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

Sweden's healthcare system is at a tipping point where smarter data use can ease a looming strain: AI Sweden's national mapping found 179 AI initiatives - mostly in diagnostics and administration - because ageing demographics (people 80+ are set to jump nearly 50% by 2031) and rising chronic disease are driving demand while staffing needs may grow by ~85,000; clinicians also lose up to two working days a week to paperwork.

That's why tools like the Vårdkartan and Swedish–Canadian collaborations with Unity Health Toronto aim to scale reliable solutions, even as leaders warn implementation is hard and needs clear strategies (see the qualitative study on implementation challenges in Swedish healthcare).

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Methodology: selection began by harvesting real pain points from frontline teams and a wide scan of potential applications (including a broad inventory of 150 use cases) and then applying a pragmatic funnel borrowed from industry guidance: first screen for whether a problem is truly AI-relevant, then score by measurable value, speed of delivery, and acceptable risk/cost; finally, pilot the highest‑scoring candidates and vet clinical rigor with an established 30‑item evaluation checklist.

This approach blends the use‑case breadth in Travis May's inventory with the practical filters in phData's framework (150 AI use cases in healthcare - Travis May; phData: How to pick the right use case for AI) and ensures methodological transparency using the clinician checklist from JMAI (JMAI 30‑item clinical AI evaluation checklist).

The guiding principle: prioritize short‑cycle wins that protect clinician time (for example, reclaiming paperwork hours) while building evidence before wider roll‑out.

Filter	Why it matters
Value	Clear, measurable impact on outcomes or costs
Speed of delivery	Short time‑to‑pilot to demonstrate ROI
Risk	Clinical safety and tolerable failure modes
Cost	Feasible build and maintenance effort

AI can solve incredible problems but also fails at trivial tasks.

Sweden's leading research shows a practical path to smarter, more targeted breast screening: the Karolinska-led ScreenTrustMRI trial found that an AI‑based score - flagging roughly the top 6.9% of women - and offering them additional MRI uncovered 36 cancers among 559 women who had previously been cleared by mammography, making supplemental MRI economically feasible by focusing on just 7–8% of participants; the study also reported the AI approach was about four times more effective than a prior density‑based strategy at finding invasive and multifocal cancers (Karolinska ScreenTrustMRI study: AI‑MRI integration improves early detection).

Complementary work from Karolinska and collaborators, covered by RSNA, shows deep neural networks can beat standard mammographic density models for short‑term risk prediction, lowering false negatives and better spotting women at risk of aggressive disease - a capability that matters for Sweden's move toward risk‑adaptive, individually tailored screening intervals (RSNA article: deep learning improves breast cancer risk prediction).

The takeaway for Swedish services: modestly sized, ethically governed pilots that add AI risk scores to existing workflows can reveal cancers that routine mammography misses, potentially saving lives without screening everyone more intensively.

“Now that the study is complete, we must pause the method because it needs approval from the European Medicines Agency for routine use. Additionally, the software needs to be packaged and quality assured to become a product, and for this, we have received continued funding from the Wallenberg Foundations,” – Fredrik Strand.

AI‑assisted medical history‑taking and triage are no longer hypothetical in Sweden - they're being rolled into everyday primary care with promising gains and practical headaches, according to an interview study in BMC Primary Care interview study on AI-assisted triage in Sweden.

Leaders from 13 primary‑care operations (and four regions piloting the system) reported that AI chat triage can give clinicians a concise report with images and suggested diagnoses so staff

“can better and faster prepare”

for visits, yet the hoped‑for efficiency often stalled when tools couldn't access electronic health records and clinicians were forced to cut‑and‑paste between systems, turning a time‑saver into extra work; other barriers included low digital maturity among some staff and patients, AI limits in handling comorbidities, and concerns about preserving the clinical encounter.

Crucially, managers emphasised that the technology itself wasn't the main hurdle - implementation was: success meant involving staff early, building superuser networks, adapting rollouts to digital readiness, and accepting iterative improvement rather than waiting for a

“perfect” product

Read the full BMC Primary Care interview study on AI-assisted triage in Sweden for implementation details and the Halmstad University article on AI in Swedish primary care pilots.

