Healthcare leaders rarely need another inspirational demo; they need proof that a model can survive contact with messy data, overloaded clinicians, and regulatory scrutiny. At Techtide Solutions, we treat case studies as “production archaeology”: they show what broke, what held, and what teams did when reality refused to match the slide deck. The patterns are surprisingly consistent across hospitals, payers, and startups.
Why real-world AI case studies matter in healthcare
1. From strong pilot projects to reliable care: showing real value in clinical and day-to-day healthcare work
Market overview: Gartner estimated that global spending on generative AI would reach $644 billion in 2025, and that level of spending is already forcing healthcare leaders to ask a direct question: what is actually ready for real use? In our experience, the gap between a successful pilot and reliable care is usually not model accuracy. It is operational reliability.
Production-ready healthcare AI needs stable integrations, predictable failure modes, and escalation paths clinicians can follow naturally. Strong case studies also show the unglamorous details. That includes downtime playbooks, backup behavior when the model cannot be trusted, and response plans for silent data drift. They also explain how teams handled an overnight EHR template change without breaking clinical trust.
2. What “transformative impact” really looks like: better diagnosis, smoother workflows, and clear results
Change in healthcare is almost never about one number. It usually comes from a combined effect across care quality, speed, and trust. In diagnostics, impact often means better performance during tired hours, like late shifts or high-volume periods. It also means fewer missed cases that depend on context, not just images. Operationally, impact means fewer clicks, handoffs, and queues. Every handoff creates another chance for a patient to be missed.
From our point of view, the strongest case studies connect three things. They link the model output, the workflow moment that uses it, and the action that changed. When teams don’t link those three tightly, “AI value” turns into a debate instead of a measurable result.
3. Core requirements: modernization, data management, and data governance
Case studies keep showing the same thing: AI capability depends on data capability. Modernization is not just moving to the cloud. It also means standardizing identities, making clinical concepts consistent, and making data history clear enough to review. Governance becomes the hidden team behind every model: who can use which data, for what reason, and under what consent rules.
We also see one practical requirement that teams often do not take seriously enough: realistic integration planning. The AI-Enabled Medical Device List is a reminder that some solutions are regulated devices, while many others are simply software, and governance needs to match that level of risk. In both cases, teams do better when they define responsibility early, especially for unclear edge cases.
Diagnostic imaging case studies of ai in healthcare
Medical imaging has looked like an easy AI fit for years. The images are already digital, labels can be prepared, and radiology workflows have clear intervention points. Even so, real-world case studies show harder problems elsewhere. Data shifts from new scanners or scan protocols can undermine performance. Clinician interaction is another challenge. Teams must use AI suggestions without trusting them too much. We treat imaging projects as systems that involve both people and technology, not just as model rollouts.
1. AI-assisted chest X-ray analysis for pulmonary diseases and COVID-19 screening phenotypes
Chest X-ray AI matured early because X-rays are high-volume, relatively standardized, and clinically central in pulmonary triage. The CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning line of work illustrates a recurring case-study lesson: even when a model performs well in controlled evaluation, deployment value hinges on how results are presented (localization cues, confidence calibration, “why” hints) and when they are shown (triage queue vs final read).
During respiratory surges, health systems also learned to separate “screening phenotypes” from “diagnosis.” In practice, AI can help prioritize likely abnormal studies, but the care pathway still depends on confirmatory context—symptoms, vitals, and lab results—because “pneumonia-like” imaging patterns can overlap many conditions.
2. Dermatology scans for melanoma: decision support and the role of annotated skin-lesion datasets
Dermatology case studies are fundamentally about labeling economics and bias control. The paper Dermatologist-level classification of skin cancer with deep neural networks became influential not only because of performance claims, but because it highlighted the power of curated, annotated image collections—and the fragility of systems trained on narrow visual distributions.
From our perspective, real deployments succeed when decision support is framed as “second look” rather than “verdict.” Clinics that treat AI as a structured checklist (flag, document, decide) tend to get better adoption than clinics that expect clinicians to “just trust the model,” especially when skin tone diversity and imaging conditions vary widely.
