincial hospitals and pharmacies include rigorous medication reconciliation (capture OTCs, vitamins and herbal remedies), clinician training on EHR safety features, and monthly compliance reporting with a clear target - MGMA recommends a 95% medication/allergy review rate - so the system's alerts actually reduce harm rather than become background noise.

Pharmacy‑focused EHRs add workflow safeguards - duplicate‑therapy checks, allergy flags and dose‑range validation - helping pharmacists and prescribers coordinate care without slowing visits.

For actionable guides, see resources on EHR checks for drug‑drug and drug‑nutrient interactions from FHEA and practical advice on using medication safety systems in your EHR from MGMA, plus pharmacy‑EHR best practices that map directly to low‑cost pilots for Cambodian clinics.

Safety feature	What it prevents / supports
Drug‑drug & drug‑nutrient interaction checking	Prevents harmful interactions and flags food risks (e.g., grapefruit juice)
Allergy & duplicate‑therapy alerts	Avoids allergic reactions and overmedication
Dosing error & lab‑value checks	Stops unsafe dosing and drug‑lab conflicts
Medication reconciliation + reporting	Supports audits; target ~95% review compliance (MGMA)

Personalized medicine - powered by genomics, proteomics and modern AI - offers a practical route for Cambodia to target scarce resources where they matter most: AI models can sift genomic, proteomic and clinical data to identify biomarkers that predict who will respond to a therapy or who needs closer follow‑up, turning broad treatment protocols into smarter, localised care plans (see the Biomarker Research review on AI in drug discovery and early development).

Small, well‑scoped pilots - regional sequencing hubs that feed anonymized data into an explainable AI pipeline, or a hospital partnership that uses AI to repurpose existing drugs for locally prevalent conditions - are low‑cost ways to prove value, build governance and train clinicians (Nucamp's practical guide to AI pilots in Cambodia maps this approach).

The industry is already moving fast: AI platforms have shortened target‑to‑candidate timelines and enabled end‑to‑end design cycles in months in some cases, showing how generative and predictive tools can de‑risk discovery and speed translational impact for populations with high unmet need (overview in DrugPatentWatch).

Imagine a future where a genomic signal from a provincial clinic flags a precise treatment pathway within weeks, not years - an operational game changer for Khmer clinicians and patients alike.

Capability	Sample Cambodian pilot
Biomarker discovery (genomics/proteomics)	Oncology or severe infection cohort sequencing + AI stratification
AI‑assisted drug repurposing	In silico screening of approved drugs against local pathogen data
Centralized sequencing + analytics	Regional hub feeding provincial hospitals with AI‑summarised reports

“A recent study demonstrated that AI-discovered drugs in phase 1 clinical trials have a better success rate compared to traditionally discovered drugs, with estimates ranging from 80% to 90% for AI-developed drugs versus 40% to 65% for drugs discovered via traditional methods.” - Association of Cancer Care Centers

Real‑time triage and emergency prioritization are immediate, high‑value AI use cases for Cambodia's crowded emergency units: a recent systematic review shows that machine learning paired with natural language processing (NLP) generally outperforms models using structured data alone - NLP models averaged a ROC‑AUC ~0.91 versus ~0.88 for non‑NLP models - and found SpO2, systolic blood pressure, age and free‑text triage notes among the strongest predictors of acuity, mortality and ICU need (see the systematic review of machine learning and NLP in emergency department triage published in BMC Emergency Medicine: BMC Emergency Medicine systematic review of ML and NLP in ED triage).

That means a low oxygen saturation or a terse nurse note in a provincial Khmer clinic can be algorithmically amplified into an urgent worklist flag - if pilots are tied to clear human‑in‑the‑loop workflows, explainability, and local validation.

Practical deployments should borrow the nurse‑centred approach championed by the Emergency Nurses Association and KATE that preserves workflow while improving acuity assignment, and follow iterative evaluation like Penn LDI's LAVA work that showed large gains when richer clinical features are added (see ENA ESI triage guidance and KATE AI integration: ENA Emergency Severity Index (ESI) triage guidance and KATE resources, and Penn LDI LAVA study on improving emergency department triage accuracy: Penn LDI LAVA summary on improving ED triage assessments).

For Cambodia, start small - one hospital, ESI alignment, nurse training and a shadow‑mode run - to measure whether AI reduces variability, catches hidden high‑risk patients, and safely speeds priority transfer to definitive care.

Finding	Value / Detail	Source
ROC‑AUC (non‑NLP vs NLP)	~0.88 vs ~0.91 (average)	BMC Emergency Medicine systematic review of ML and NLP in ED triage
Top predictors retained	SpO2, triage notes, chief complaint, SBP, age, mode of arrival	BMC Emergency Medicine systematic review of ML and NLP in ED triage
ENA/KATE reported impact	2× improvement in triage acuity assignment for high‑risk patients (nurse‑centred AI)	ENA Emergency Severity Index (ESI) triage guidance and KATE resources

“There's been a call for more algorithmic approaches to emergency department triage but there are a lot of challenges to developing those algorithms.”