Study detail	Value
Publication	BMC Primary Care (24 July 2024)
Participants interviewed	13 operational managers / senior leaders
Regions using system at interview time	4 Swedish regions
Top barriers	Interoperability, staff digital literacy, duplication of work
Top facilitators	End‑user involvement, superuser networks, staged rollouts

AI-driven oncology tools are starting to move from promise to practice in ways that matter for Swedish cancer services: a recent review in Molecular Cancer outlines how algorithms can stratify patients into prognostic subgroups and help monitor progression (Molecular Cancer review: current AI technologies in cancer diagnostics and treatment), while work reported by the Harvard Gazette shows a versatile model (CHIEF) that reads whole‑slide histopathology, predicts molecular features and survival, and even produces heat maps where richer immune cell presence in tumour‑adjacent tissue links to longer outcomes - a vivid marker clinicians can use when prioritising follow‑up imaging or trials (Harvard Gazette: CHIEF AI model reads whole‑slide histopathology to predict patient survival).

Practical oncology toolkits - from pathology assistants like PathAI to genomic‑driven services such as Tempus - promise faster, more personalised plans, but experts warn of accuracy, workflow, and equity gaps that demand prospective validation, continuous monitoring, and federated or privacy‑preserving data strategies before national scale‑up in Sweden (Cancer Therapy Advisor: barriers to wider use of AI in oncology); the short‑term win is staged, multimodal pilots that link AI recommendations to clinician review and real‑time performance checks.

Article	Detail
Current AI technologies in cancer diagnostics and treatment	Published 02 June 2025 - 10k accesses, 9 citations, 7 Altmetric

“Our ambition was to create a nimble, versatile ChatGPT-like AI platform that can perform a broad range of cancer evaluation tasks.”

Radiology triage and critical‑finding flagging are among the most practical AI moves for Swedish hospitals that need faster ED decisions and leaner imaging workflows: AI algorithms can scan incoming CTs and X‑rays in real time, bump suspected emergencies to the top of the worklist, and activate care teams so clinicians see the most urgent cases first - reducing dangerous delays and easing radiologist overload.

Vendor platforms that integrate with PACS/RIS and the EHR can automate prioritization and follow‑up while handling routine QA and report drafting, but success hinges on tight integration and local workflow rules rather than the algorithm alone (see Aidoc's approach to embedding AI into radiology workflows and care coordination).

Real‑world deployments show meaningful gains: a Change Healthcare/Aidoc rollout cut ED turnaround times by ~27% and made AI‑positive studies read faster (about 15 vs 18 minutes), with the AI acting as a safety net by flagging dozens of acute intracranial hemorrhages in a month; those kinds of performance improvements translate in practice to quicker treatment decisions and fewer unnecessary repeat scans.

For Swedish services, the takeaway is clear - prioritize interoperable triage tools, design local routing rules, and measure downstream effects like ED flow and report turnaround rather than treating algorithms as plug‑and‑play fixes (see Aidoc radiology AI imaging solutions, Change Healthcare AI-powered study prioritization case study).

Metric	Result (Change Healthcare / Aidoc case)
ED turnaround improvement	~27% faster over three months
AI‑positive study read time	15 minutes
AI‑negative study read time	18 minutes
Acute intracranial hemorrhages flagged	77 patients in one month

Sepsis early‑warning systems offer one of the clearest “so what?” payoffs for Swedish hospitals - when an algorithm flags deterioration hours earlier it can speed antibiotics and ICU care - but the evidence demands pragmatic caution: Johns Hopkins' TREWS and other models have identified sepsis well ahead of usual protocols (the TREWS work is summarized in the Mayo Clinic Platform review, showing earlier alerts linked to faster antibiotics and lower mortality), and vendor/health system rollouts (summarized in a recent Medwave roundup) report alerts that catch patients roughly six hours sooner or boost recognition by double‑digit percentages; yet high‑profile investigations found real‑world pitfalls when models implicitly learned clinician actions instead of true physiologic risk, so some Epic Sepsis Model validations performed much worse outside the developer's tests (see the STAT investigation and Michigan analysis).

For Sweden that means pilots should prioritise models validated on pre‑treatment data, integrate alerts into nurse/physician workflows to avoid late‑cueing, and measure downstream effects - time to antibiotics, ICU transfers, and alert burden - rather than trusting vendor AUCs alone.

Metric	Result
TREWS early detection	Identified ~82% of retrospectively confirmed cases; earlier alerts linked to reduced mortality (Mayo Clinic Platform)
Epic/UPMC example	Machine learning identified sepsis ~6 hours earlier in some deployments (Medwave summary)
Kaiser Permanente	Increased recognition of impending severe sepsis by ~21% (Medwave summary)
Epic model external performance	87% overall but 62% when restricted to pre‑onset data; 53% before blood culture orders in a Michigan study

“Sepsis has all these vague symptoms, so when a patient shows up with an infection, it can be really hard to know who can be sent home with some antibiotics and who might need to stay in the intensive care unit.”