3. CT and MRI scan analysis: finding useful insights from large image sets for faster, more consistent review
CT and MRI work creates a different kind of scale problem. These scans contain a large amount of image data, and doctors often have very little time to review them, especially in brain and trauma cases. Many case studies describe systems that focus on urgent triage first. Instead of trying to write a full radiology report, they look for likely brain bleeds, large blocked blood vessels, or lung blood clots, then send those cases to the right queue faster.
From a technical side, these systems depend heavily on careful image preparation. Teams need to handle slice thickness in a consistent way, reduce problems caused by motion, and keep image direction aligned correctly. In real-world use, the key lesson is that speed alone is not enough. If the model marks something as urgent, that alert must connect to a real response path. Otherwise, it becomes just one more warning that people start to ignore.
4. AI-assisted breast cancer detection: supporting mammography reading workflows and reducing reviewer workload
Breast screening is one of the clearest examples of “human-AI teaming” because many workflows already include double reading and arbitration. The study International evaluation of an AI system for breast cancer screening helped push the field toward a more nuanced question than “can AI read mammograms?”—namely, “how should AI change reading strategy without eroding safety?”
In the case studies we trust, AI acts as a workflow optimizer. It surfaces difficult cases, highlights regions of interest, and supports consistent attention during high volume sessions. The subtle but crucial detail is auditability. Teams want to reconstruct what the model showed at read time, not just what it would show today.
5. Artificial intelligence for digital pathology: whole-slide image analysis and computational consensus for cancer subtyping
Digital pathology is a compute and storage story disguised as an AI story. Whole-slide images behave more like “gigapixel maps” than photos, so real-world systems tile, embed, and aggregate features across tissue regions. The case-study lesson is that the model is only half the product; the viewer, navigation speed, and annotation tooling determine whether a pathologist feels helped or slowed.
Regulatory momentum matters here because it changes procurement and risk posture. A widely cited milestone is Paige Receives First Ever FDA Approval for AI Product in Digital Pathology, which illustrates how validation, generalization claims, and intended-use boundaries become part of the product itself. In our view, the deepest lesson is that “computational consensus” only earns trust when it is transparent about uncertainty and visual evidence.
6. AI for ophthalmology: accelerating screening and triage for diabetic retinopathy, glaucoma, and cataract telehealth monitoring
Ophthalmology case studies are often the closest thing to “AI at the edge” in healthcare: portable retinal cameras, remote clinics, and fast screening loops. The landmark narrative around autonomous screening is captured in FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems, which emphasizes a real operational goal: bring screening to settings where specialists are scarce.
Telehealth monitoring also exposes a lesson we care about as builders: device workflows need robust quality gating. If image quality is poor, the system must say so clearly, because “uncertain” results are not neutral—they can trigger missed referrals or unnecessary anxiety depending on how they’re communicated.
Clinical decision support and pharmacy-led AI case studies
Clinical decision support is where the “AI story” becomes deeply human: people are stressed, time is short, and the wrong recommendation can harm. Pharmacy teams, in particular, have become pragmatic innovators because they sit at the intersection of medication safety, operational throughput, and evidence review. We like these case studies because they expose governance and accountability in plain sight.
1. Leveraging AI to reduce use of deliriogenic medications in clinical decision support
Delirium prevention is a textbook case for risk stratification paired with targeted intervention. The ASHP case study Leveraging AI to Reduce Use of Deliriogenic Medications describes a multimodal approach that blends structured EHR signals with note-derived context to surface patients at elevated risk, then supports clinical teams in prioritizing assessment and safer prescribing.
What stands out to us is the product shape: not a black-box recommendation, but an embedded workflow artifact (risk visibility inside daily lists) that aligns with how teams actually coordinate. That design choice—making the model “legible in the workflow”—often matters more than algorithm choice.
2. Use of pharmacist-reinforced AI tools for drug information workflows
Drug information is a perfect environment for “human-in-the-loop” AI because the cost of a wrong answer is high and the evidence base changes constantly. The ASHP case study Use of Pharmacist-Reinforced AI Tool for Drug Information highlights a pattern we increasingly recommend: pair algorithmic retrieval and drafting with pharmacist review that actively improves future responses.
In our view, this is the right kind of “augmentation” for clinical knowledge work. Rather than pretending the model is a clinician, the system behaves like an evidence-summarizing junior analyst whose work must be signed and owned by professionals.
