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

Lebanon stands at a practical inflection point: home‑grown research and reviews show AI already transforming diagnosis, lab analysis and radiology triage while promising sharper, more personalized care and lower downstream costs - especially when diagnostic imaging and telehealth bridge gaps for rural patients and overstretched urban providers.

Local authors highlight how AI can reduce waste in the health bill and speed discovery, but also flag training, data quality, privacy and fairness as immediate hurdles; a recent scoping review sums the most used methods, benefits and barriers for Lebanese health technologies.

For hospitals and clinics thinking strategically, starting with imaging and bilingual triage tools offers fast wins while investing in staff upskilling and governance to ensure trustworthy, equitable rollouts (see the role of AI in healthcare and key Lebanese trends linked below).

Bootcamp	Length	Early bird cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15-week bootcamp)

Selection balanced practical impact in Lebanon with technical readiness: priority went to prompts and use cases that reduce frontline burden in low‑resource settings (as seen in the Rhazes AI pilot at Al Hamshari Hospital), scale with governance and human‑in‑the‑loop evaluation, and are teachable to local clinicians and students.

Criteria included measurable patient‑level benefit (faster throughput, fewer missed diagnoses), clear admin wins (documentation, coding and triage), feasibility where infrastructure is limited, and strong evaluation paths to move pilots into production - drawing on lessons from the Al Hamshari trial and enterprise practices for scaling agents.

Prompt design followed healthcare best practices (clarity, scope, safety checks) and paired technical tests with clinician review so outputs can be audited and improved.

Medical education research and local training needs also guided choices: use cases that create quick, demonstrable time savings and that can be supervised by upskilled staff were ranked higher.

These pragmatic, evidence‑driven filters - impact in low‑resource clinics, governance for agents, measurable metrics, and prompt safety - shaped the top 10 list for Lebanon's hospitals and clinics (see pilot details and scaling guidance linked below).

Evaluation Metric	Why it matters
Number of patients seen	Measures throughput gains from AI-assisted workflows (Al Hamshari pilot)
Patient outcomes (death vs admissions)	Clinical safety and effectiveness
Record keeping quality	Enables monitoring, research and billing automation
Staff and patient satisfaction	Acceptability and sustainability of deployment

“For every hour a doctor spends with a patient, they spend two hours doing paperwork.” - Zaid Al‑Fagih, Rhazes AI (reporting on the Al Hamshari Hospital pilot)

For Lebanese hospitals and imaging centers, AI is already a practical lever to speed triage, surface subtle findings and reduce costly downstream care: cloud and on‑prem tools can flag urgent chest X‑rays, speed mammography reads, and serve as a dependable “second read” for breast screening (see SecondReadAI for mammography support).

Algorithms trained on millions of images enable instant, user‑friendly interpretation of X‑rays, CT and MRI that clinicians and even patients can act on quickly - CT Read emphasizes clear, plain‑language reports and near‑instant analysis that help demystify scans for non‑specialists.

Vendors and reviews also report real efficiency gains (examples include cutting chest X‑ray turnaround from 11.2 to 2.7 days and algorithm performance as high as ~94.4% for lung nodules and ~89.6% for breast cancer in large datasets), benefits that matter in Lebanon where radiology capacity can bottleneck care.

Start with high‑impact, well‑validated prompts - triage chest X‑rays, automate mammography detection, and route urgent cases - to free radiologists for complex reads while retaining human oversight and auditability (see practical Lebanese use cases and cost benefits linked below).

Precision treatment planning is already within reach for Lebanese patients thanks to AUBMC's in‑house Genomic Profiling Program, which runs comprehensive NGS assays (ROCHE AVENIO/FOUNDATIONONE) locally and trims report turnaround to 5–7 days instead of the six‑week waits that come with overseas testing - an operational detail that can change treatment timelines for advanced cancers.

Genomic profiling at AUBMC combines tumor sequencing, predictive genetic testing and inherited‑risk counseling (including carrier screening for hemoglobinopathies), enabling oncologists to match targeted drugs, spot hereditary risks, and avoid repeated biopsies when tissue is scarce.

Globally, AI and machine‑learning methods are proving their value by sifting large genomic datasets to surface mutation‑treatment patterns and survival signals - work like the USC study that identified hundreds of mutation‑outcome links shows how computational tools can sharpen treatment choice.

