Top 10 AI Prompts and Use Cases and in the Healthcare Industry in United Arab Emirates

The UAE is rapidly turning AI from promise into practice across healthcare - speeding diagnostics, enabling AI-powered telemedicine and using predictive analytics to manage beds and staff - with government strategy and private partnerships pushing bold pilots and real-world wins.

Sources from Feather's look at “AI in Healthcare” in the UAE and CIO Middle East's reporting on national AI investments show hospitals and platforms embracing tools that improve accuracy and patient experience, and Emirates Health Services' AI-powered website was singled out in the UN's “AI for Good: Innovate for Impact 2025.” For clinicians, administrators and healthcare entrepreneurs who need practical, job-ready AI skills, Nucamp's AI Essentials for Work bootcamp (15 weeks) focuses on prompt-writing, applied AI workflows and workplace use cases to help teams deploy compliant, effective solutions in UAE settings.

Learn more from Feather's overview, CIO's coverage, or Nucamp's course syllabus.

Program	Details
AI Essentials for Work	15 Weeks; Learn prompts, AI tools, and applied workplace skills; Early-bird $3,582, then $3,942; Syllabus: AI Essentials for Work syllabus (15-week bootcamp)

“Gen AI is one of those sets of tools and solutions that come together to deliver significant outcomes, particularly in enhancing the patient experience. The most obvious difference in healthcare is that anything technology does to enhance patient experience or touch a patient's life is far more impactful than in other industries. This is why technology like GenAI has four times the value in healthcare compared to other sectors. It's about saving lives, improving recovery, and making lives better, which makes it truly special.” - Veneeth Purushotaman, Group CIO (quoted in CIO Middle East)

Selection focused on practical, UAE‑relevant impact: use cases had to align with national AI priorities and demonstrable outcomes, show technical maturity or clear pilot results, and offer measurable operational or economic benefit for hospitals and public health programs.

To do that, projects were screened for alignment with the UAE's strategy and regional impact data (see PwC's analysis of national AI targets and sector potential), for documented pilots and clinical tools reported in Dubai and Abu Dhabi (examples and outcomes summarized in Appinventiv's roundup of Dubai healthcare pilots), and for market growth and scalability that justify investment and workforce training.

Methodology combined policy-fit, cited pilot evidence (Med42, AIRIS‑TB, Fakeeh, NABIDH and similar initiatives), and market signals so the Top‑10 list favors prompts and workflows already moving from lab to ward - the ones most likely to deliver faster diagnostics, smoother RPM, or predictable cost savings when piloted following a controlled rollout and monitoring plan.

Selection criterion	What we checked / source
Policy & strategic fit	UAE AI strategy and regional impact (PwC)
Documented pilots & clinical outcomes	Reported projects in Dubai/Abu Dhabi (Appinventiv)
Market growth & scalability	AI healthcare market trends and UAE CAGR (industry statistics)

“The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time‑honoured connection and trust.” - Eric Topol

AIRIS‑TB from M42 is a striking example of AI moving from pilot to practice in the UAE: validated in collaboration with Capital Health Screening Centre in Abu Dhabi and reviewed under Department of Health - Abu Dhabi oversight, the model analyzed over 1,000,000 real-world chest X‑rays and delivered an AUROC of 98.5% with a 0% false‑negative rate for TB‑specific cases - a headline‑grabbing result that suggests routine reads could be safely automated at scale and frees radiologists to focus on the most complex or urgent patients.

The study reports AIRIS‑TB could triage up to 80% of low‑risk CXR assessments in high‑throughput, low‑prevalence settings, while maintaining consistent performance across age, gender, HIV status and income levels; that combination of accuracy, equity and throughput is exactly the kind of tool UAE hospitals need to cut backlog, speed interventions and lower costs.

For context on AI CXR triage performance more broadly, see the M42 AIRIS‑TB clinical results and a recent meta‑analysis of AI for TB on chest X‑ray.

Metric	Result
AUROC	98.5%
False‑negative rate (TB cases)	0%
Scale	>1,000,000 CXRs reviewed
Automation potential	Up to 80% of routine CXR assessments
Generalizability	Strong across demographics; validated across six WHO regions

“The outcomes of this study reaffirm that AI models like AIRIS‑TB can not only match - but safely surpass - human-level accuracy and efficiency in clinical practice.” - Dr. Laila Abdel Wareth, CEO of CHSC

Med42 is a purpose-built clinical large language model from M42 that brings a 70‑billion‑parameter, instruction‑tuned architecture to UAE healthcare teams, promising much faster access to medical knowledge for tasks like medical question answering, record summarization and point‑of‑care decision support; the model's public profile notes strong benchmark performance (outperforming GPT‑3.5 in places and, for the Med42‑v2 suite, topping Clinical Elo ratings and achieving a MedQA zero‑shot score around 79.10), yet developers and reviewers stress it is not ready for unsupervised clinical use and requires extensive human evaluation to avoid hallucinations and bias.