3. Enhancing pharmacy efficiency with AI-assisted clinical documentation tools
Documentation automation is attractive because it targets a daily pain: charting that competes with patient attention. The ASHP case study Enhancing Pharmacy Efficiency with an AI-Assisted Clinical Documentation Tool describes a pharmacy team adopting ambient-style documentation assistance to reduce distraction during visits and accelerate note completion afterward.
From a software engineering standpoint, we treat this as a “drafting pipeline” with strict guardrails: capture, transcribe, structure, draft, and then force explicit review. The biggest risk is subtle hallucination—plausible text that was never said—so safe implementations emphasize provenance (what came from audio vs what was inferred).
4. Project Cable Car: pharmacy fax classification as workflow optimization
Fax is an unglamorous backbone of healthcare, and that’s exactly why automating it can be so impactful. The ASHP case study Project Cable Car: Pharmacy Fax Classification outlines an AI system that reads incoming medication faxes, interprets intent, labels content, and routes documents into the right work queues.
Technically, this is a document AI stack: OCR, layout parsing, entity extraction, and intent classification, followed by deterministic workflow routing. Operationally, the lesson is simple: automating “sorting” is often safer than automating “deciding,” and it still returns real time to clinicians.
Operational and administrative automation case studies across health systems
Operational AI is where we see the fastest ROI and the most underappreciated risk. Automating admin tasks can remove friction, but it can also create invisible failure modes—missing a referral, misrouting a message, or scheduling the wrong appointment type. Case studies matter because they show the control surfaces: audits, monitoring, and escalation rules that keep automation from becoming chaos.
1. Automation of administrative tasks using natural language processing for clinical documentation and EHR workflows
Ambient documentation is one of the clearest “AI is actually helping” stories, and it’s increasingly studied in real care settings. The article Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout reflects a broader trend: clinicians will adopt automation when it gives them time back without adding cognitive risk.
Safety, however, is not automatic. The evaluation Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters underscores a lesson we’ve learned the hard way: “draft notes” must be treated like draft code—reviewed, tested, and never merged into the record without explicit human acceptance.
2. Virtual care navigation and “digital front door” assistants to reduce contact center burden and improve patient self-service
Digital front door assistants work best when they avoid diagnosis and focus on navigation: appointment preparation, benefit questions, wayfinding, and post-visit instructions. A modern example is University Hospitals and Hippocratic AI Collaborate to Advance Patient Outcomes Through Safe, Patient-Facing AI, which emphasizes conversational agents designed for patient engagement rather than clinical judgment.
From our standpoint, the architecture lives or dies by safe boundaries: retrieval from approved content, robust identity verification, and clear handoff to humans. If a bot can’t confidently classify intent, it should route—not guess—because “being helpful” is not the same as being safe.
3. AI-driven scheduling and resource allocation to reduce wait times and improve satisfaction
Scheduling is a constrained optimization problem with human consequences. A concrete example is Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial, which illustrates how AI can streamline pre-visit steps and reduce friction in the outpatient journey.
In implementation terms, we think of scheduling AI as “policy plus constraints.” The policy predicts demand and no-shows; constraints enforce clinical rules (visit type, staffing, equipment). Case studies consistently show that constrained automation earns trust, while unconstrained “smart scheduling” creates downstream rework.
4. Connected and ambient care: wearables, touch-free sensors, and smart device monitoring for continuous insights
Connected care mainly deals with data that arrives over time. The job is to take in steady data streams, spot unusual changes, and respond without overwhelming staff. Wearables and room sensors can help detect falls, track patients after discharge, or monitor chronic conditions. But the hardest part is not collecting the data. It is deciding which signals truly need action.
At Techtide Solutions, we design these systems with “alert budgets” in mind. Instead of trying to catch everything, we focus on sending the highest number of useful alerts each clinician can realistically handle in an hour. That requires a normal baseline for each patient, rules to reduce unnecessary alerts, and trends that staff can easily understand. Case studies work best when monitoring is built into the care program itself, not just added to a dashboard.
Population health, risk adjustment, and predictive analytics in practice
Population analytics is where healthcare AI most often collides with incentives. Risk adjustment models, gap closure tools, and care management predictions can improve funding alignment and patient outcomes, but they can also amplify bias if teams treat proxies as truth. Strong case studies show how organizations align data science, compliance, and clinical leadership around a shared definition of “need.”