For Lebanon, practical next steps are integrating molecular tumor boards to interpret reports, building clinician workflows that pair NGS results with human oversight, and exploring advanced options such as neoantigen prediction and vaccine design through services like CancerNeo® to expand immunotherapy strategies; each step leans on fast local testing, trained genetics counselors, and governance to translate molecular data into safer, more precise care.

“By understanding how different mutations influence treatment response, doctors can select the most effective therapies.” - Ruishan Liu

Predictive analytics can shift Lebanese care from reactive to preventive by turning routine clinic data into population risk maps: a recent single‑score effort validated across cohorts shows final risk ranges such as −34 to 72 for dementia and −42 to 45 for diabetes, offering a compact way to flag ageing‑related risk (see the BMC Medicine single‑risk assessment).

For chronic kidney disease, a systematic review identified dozens of usable models (36 for healthy adults, 12 for people with type 2 diabetes) with AUCs ranging roughly 0.63–0.91 and several scores that perform above 0.8 - one sex‑specific CKD model even reached AUC ≈0.95 - signals that primary‑care screens can be both simple and discriminating (see the CDC review).

Clinically, diabetes is a double threat: large cohort work links type 2 diabetes to a 53–73% higher dementia risk, and meta‑analyses tie severe hypoglycemia to elevated dementia rates, underscoring why stratifying patients by glycemic control and comorbidity matters for prevention.

In Lebanon, repurposing validated scores into bilingual EHR prompts and targeted screening campaigns - starting with people who have diabetes, hypertension or CKD risk factors - offers rapid, measurable prevention wins while local validation studies confirm calibration to the population.

Metric	Reported range (source)
Dementia single‑score range	−34 to 72 (BMC Medicine single‑risk dementia assessment study)
CKD model AUCs	≈0.63–0.91; several >0.80; sex‑specific model up to ~0.95 (CDC systematic review of chronic kidney disease prediction models)
Type 2 diabetes → dementia	~53–73% higher risk (large UK cohort)

Fig chatbot can act as a 24/7 bilingual digital health assistant for Lebanon by combining symptom triage, Arabic language support and appointment booking so patients get the right next step fast - self‑care advice, a clinic visit, or urgent referral - without tying up clinicians.

Emerging work shows chatbots can perform symptom triage and multilingual conversation, guiding users through structured interviews to suggest the most suitable action, and companies like Infermedica now offer Arabic symptom navigation used by millions to plug access gaps and lower costs (Infermedica Arabic symptom triage and AI-driven patient navigation).

Practical templates already exist for Arabic workflows (WhatsApp‑ready booking, reminders and lead capture) that run 24x7 and reduce front‑desk load - one Tars deployment reported saving 4,000+ calls per month - so a Lebanon‑focused Fig chatbot can free staff for complex care, improve rural access, and nudge patients toward timely, cost‑effective options (Arabic healthcare chatbot templates with WhatsApp integration).

For hospitals and insurers building trustworthy bots, pairing clear triage prompts with clinician review, escalation rules and bilingual UX creates a practical tool that patients actually use and that measurably lightens administrative burden (Chatbot triage and multilingual support in healthcare overview).

“The broader goal is to improve customers' performance, increase healthcare accessibility, and reduce costs.” - Piotr Orzechowski, Infermedica

Cloudfish social‑listening for Lebanon means turning Arabic social feeds into an early‑warning layer that complements clinical surveillance: WHO's Eastern Mediterranean review found that 95.8% of COVID‑19 signals (9,732 of 10,160 items) were captured via social media, and nearly all event updates from those feeds were official Ministry of Health posts, underscoring how platforms became a real‑time channel for public‑health intelligence (WHO EMRO social‑media epidemic intelligence (BMJ Global Health study)).

NLP research shows Arabic infodemiology can work if tools handle dialect and slang - one JMIR Med Inform study achieved F1 scores up to ~94% for COVID‑19 tweet classification and used informal terms to boost accuracy while recovering user locations with ~63.6% accuracy for COVID‑19 cases (Arabic COVID‑19 tweet classification (JMIR Medical Informatics study)).

Large‑scale topic and sentiment work on >1,000,000 Arabic tweets (processed to ~637,718) found anger to be the dominant public emotion, which is exactly the kind of signal public‑health communicators need to spot and address quickly (Arabic tweet topic and sentiment analysis (JMIR Infodemiology study, 2025)).