Patient engagement tools - AI chatbots, virtual assistants and post‑discharge callers - offer a low‑cost way to keep Cambodian patients connected after a clinic visit, especially where travel and clinician time are limited: platforms described by Capacity can handle appointment booking, symptom checks, medication reminders and insurance questions 24/7, while HIPAA‑focused vendors like Emitrr HIPAA-compliant chatbot guide show how secure, EHR‑connected bots can deliver post‑discharge instructions and follow‑ups without adding front‑desk workload.

Real‑world programs demonstrate tangible benefits - automated reminders can cut missed appointments by up to ~30% and even reduce unnecessary ER visits (~20% in a cited case), and hospital deployments of generative agents (UHS + Hippocratic AI) made thousands of follow‑up calls with an average patient rating of 9.0/10 and nurse alerts when a callback was needed.

For Cambodia that means designing Khmer‑language, low‑bandwidth flows, clear consent and a human‑in‑the‑loop for escalations; a friendly follow‑up call that reviews meds, checks wounds and prompts a same‑day clinic visit can be the difference between a smooth recovery and an avoidable readmission.

See Capacity's chatbot overview and UHS's post‑discharge program for operational examples.

Benefit	Reported impact / source
Reduced no‑shows	Up to ~30% fewer missed appointments (Riseapps / Capacity)
Fewer ER visits	~20% reduction reported in a hospital chatbot case (Helpsquad / Mayo Clinic example)
Patient satisfaction for GenAI follow‑ups	Average rating 9.0 / 10 (UHS + Hippocratic AI)

“Following an ER visit, I had a call from ‘Daisy.' … I found her voice to be friendly, welcoming and therapeutic.” - UHS patient on Hippocratic AI follow‑up calls

Assistive robotics in Cambodian care can start with practical, high‑impact devices that already have strong clinical evidence: the Stryker LUCAS 3 automated chest‑compression system delivers guidelines‑consistent compressions (5.3 cm depth at ~102/min), keeps compressions going during long transports or in the cath lab, and frees frontline staff to focus on airway, defibrillation and diagnosis - advantages that matter when ambulance rides and staff fatigue lengthen the time to definitive care.

With >50,000 devices deployed globally, documented >99% operational reliability, studies showing ~+60% increased cerebral blood flow versus manual CPR, and a median interruption of only 7 seconds when switching from manual to mechanical compressions, LUCAS is a compact, battery‑operated tool that supports safer patient transfer and cleaner post‑event quality‑improvement data via LIFENET connectivity and CODE‑STAT reports (Stryker LUCAS 3 automated chest-compression system product page).

For teams looking beyond resuscitation toward AI‑enabled surgical assistance and robotics, practical pilots and training pathways are outlined in Nucamp's guide to deploying AI in Cambodian healthcare (Nucamp AI Essentials for Work syllabus: Guide to deploying AI in Cambodian healthcare), so hospitals can pair proven devices like LUCAS with longer‑term investments in surgical‑assistance workflows and staff upskilling.

Specification / Outcome	Value
Compression depth	5.3 cm (guidelines‑consistent)
Compression rate	~102 compressions per minute
Battery life (typical)	~45 minutes (with multiple batteries/external power)
Device weight	17.7 lb (with battery, no straps)
Operational reliability	>99%
Physiologic impact	+60% increased blood flow to the brain vs manual CPR
Transition interruption (median)	~7 seconds from manual to mechanical

Getting started with AI in Cambodia means marrying ambition with discipline: begin with a single, well‑scoped pilot (one hospital, one workflow or modality) that measures clear ROI, keeps a human‑in‑the‑loop, and feeds what's learned back into local quality improvement structures like Cambodia's QICs so improvements stick; national conversations such as the CADT/KAS seminar underscore the need for ethical safeguards and cross‑sector partnerships (Seminar on AI in Public Health - CADT).

Remember that adoption is running ahead of governance - a recent HFMA report found widespread internal use of AI but only a small share of systems with mature governance - so pair every pilot with basic data stewardship, vendor accountability and a simple governance checklist (HFMA / Eliciting Insights: health system readiness for AI).

Finally, invest in practical skills for local teams - short, applied programs such as Nucamp's 15‑week AI Essentials for Work help clinicians and managers write effective prompts, run pilots responsibly, and translate early wins into scalable, trustable improvements (AI Essentials for Work syllabus (Nucamp)) - because the fastest path from pilot to better care is people who can safely operate the technology and insist on measurable results.

Action	Evidence / Detail
Start small pilots	One hospital / one workflow or modality - measure simple ROI
Strengthen governance	Only ~18% of health systems reported mature AI governance in HFMA survey
Build local capacity	Nucamp AI Essentials for Work - 15 weeks (syllabus: AI Essentials for Work syllabus (Nucamp))
Embed in QI systems	QICs in Cambodia improved facility performance by aligning with national quality monitoring

“Much like following accounting rules and regulations, healthcare executives understand that good governance around AI builds community trust and ensures responsible and ethical use of information.” - Todd Nelson, HFMA