1. Inferscience HCC Assistant: real-time risk adjustment coding recommendations and gap analysis for missed codes
Risk adjustment workflows live inside documentation habits, which is why “point-of-care” recommendations are so tempting. The vendor description HCC Coding Software To Improve Risk Adjustment frames a common pattern: use NLP to surface potential coding gaps while clinicians are still composing the assessment and plan.
Operationally, we see two prerequisites for safe adoption. First, clinical education must explain why HCC capture is annual; the practice guide How to Correctly Capture Patient Risk for Value-Based Care Programs makes that cycle explicit. Second, organizations need audit tooling so coders and clinicians can resolve disagreements without turning AI suggestions into “autopilot billing.”
2. University Hospitals: leveraging NLP for population health management to identify at-risk groups and care gaps
NLP is often the missing bridge between population health dashboards and the reality of clinician notes, pathology narratives, and scanned documents. University Hospitals describes that intent directly in its partnership announcement: Utilize natural language processing (NLP) to uncover the unstructured information contained within clinician notes, pathology reports and genomics results for early disease identification and intervention.
From our point of view, the technical takeaway is not “use NLP,” but “operationalize NLP.” That means building concept dictionaries, harmonizing ontologies, validating extraction quality with clinicians, and then wiring results into care gap worklists that people already use.
3. Healthfirst: scaling machine learning operations to automate data cleaning, normalization, feature engineering, and model training
Payers and at-risk providers often discover that model development is the easy part; the hard part is repeating it reliably across lines of business. The case study Healthfirst Achieves Agile AI/ML in Healthcare reflects an MLOps reality: prediction pipelines only matter if they can be rebuilt, audited, and monitored without heroics.
We typically translate that lesson into engineering requirements: versioned datasets, reproducible feature pipelines, automated checks for schema drift, and model registries tied to governance approvals. Without those pieces, teams end up “retraining by folklore,” which is not a sustainable operating model.
4. Building and operationalizing outcome predictions in existing workflows with continuous monitoring
Outcome predictions fail when they live in a separate portal that no one has time to open. Successful case studies embed risk signals where decisions happen: discharge planning, care coordination queues, or nurse triage worklists. In our experience, the design goal is “one extra glance,” not “one more tool.”
Governance frameworks help keep that embedding responsible. The Artificial Intelligence Risk Management Framework is useful here as a shared vocabulary for mapping risks (validity, privacy, fairness, transparency) to controls (testing, monitoring, documentation, oversight). We treat monitoring as a product feature: drift detection, feedback loops, and clear rollback triggers.
Prediction-focused clinical case studies: bladder and seizure forecasting
Prediction is where healthcare AI becomes most personal: it promises to warn patients before harm occurs. We also consider it the highest-risk category of “non-diagnostic” AI because the output can change behavior—when a patient seeks care, how a clinician triages, or whether a device stimulates nerves. Case studies in this space are valuable precisely because they expose the full loop from sensing to action.
1. Bladder volume prediction: enabling conditional neurostimulation and timely patient notifications
Closed-loop bladder care illustrates a powerful pattern: predict a physiological state, then trigger an intervention only when needed. The study Real-Time Bladder Pressure Estimation for Closed-Loop Control in a Detrusor Overactivity Model captures the engineering essence—decode signals in real time and use that estimate to drive conditional stimulation rather than continuous therapy.
In our view, the key lesson is that “prediction” is not the end goal; timing is. Systems must balance false alarms (annoying, fatiguing) against missed detections (harmful). Practical deployments need patient-specific thresholds, robust sensor QA, and safety interlocks that default to conservative behavior.
2. Real-time monitoring architecture: live signal processing for estimating volume or pressure
Real-time systems bring up practical limits that are easy to miss in research papers written after the fact. Delay limits, computing power, and noisy signals all affect what can actually work, especially on small devices. Even when teams use advanced neural methods, many real-world examples point to a simple lesson: it is better to use stable features that work well enough than fragile systems that need perfect conditions and constant care.
We design these systems like live streaming systems. Data flows in continuously. The system calculates features over short windows, makes predictions immediately, and uses a control layer to enforce safety limits.