Study / Source	Key metric	Value
WHO EMRO (BMJ Global Health)	COVID‑19 signals from social media	95.8% (9,732 of 10,160)
Alsudias & Rayson (JMIR Med Inform)	Tweet classification F1 / COVID geolocation accuracy	F1 up to ~94%; geolocation ≈63.6% (COVID‑19)
Alshanik et al. (JMIR Infodemiology 2025)	Arabic tweets analyzed / dominant emotion	>1,000,000 collected; ~637,718 processed; anger dominant

EHR documentation automation - ambient audio capture, speech‑to‑text and AI scribe workflows that turn conversation or shorthand into polished SOAP notes - is a practical way for Lebanese clinics to reclaim clinician time, reduce backlog and make telehealth visits charting‑ready in minutes; vendors advertise results ranging from automated notes in under a minute to charting time cut by as much as 70%, with some users reporting up to two hours saved per day or 7+ hours a week.

These tools support multiple inputs (live dictation, telehealth recordings, uploaded audio) and offer EHR APIs and FHIR/HL7-friendly integrations so generated notes can flow into existing systems, while commercial platforms emphasize compliance and BAAs for secure handling of PHI. For overstretched hospitals and private practices in Lebanon, pragmatic pilots - start with high‑volume ambulatory clinics, pair the scribe with rapid clinician review, and measure throughput and documentation quality - can free clinicians for complex care and speed coding and billing workflows; vendors such as SOAPNoteAI clinical documentation platform and implementation guides from John Snow Labs guide to generating SOAP notes with AI show common deployment patterns and tech stacks to consider.

Metric	Reported value (source)
Charting time reduction	Up to ~70% (ScribeHealth.ai)
Provider time saved	Up to 2 hours/day reported by providers (Sunoh)
Auto‑note generation & usage	SOAPNoteAI: 25,840 notes generated; 4,240 hours saved (SOAPNoteAI)
Adoption example	AutoNotes trusted by 65,000+ clinicians (AutoNotes)

“I LOVE SOAP Note AI! It gives me back the time that charting was taking away from my patients at work and my family at home.” - Allison Carroll (SOAPNoteAI testimonial)

Remote patient monitoring (RPM) offers a practical way to extend care beyond clinic walls in Lebanon - especially for rural patients and overstretched urban providers - by using wireless wearable sensors for continuous observation and agent‑driven alerts that surface early concerns between visits.

A qualitative overview of nurses' perspectives on continuous monitoring highlights that implementation hinges on real workflows and frontline buy‑in, making human factors as important as the devices themselves (BMC Nursing study on wireless wearable sensors).

For Lebanese health systems, pairing these monitoring tools with clear escalation rules, bilingual patient touchpoints and reliable escalation channels can turn raw signals into timely clinical action; practical guidance on deploying telehealth and remote monitoring at scale in Lebanon helps translate that potential into measurable access and efficiency gains (Telehealth and remote monitoring in Lebanon).

The memorable test: a continuous sensor that “keeps watch” between visits can be the difference between an early outpatient adjustment and an avoidable emergency admission, but only when nurses, clinicians and alerting agents are all part of the design.

Study	Metric	Value
Remote continuous monitoring (BMC Nursing)	Accesses	5,423
Remote continuous monitoring (BMC Nursing)	Citations	17
Remote continuous monitoring (BMC Nursing)	Altmetric	2

Administrative automation - covering scheduling, billing and claims review - can unlock immediate gains for Lebanon's clinics and insurers by removing manual chokepoints at registration, speeding reimbursements and cutting denial rework: studies show denial rates are rising (38% of organizations report >10% claims denied) and nearly half of denials stem from missing or inaccurate data, so front‑end validation and automated eligibility checks pay for themselves fast.

Practical deployments combine smart scheduling and bilingual patient intake with an automated claims engine that flags at‑risk submissions, runs rule‑based edits and triages denials for high‑value appeals; predictive triage and workflow automation have been shown to reduce rework and accelerate payment cycles in provider pilots.

Start small - automate registration and prior‑auth checks, measure clean‑claim rates, then expand to denial‑triage and automated follow‑ups - while following proven rollout steps (build a sound AI business case, manage expectations, and phase deployments) to ensure sustainable results.

For implementation guidance and industry best practices see the Experian claims modernization overview and Expert.ai's five best practices for automating claims.