For UAE hospitals and regulators the takeaway is pragmatic: Med42 can accelerate clinician workflows and patient‑facing copilots if deployed with clinician oversight, rigorous validation and vendor due‑diligence - see the Med42 model page for technical details and the Med42‑v2 summary for evaluation results, and consult local procurement guidance when piloting clinical AI.

Metric	Detail
Model	Med42 (clinical LLM)
Parameters	70B
Training data	250M tokens (Med42); ~1B tokens cited for Med42‑v2
Performance highlights	Strong on MedQA/MMLU clinical topics; Med42‑v2 top Clinical Elo; exceeds GPT‑3.5 and outperforms GPT‑4 on many MCQA tasks
Status & risks	Not ready for real clinical use; requires human evaluation; risk of incorrect or biased outputs
Intended uses	Medical QA, patient record summarization, diagnostic decision support (with oversight)

The Emirati Genome Program (EGP) is a national, M42‑backed effort to make genomics the backbone of precision medicine in the UAE - an ambitious plan to sequence one million Emiratis and already one of the largest long‑read cohorts in the world, with over 80,000 nanopore‑sequenced samples to date - positioning the country to tailor care based on population‑specific variants and to embed genetics into routine screening and drug selection.

Practical wins are already visible: integration with premarital and newborn genetic screening, a growing pharmacogenomics reporting system (PGx), and the creation of the Emirati Reference Genome platform and a completed Telomere‑to‑Telomere reference study that together enable faster, more accurate diagnosis and targeted therapies for Emirati patients; read the program overview on M42, the Nanopore report on long‑read scale‑up, or the Department of Health‑Abu Dhabi update for the latest milestones.

The result is a living national dataset that not only supports clinical decisions today but also accelerates R&D and workforce training so precision medicine can move from specialist labs into everyday Emirati healthcare.

Metric	Detail
Target cohort	1,000,000 Emiratis (national goal)
Samples collected	More than 700,000 genetic samples
Long‑read sequencing	>80,000 nanopore‑sequenced samples (largest long‑read cohort)
Pharmacogenomics (PGx)	~160,000 reports available
Newborn screening	Tests 733 genes for >800 conditions
Workforce upskilling	100+ Emirati physicians trained in genomic medicine
Reference genome	Telomere‑to‑Telomere Emirati Reference Genome completed

“This collaboration reflects Abu Dhabi's determination to pioneer real‑world applications of advanced science. Partnering with UCSF and IGI, one of the world's most respected institutions in gene therapy, would accelerate our ability to integrate genome‑guided care into our healthcare system, creating an unprecedented opportunity to correct genetic conditions early in life, prevent chronic disease progression and reduce long‑term healthcare costs”. - H.E. Dr. Noura Khamis Al Ghaithi

Fakeeh University Hospital (FUH) in Dubai illustrates how a highly digitalized, integrated care model and targeted automation can create the technical foundation for AI‑enabled telemedicine and remote patient monitoring (RPM): FUH's rollout of automated dispensing cabinets (ADCs), a Central Pharmacy Manager (CPM) and Omnicell inventory optimisation cut medication errors and slashed medication‑preparation time, while its broader “digitization of healthcare” agenda builds the workflows and data streams telemedicine platforms and RPM devices depend on - see the FUH digitization overview and the detailed pharmacy automation case study for operational context.

Those same building blocks - reliable device integration, item‑level inventory data and faster point‑of‑care workflows - are exactly what RPM programs need to detect complications early, route alerts to clinicians and keep nurses focused at the bedside; for practical RPM examples and outcomes, review the remote patient monitoring case studies that show how RPM can detect post‑surgical problems and support complex pediatric cardiology.

The practical takeaway for UAE hospitals: invest first in interoperable digital infrastructure and pharmacy automation to unlock safer, scalable AI telehealth and RPM pilots that measurably reduce risk and free clinicians to spend more time with patients.

Metric / Feature	Result / Note
Facility	350‑bed tertiary care (Dubai)
Automation implemented	ADCs, CPM, Omnicell inventory optimisation
Medication error / near miss rate	Reduced to 0%
Medication preparation time	72% reduction (with 60% order volume increase)
Staff satisfaction	~70% reported improvement

“Our analysis shows a 72% reduction in medication preparation time, even with a 60% increase in order volume, underscoring its role in streamlining workflows and reducing human error.” - Dr. Hossam Hosni, Pharmacy Informatics and Automation Specialist

Insilico Medicine showcases how generative AI can shrink the discovery timeline and offer UAE life‑science hubs a ready model for faster, smarter drug R&D: its Pharma.AI suite (PandaOmics for target discovery and Chemistry42 for generative chemistry) has produced candidates in as little as 18 months from target to preclinical nomination and pushed a lead fibrosis compound into Phase II, demonstrating the kind of end‑to‑end acceleration that can help Emirati research centres and clinical trial sponsors shorten timelines and lower early‑stage costs; read the deep‑dive on how Chemistry42 and PandaOmics power each preclinical step in NVIDIA's report and the Rentosertib USAN milestone that marks an AI‑designed compound moving toward patients.