Predicting 30‑day readmission for heart failure is one of the highest‑value AI moves for Swedish hospitals: systematic reviews show many machine‑learning models exist but vary in quality and key predictors (Systematic review of ML‑based 30‑day heart failure readmission - PubMed), while a process‑mining + deep‑learning approach produced an AUROC of 0.93 on MIMIC‑III by modelling time‑stamped EHR events and past visits (Process mining and deep learning for unplanned 30‑day heart failure readmission - BMC Medical Informatics and Decision Making).

Real‑world translation matters: MultiCare's deployed model (AUROC 0.85, sensitivity 0.84) fed daily risk scores into nurse worklists and scaled predictions to ~150 daily alerts, letting teams prioritize follow‑up and likely preventing avoidable returns - an operational pattern Swedish regions can mirror and augment with regional remote‑monitoring pilots like Kontiki that already report measurable drops in readmissions across Sweden and Norway (Kontiki remote‑monitoring pilot reducing heart failure readmissions in Sweden and Norway).

The practical takeaway: validate models on local data, link scores to discharge plans and outreach, and measure downstream bed‑days and follow‑up adherence so predictions turn into fewer people readmitted and more staffed capacity for complex care.

Source / Metric	Result
Pishgar et al. (BMC)	AUROC 0.93; precision 0.886; sensitivity 0.805
MultiCare (Health Catalyst)	AUROC 0.85; sensitivity 0.84; ~150 daily HF risk predictions

Sweden's hospitals and regional clinics can gain practical advantage from Mayo Clinic's work showing that AI‑enabled ECGs and wearable single‑lead tracings turn a cheap, ubiquitous test into a sensitive arrhythmia and heart‑disease screen: Mayo teams have adapted a 12‑lead algorithm to read Apple Watch ECGs and single‑lead recordings to flag low ejection fraction, atrial fibrillation, hypertrophic cardiomyopathy and even early cardiac amyloidosis, all with performance good enough to act as a scalable triage step before imaging or specialist referral; see Mayo Clinic's write‑ups on ECG‑AI for multiple heart diseases and the smartwatch study that transmitted 125,610 ECGs from 2,454 participants for analysis (AI transforms smartwatch ECGs into a diagnostic tool).

For a country with long distances and ageing populations, the “from your sofa” potential - low‑cost, EHR‑integrated alerts that prompt targeted follow‑up - offers a clear “so what?”: earlier detection, fewer missed arrhythmias, and smarter use of echo and cardiology resources when clinician review bridges AI flags to care.

Metric	Result
Participants (watch study)	2,454
ECG recordings transmitted	125,610
AUC for single‑lead detection of low EF	~0.88
Sensitivity / Specificity (watch study)	81.2% / 81.3%

“Hopefully, this type of technology, if properly deployed, will enhance the capabilities of small‑town doctors everywhere by putting some of the skills of a tertiary‑care cardiologist in their pockets.”

Quantitative coronary plaque analysis by CCTA is becoming a practical tool for Swedish cardiology: multicentre work shows deep‑learning tools can segment and quantify plaque with performance that approaches invasive standards, and that makes it possible to move from

does the artery look narrow?

to

how much and what kind of plaque does this patient carry and how is it changing?

- a shift that matters for Sweden's ageing population and long travel distances, because a noninvasive, reproducible plaque volume or low‑attenuation plaque score can target preventive therapy and remote follow‑up to the people who need it most.

The prospective REVEALPLAQUE study compared automated deep‑learning quantification with intravascular ultrasound across multiple centres and supports this clinical promise (REVEALPLAQUE prospective DL CCTA versus IVUS study on PubMed), while independent validation work reported high sensitivity, specificity and accuracy for automated plaque detection and classification (QIMS study: automated detection and classification of coronary plaques).

Expert guidance from the cardiac imaging community also stresses that gains depend on standardized acquisition, consistent reconstruction and validated software before using serial plaque measures to change care (ACC guidance on quantitative coronary plaque analysis by CCTA).

The practical

so what?

is vivid: a single CCTA can become a 3‑D, color‑coded baseline that clinics can trend over time to personalise prevention - but only if image quality, thresholds and reporting are harmonised across scanners and regions.