Metric	Reported value (source)
Providers reporting >10% claims denied	38% (Experian Health)
Common cause: missing or inaccurate data	46% (Experian Health)
Denials reviewed manually	48% (Experian Health)
Providers planning near‑term investment in claims tech	45% planning investment (Experian Health)

“You know when the Patient Access Curator went live because you can see it in our stock price. It helped us drive a $100 million bottom-line improvement within two quarters.” - Ken Kubisty (Experian case study)

Clinical trial matching and registry automation depend on privacy-first record linkage: studies show deterministic, privacy‑preserving record linkage (PPRL) using homomorphic encryption can match cohort and administrative data at very high accuracy without sharing raw identifiers - one large validation linked 8,117 of 8,128 records (99.9% success) and reached 99.8% indirect‑identifier validity after auditing, a process that involved 26 billion encrypted comparisons and took 36 days in early implementation (IJPDS validation of deterministic privacy‑preserving record linkage (PPRL)).

Federal projects and guidance are explicitly evaluating PPRL to expand hospital and Medicaid research linkages while protecting privacy (CDC/ASPE evaluation of privacy‑preserving record linkage methodology for national hospital data linkage), and de‑identification best practices remain essential for sharing trial data responsibly (BMC Medical Research Methodology guidance on patient‑level data sharing and de‑identification).

For Lebanon, the practical takeaway is clear: registry‑embedded trials and automated matching can be enabled with PPRL, but require governance for a neutral third‑party decryptor, substantial compute capacity, and careful de‑identification workflows so trial matching and national registries deliver research value without compromising patient privacy.

Metric	Value / Source
Successful deterministic matches	99.9% (IJPDS validation)
Indirect‑identifier validity after audit	99.8% (IJPDS validation)
Comparison workload	~26 billion encrypted comparisons (IJPDS validation)
Processing time (pilot)	36 days with manual steps (IJPDS validation)

NAR‑style drone logistics and remote facility inspection offer a pragmatic, fast‑win layer for health systems that need to move small, time‑sensitive items across difficult terrain: African pilots show drones can cut delivery times from hours to minutes (one Zipline hub slashed some hospital deliveries from two hours to ~15 minutes and today serves roughly 75% of a nation's outside‑capital blood supply), while trials in the US and UK demonstrate long‑range BVLOS potential and strict regulatory work‑flows to enable safe flights.

Practical lessons for Lebanon's health planners include matching realistic payloads (most medical drones carry ~1.8–4 lb / ~1.75 kg payloads with ranges up to ~60–96 miles), protecting cold‑chain integrity for blood and vaccines, and building public‑private partnerships and corridor approvals before scaling.

Start with single‑use pilots (emergency blood, lab sample shuttles, or on‑demand supply drops to hard‑to‑reach clinics), measure turnaround and stockout reductions, and design neutral governance so data, safety and community trust grow together - approaches already documented in Rwanda, Malawi and recent US BVLOS trials (see pilot insights and case studies linked below).

Metric	Reported value / source
Typical drone payload	~1.8–4 lb (~1.75 kg) (Zipline / case studies) - Think Global Health article on drones delivering humanitarian aid in Africa
Typical range	~60–96 km (case reports)
Example delivery time cut	From ~2 hours to ~15 minutes (Rwinkwavu hospital, Zipline) - Reach Alliance case study on Zipline's impact in Rwanda
Demonstrated BVLOS trial	80‑mile delivery in North Dakota (Project Rural Reach) - UND Center for Innovation landmark medical drone delivery trial (North Dakota)

“There is a shift of trust in the health system.” - Olivier Defawe (commenting on how reliable drone delivery changes clinic behavior)

Practical next steps for Lebanese health leaders are clear: pick a high‑impact pilot (imaging triage, bilingual triage bots or EHR scribing), pair it with strong governance and clinician‑in‑the‑loop review, and use national surveillance pilots to scale - projects like the AI4PEP Lebanon platform show how lab feeds, FHIR/HL7 integration and expert‑validated alerts can lift pandemic surveillance from paper to a real‑time dashboard (AI4PEP Lebanon pandemic surveillance platform).

Start where capacity bottlenecks hurt most - Al Hamshari Hospital's Rhazes AI pilot, for example, supports clinicians in an 80‑bed facility that treats roughly 4,000 refugees each month and demonstrates how automation can free time for care (ComputerWeekly coverage of the Al Hamshari Rhazes AI pilot).

Pair technical pilots with workforce training - short, practical courses in prompt design and AI operations such as the AI Essentials for Work bootcamp help clinicians and admins become effective AI supervisors - and measure everything (throughput, outcomes, documentation quality) so small wins justify broader rollout (AI Essentials for Work bootcamp registration).

Bootcamp	Length	Early bird cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work bootcamp

“For every hour a doctor spends with a patient, they spend two hours doing paperwork.” - Zaid Al‑Fagih, Rhazes AI