The practical upshot for UAE healthcare: pairing national genomics datasets and trial-ready sites with AI platforms could convert local disease insights into licensed candidates far faster than legacy workflows - a memorable metric to keep in mind is that Insilico reduced model deployment cycles from months to weeks and produced testable molecules in weeks, not years, altering the pace at which hypotheses become therapies.

Metric	Detail / Source
Platform	Pharma.AI (PandaOmics, Chemistry42) - NVIDIA blog
Time to preclinical candidate	~18 months (target→preclinical nomination)
Time to Phase II	~30 months for lead fibrosis program (Tech Review / NVIDIA)
Pipeline	31 programmes for 29 targets; multiple clinical candidates (WEF)
Model training / deployment speed-up	>16x faster iteration; deploy time from 50 to 3 days (AWS case study)
Regulatory milestone	Rentosertib received official USAN name (News‑Medical)

“This first drug candidate that's going to Phase 2 is a true highlight of our end‑to‑end approach to bridge biology and chemistry with deep learning.” - Alex Zhavoronkov, Founder and CEO (reported in NVIDIA)

MoHAP has paired a long‑standing AI vision with nation‑wide screening muscle to make chronic‑disease prevention tangible: the AI‑powered Enayati platform (wearables, sensors and continuous monitoring) provides real‑time alerts and risk prediction for chronic and genetic conditions, while the National Prediabetes and Diabetes Screening campaign combines digital risk questionnaires and HbA1c testing with immediate referrals, lifestyle coaching and structured 3‑ and 6‑month follow‑ups to close the loop on care; that practical blend of prediction, on‑the‑spot testing and phone‑based support (hotline 800‑DIABEATS) helped MoHAP exceed its targets - surpassing 150,000 screenings in one year and delivering more than 12,000 tests in the 100‑day challenge - showing how AI plus simple, scaled workflows can turn data into earlier intervention and measurable public‑health wins (the campaign even lit the Dubai Frame in blue and marked the milestone with a Skydive Dubai event).

Read MoHAP's Enayati launch and the campaign outcomes for operational details and partnership notes.

Metric	Result / Note
Enayati platform	MoHAP Enayati AI-powered health monitoring platform
National screenings (year 1)	>150,000 completed
100‑day challenge target / result	5,000 target → >12,000 tests completed
Follow‑up cadence	3 months, 6 months (with remote counselling)
Public‑private partners	Merck Gulf, BinSina, ADPHC, Dubai health bodies, Emirates Health Services

“This milestone of conducting over 150,000 early detection screenings is a clear testament to the successful strategic partnership between the public and private sectors, as well as the unwavering support of the UAE's leadership.” - H.E. Dr. Hussein Al Rand, Assistant Undersecretary for Public Health Sector (MoHAP)

Robotic companions and virtual caregivers are already moving from concept to clinic in the UAE, blending human-centred hospitality with clinical utility: King's College Hospital Dubai's King's Bond greets visitors at reception, speaks Arabic, English, Chinese, Russian and Urdu, recognises faces after the first encounter and helps with directions and appointment bookings - a friendly, multilingual bridge for older patients who may be anxious or hard of hearing - while the ROSA system is being used for precision, minimally invasive orthopaedic surgery that shortens recovery and reduces pain.

These paired examples - a social robot that personalises the hospital journey and a surgical robot that improves outcomes - illustrate practical pathways for elderly care where companionship, medication or rehab reminders and gentler procedures work together to keep seniors safer and more independent.

For readers mapping pilots to procurement or service design, the King's Bond companion robot and ROSA robotic surgical assistant are concrete, local examples to study and adapt.

“With robotic-assisted knee replacement, we can ensure perfect positioning of the implants right from the first attempt. This approach significantly enhances the patient's and surgeon's experience by making the procedure smooth, efficient, and less invasive.” - Dr. Farid Ghasemzadeh Mojaveri, Consultant Orthopaedic Surgeon

In Dubai's shift from siloed clinics to a proactive public‑health engine, NABIDH is the spine: the DHA‑backed Health Information Exchange links roughly 1,500 facilities and 11,000 doctors into a unified patient timeline that serves about 3.3 million people, turning individual medical records into actionable population health intelligence.