Metric / Study	Result
QIMS automated algorithm - sensitivity	92%
QIMS automated algorithm - specificity	87%
QIMS automated algorithm - accuracy	89%
QIMS - NPV / PPV	95% / 79%
REVEALPLAQUE	Prospective multicentre DL CCTA vs IVUS - supports quantitative agreement

Closing the loop on imaging doesn't have to be a paperwork black hole: AI workflow tools can extract follow‑up recommendations at point‑of‑read, triage them by urgency, and drive patient‑facing reminders so nothing actionable gets forgotten - a small lung nodule in a busy ED can be turned from “noted in text” into a tracked task, patient text and an audit trail.

Vendors like Nuance offer integrated solutions (PowerScribe Follow‑up Manager) that report a 7X rise in incidental‑finding identification, a 98% follow‑up closure rate and a 4.1X ROI, while professional guidance from the ACR outlines patient‑friendly, triaged report recommendations and workflows for sending lay‑language follow‑up lists to patients and clinicians.

Pairing closed‑loop imaging follow‑up with automated PROM collection via AI chatbots also pays off: real‑world projects show engagement jumps (e.g., +45% with Snapdragon/Intermountain, 300% with Basal Analytics) and high completion for digital rehab PROMs (Kaia 91%).

For Swedish regions juggling long waits and dispersed populations, the practical win is clear - automate the mundane communication steps, measure closure and PROM response rates, and free clinicians to focus on interpretation and care escalation rather than chasing paperwork.

Read more on the Nuance PowerScribe Follow‑up Manager product page, the ACR patient‑facing follow‑up recommendations and workflows, and Medwave's review of automated PROM collection and engagement studies.

Program / Metric	Result
Nuance PowerScribe Follow‑up Manager	7X incidental findings identification; 98% follow‑up closure; 4.1X ROI
ACR triaged patient‑friendly follow‑up	Automated NLP generates lay‑language follow‑up lists sent to patient and provider
AI chatbots for PROMs	Engagement: +45% (Snapdragon/Intermountain); 300% (Basal Analytics); completion 91% (Kaia)

Sweden's path to smarter staffing starts with better forecasts and pragmatic analytics: large European projects like RN4CAST nurse forecasting study demonstrate how national‑level nurse data (in Sweden nurses were sampled via the Swedish Nursing Association, which covers about 85% of nurses) can ground workforce planning in real realities rather than crude headcount ratios.

Operational analytics that combine historical volumes with nontemporal signals - weather, web search and site visits, and event data - have proved especially useful for predicting patient arrivals, with internet‑search features reducing forecast error substantially in several studies and, in one example, preventing improper staffing on two extra days per year (systematic review of patient-arrival forecasting features).

The practical

“so what?”

is clear: when regional Swedish hospitals pair validated forecasts with flexible rosters and targeted remote monitoring, staffed capacity can bend away from costly over‑roster or dangerous understaffing, turning analytics into more reliable shifts, fewer cancelled procedures and better continuity for chronic care travelers.

Metric / Source	Key insight for Sweden
RN4CAST (EU)	Sweden sampled nurses via Swedish Nursing Association (≈85% coverage) to link staffing, work environment and outcomes
Patient‑arrival features (systematic review)	Weather, internet search/usage and social‑interaction data improve forecasts; internet search reduced errors up to ~33% in some studies
Operational payoff	Better forecasts → targeted staffing and remote follow‑up, reducing improper staffing days and smoothing capacity

Sweden already has momentum - AI Sweden's national mapping found 179 healthcare AI initiatives and tools such as Vårdkartan are helping regions share what works - yet pressure is real: the 80+ population is set to rise almost 50% by 2031, roughly +85,000 more healthcare workers will be needed, and clinicians still spend up to two working days a week on administration (AI Sweden).

That combination makes a pragmatic, phased approach the sensible route: start with small, high‑value pilots that solve clear workflow pain (radiology triage, readmission outreach, sepsis alerts), validate models on local data, lock integration into PACS/EHRs before scaling, and invest in staff skills so technology frees time rather than adding work.

Use national resources to shorten the learning curve - explore AI Sweden's sector work and Vårdkartan for regional examples, try the free Get Started with AI online course for basic literacy, and for practical workplace training consider the Nucamp AI Essentials for Work bootcamp (15 Weeks) to build prompt‑writing and deployment skills across teams.

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“The single biggest driver behind our decision to start working with AI-based diagnostic tools at all was definitely the genuine shortage of radiologists in the field of mammography.” - Jonas Söderberg