The practical lesson stays simple: if the monitoring system can’t clearly explain why it reacted, doctors and patients won’t trust the stimulation.
3. Epileptic seizure prediction: addressing unpredictable seizures and refractory patient needs
Seizure forecasting remains one of the most compelling (and challenging) prediction problems because of patient variability and the stakes of false reassurance. The paper Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning is often discussed because it connects wearables to real-world forecasting rather than lab-only detection.
From our perspective, the most durable lesson is personalization. Population models can provide a starting point, but clinical usefulness tends to emerge when systems learn an individual’s rhythms, medication changes, sleep patterns, and stress signals—while still preserving privacy and maintaining robust consent boundaries.
Ethics, bias, accountability, and trust in case studies of ai in healthcare
Trust is not a branding exercise in healthcare; it is an operational requirement. Every model encodes decisions about labels, proxies, and objectives, and those choices can quietly create inequity even when “race isn’t used” or “the model is accurate.” We push ethics into engineering: data selection, evaluation slices, and accountability pathways become first-class design elements.
1. Pneumonia mortality risk prediction: counterintuitive patterns and hidden confounding from care differences
The classic cautionary tale here is that models can learn “who gets treated” rather than “who is sick.” Rich Caruana’s talk Intelligible Machine Learning Models for HealthCare describes how interpretable modeling can expose counterintuitive patterns that would otherwise remain hidden inside complex predictors.
We consider the lesson foundational: without interpretability and clinical review, a model can look brilliant while encoding confounding from practice patterns. Case studies that surface these failures are not embarrassing—they are how the field learns to build safer systems.
2. Test ordering recommendations: system-wide training data vs facility-level realities and unintended clinical tradeoffs
Test ordering recommendation systems reveal a subtle hazard: the “best” policy depends on local workflows, lab turnaround times, and staffing constraints. Research such as An Optimal Policy for Patient Laboratory Tests in Intensive Care Units explores learning policies from historical data, but real-world translation demands facility-level calibration and strong clinician oversight.
In practical deployments, we’ve found that the biggest unintended tradeoff is shifting burden rather than reducing it. If a model reduces tests but increases clinician uncertainty, the system may trigger more consults or repeated assessments, moving cost from the lab to the bedside.
3. Patient autonomy and algorithm opt-outs: coded bias concerns, privacy fears, and representation impacts
Opt-outs are often treated as a compliance checkbox, but case studies show they shape model validity. When certain groups opt out at higher rates—because of historic mistrust, privacy concerns, or fear of discrimination—the resulting training data can become less representative, and the model can degrade specifically for the people already underserved.
From our standpoint, autonomy means more than “allow opt-out.” It also means communicating what the model does, what data it uses, how long it retains information, and how humans remain accountable. Transparent consent UX is not just ethical; it is also statistically stabilizing.
4. Care management algorithms: cost-to-treat proxy bias and the risk of systematically underrating patient need
One of the most cited real-world bias case studies is the finding that cost can be a misleading proxy for need. The article Dissecting racial bias in an algorithm used to manage the health of populations explains how a widely used approach can under-identify patients for extra care when historical spending differs across groups due to structural inequities.
We take a hard stance here: proxy choice is a design decision, and design decisions have moral weight. Responsible teams test alternative targets, measure subgroup performance, and treat “equity regressions” like safety regressions—something that blocks release.
5. Ethical questions raised by an automated healthcare app: trust, over-control, openness, and unfair outcomes
Automated healthcare apps are shaping behavior more and more through reminders, encouraging messages, and risk alerts. The ethical question is not simply whether this kind of guidance is allowed. The question is whether the system has the right to steer a patient toward one choice. That concern grows when the app’s goals, like lower costs or higher engagement, differ from patient priorities.
At TechTide Solutions, we design reflection prompts to feel like real clinical conversations. We offer clear choices and explain tradeoffs in plain language. Human handoff should happen immediately when the situation is serious. If an app cannot explain its reasoning clearly, it should not try to shape behavior. When a system is unclear and still pushes people, unfairness grows fast.
TechTide Solutions: building custom healthcare AI software tailored to customer needs
Case studies are not just stories we read; they’re constraints we build into our delivery playbooks. At Techtide Solutions, we approach healthcare AI as product engineering under clinical governance, not as model experimentation. The result is deliberately “less magical” and far more dependable.