By making lab results, prescriptions and imaging available in near‑real time, NABIDH reduces duplicate tests, speeds referrals and feeds predictive analytics and disease‑surveillance dashboards that help hospitals anticipate bed demand or spot emerging clusters - and, crucially, the platform now supports AI tools to monitor data security and flag anomalies as of April 2025.

For UAE providers planning pilots, NABIDH integration is the practical first step toward AI‑driven RPM, telehealth workflows and evidence‑based resource allocation; for implementation details and integration guides, see the NABIDH connected platform overview and Healthcluster's note on the role of NABIDH in UAE healthcare.

Metric	Detail
Connected facilities	~1,500 (public & private, Dubai)
Participating clinicians	~11,000 doctors
Population coverage	~3.3 million people
AI / security monitoring	AI tools integrated to monitor data security (Apr 2025)
Primary benefits	Interoperability, predictive analytics, reduced duplicate testing, telehealth enablement

DiabeticU-style programs that pair point-of-care devices, continuous glucose monitors and consumer wearables can turn episodic clinic visits into an ongoing, data-driven care pathway for people with diabetes across the UAE - feeding heart rate, glucose and activity trends into EHRs for smarter remote patient monitoring and personalized coaching.

Practical integration needs include FHIR/HL7 mapping, real-time and offline sync, and middleware to normalize device data so clinicians see a single, actionable timeline rather than siloed app dashboards; a good primer on those steps is DreamSoft4U's integration guide for wearable devices and EHR (DreamSoft4U integration guide for wearable devices with EHR).

Clinically validated devices such as CGMs and ECG-capable smartwatches can enable timely alerts and trend-based interventions (reducing readmissions and improving adherence), as outlined in EHR In Practice's review of wearable integration with electronic health records (EHR In Practice review: wearables and EHR integration potential), while emerging AI research documents privacy-preserving, federated and noise‑filtering approaches that improve signal quality and trustworthiness for population-scale pilots in Abu Dhabi and Dubai (journal article on AI-enabled wearables research and privacy-preserving techniques).

The “so what”: when device data streams into a unified clinical workflow, a single anomalous glucose trace can trigger verification, coaching and medication reviews instead of an avoidable ER visit, saving time and cost while keeping patients safer.

Smart hospital operations in Dubai are moving beyond pilots to practical, repeatable workflows: Dubai Healthcare City is already using AI to streamline back‑office functions like medical‑claims processing while frontline teams deploy predictive bed‑management and location technologies to keep patients moving through care more smoothly (see Appinventiv's roundup on AI in Dubai healthcare).

Proven techniques - real‑time bed management algorithms and AI‑driven capacity forecasting - cut emergency waiting and enable faster admissions by automating bed assignment and discharge coordination (peer‑reviewed work on automated bed management shows measurable downstream impacts).

When combined with RTLS, IoT and geofencing for asset and staff tracking, hospitals can reduce time spent searching for equipment, speed room turnover, and trigger housekeeping and supply requests automatically, turning manual coordination into invisible orchestration that frees clinicians for care (Mapsted's guide explains these location‑technology benefits).

The “so what” is simple: smarter operations translate into fewer delays, lower operational waste and a patient experience where administrative friction disappears - making AI‑enabled workflows a practical priority for Dubai Healthcare City and for tertiary centres across the UAE such as Rashid Hospital.

Practical next steps for UAE healthcare teams are clear: focus on tight, measurable pilots that map to national goals, build on the country's shared data rails, and lock in governance and workforce readiness before scaling.

Start small by selecting high‑value use cases (faster imaging reads or RPM alerts) and run controlled pilots with clinician oversight and KPI tracking (time‑to‑diagnosis, avoidable ED revisits) as Appinventiv's implementation roadmap recommends - that keeps projects pragmatic and accountable (Appinventiv guide to AI in healthcare in Dubai).

Next, anchor models to the UAE's interoperability backbone (NABIDH / Malaffi / Riayati) so tools learn from representative, federated data and can move from testbed to hospital‑wide use without sideways surprises, a core point in the national strategy briefing (Inside the UAE's AI Healthcare Strategy briefing).

Finally, treat governance and people as non‑negotiable: adhere to DOH/MoHAP guidance, privacy laws and ISO‑aligned AI management practices while upskilling clinicians and operations staff - practical training (for example, Nucamp's 15‑week AI Essentials for Work) closes the skills gap and helps teams write safe, effective prompts for day‑to‑day clinical workflows (Nucamp AI Essentials for Work syllabus (15‑week bootcamp)).

Priority	Action	Why it matters
Use‑case pilots	Run clinician‑led pilots with clear KPIs	Keeps deployment safe, measurable and fundable
Data & integration	Connect to NABIDH/Malaffi/Riayati and use federated datasets	Ensures generalisability and scale
Governance & workforce	Follow DOH/MoHAP rules, ISO practices and train staff	Reduces risk, builds trust and operational readiness