1. Custom solution design that fits clinical workflows, patient experience, and day-to-day operations
Successful healthcare AI starts by mapping how people actually work, not by drawing system diagrams. In our projects, we begin by finding the key decision point. It may be triage, prescribing, coding, or scheduling. Next, we identify the person responsible for that decision. Then we shape the AI output for what that person can grasp quickly. In many cases, that means short summaries, ranked options, and links to evidence. It does not mean open ended text that can be misread.
We also design the no path on purpose. If the model is unsure, the software should fail safely. If key data is missing, it should hand the decision to a human. When a case falls outside training, the handoff should be clear and calm.
2. Secure data foundations and integrations: connecting EHR/EMR systems, imaging workflows, and data rules
Integration is where healthcare AI becomes real: patient identity checks, visit context, orders, results, and audit logs all need to work together. Our approach is to treat every connection like an agreement that must be watched closely. That means checking data structure, checking meaning, and sending alerts when source systems change. This discipline helps stop the quiet drift that can turn “working AI” into “dangerously wrong AI.”
We build data rules and controls around least privilege and clear limits on how teams can use data. We define access controls, logs, and retention rules in code, and we review them like any other safety-critical part of the system. Trust isn’t an extra feature—it’s the foundation.
3. Deployment and lifecycle support: monitoring, continuous improvement, and responsible workflow embedding
Deployment is the beginning of the real experiment, not the end. We ship monitoring for model performance, workflow adoption, and safety signals, then review those signals with stakeholders on a cadence that matches clinical risk. Continuous improvement becomes responsible only when it is controlled: versioned releases, documented changes, and backtesting before anything touches patient care.
A strong governance anchor is the FDA’s lifecycle-oriented perspective in Good Machine Learning Practice for Medical Device Development: Guiding Principles, even when the product is not a regulated device. We apply that mindset broadly: define intended use, test human-AI performance, and monitor after release as a standard operating procedure.
Conclusion: turning lessons into a repeatable playbook for adoption
Across these case studies, a consistent truth emerges: healthcare AI succeeds when it behaves like a well-governed clinical service, not a clever model. At TechTide Solutions, we build for a future where systems stay integrated, remain auditable, and admit uncertainty with humility. The winners will be the organizations that can operationalize learning without sacrificing trust.
1. Cross-cutting takeaways from diagnostics, operations, population health, and prediction case studies
Imaging case studies remind us to respect changing data conditions and the role of human review. Pharmacy case studies show AI works best when experts keep final responsibility instead of becoming passive users. Operational examples show how small process improvements compound when teams build safety checks and clear handoff paths into the system. Prediction focused work also reminds us that response speed, patient specific tuning, and alert burden all shape clinical reality. They are not minor technical details.
Above all, the strongest case studies treat trust as something you can track and improve. They look at how often people actually use the system, when they choose to ignore or replace its suggestions, what error reviews reveal, and whether results are fair across different groups. When teams can measure trust, they can make it stronger.
2. Implementation checklist: data access, domain expertise, computing capacity, and workflow integration research
Start by agreeing on the data rules: which fields are included, how often they change, and who is responsible when source systems are updated. Create a clinical review group that can check the results and decide what actions are safe. Confirm technical limits early, especially for imaging tools and live sensor systems. Just as importantly, study how the system fits into real care work as carefully as you test the model. If doctors and care teams cannot use it in the moment, it is not truly usable.
We also recommend setting aside budget for work after the system goes live. Monitoring, reviewing feedback, and checking the model again from time to time are not extra features. They are part of using the system responsibly.
3. Sustaining trust: transparency, accountability, and equity as ongoing operational requirements
Trust fades quietly when systems change and no one explains why. Strong programs describe what the model is meant to do in plain language, make it clear who is responsible for results, and check fairness before each release. For clinicians, transparency should focus on what helps them act: the key inputs, the system’s limits, and what to do when the model does not match human judgment.
Next step: if we at TechTide Solutions were advising your organization tomorrow, we would ask one basic question—which important decision do you want AI to help shape, and what proof would make your clinicians trust that the system belongs in that decision-making process?
