PLAYBOOK ATLASPlaybook Atlas

Healthcare

AI in healthcare: explore 47 applications.

Support clinical and operational teams with clearly scoped, source-linked information.

Explore documentation, patient-flow planning, imaging support and clinical-evidence review, with the intended user and purpose defined for each application.

Plan your first AI move See the 47 applications

Where the 47 applications sit

Explore the domains
  1. 35Patient Care Delivery
  2. 18Medical Imaging
  3. 10Clinical Operations
  4. 3Support Processes

01Why AI now

Why healthcare is moving on AI, in three figures.

Each figure links to its source.

  1. 49% of physicians reported burnout in 2024

    Medscape's 2024 survey found 49% overall physician burnout, with emergency medicine highest at 63% and bureaucratic tasks the leading contributor.

    Source: The DO (American Osteopathic Association)

  2. 72% of physicians use AI in practice

    The AMA's 2026 survey found that 72% of U.S. physicians incorporated at least one AI use case into their practice, up from 48% in 2024.

    Source: American Medical Association

  3. Medicaid improper payments reached $37.4B

    CMS estimated $37.39 billion in Medicaid improper payments for fiscal 2025, with 77.17% resulting from insufficient documentation.

    Source: Centers for Medicare & Medicaid Services

How to evaluate a first project

Distinguish an administrative task from an application that influences clinical care. Specify the intended population, setting, inputs and reviewer. Assess errors, missing information and workflow consequences against appropriate reference evidence; clinical use requires validation and review for that particular intended purpose.

02Capability map

Where healthcare puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Patient Care Delivery

Applications in Patient Care Delivery

What the use cases ask AI to do

Share of the healthcare use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    50%

  2. Perception

    Sees, hears and reads: documents, images, audio and video.

    36%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    14%

Under 1% each: generation and optimization.

03Workflow ideas

Ways AI could change the work, in healthcare.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 41 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches healthcare uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 9 applications

    Generative AI

    Models that learn data distributions to generate new text, images, code, and media.

    Use it when

    • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
    • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
    • When you can ground the model on reliable internal or external data sources to improve factuality.

    Skip it when

    • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
    • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
    • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
    Generative AI, in depth

  2. 02 · 8 applications

    Computer-Vision

    AI pattern for interpreting and acting on images and video data.

    Use it when

    • When you need to automate or augment tasks that currently rely on human visual inspection (e.g., quality control, surveillance).
    • When visual information (images, video, documents) is the primary or richest source of signal for the problem.
    • When real-time or near-real-time decisions based on camera feeds are required (e.g., robotics, autonomous systems, safety monitoring).

    Skip it when

    • When the problem can be solved more simply and robustly with non-visual sensors or structured data (e.g., barcodes, RFID, logs).
    • When you lack access to sufficient, representative visual data and cannot feasibly collect or label it.
    • When privacy, legal, or ethical constraints prohibit capturing or processing the necessary images or video.
    Computer-Vision, in depth

  3. 03 · 5 applications

    Clinical-Workflow-Intelligence

    Embed AI directly into clinician-facing workflow and decision-support loops.

    Clinical-Workflow-Intelligence, in depth

05The applications

47 healthcare applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Radiology Imaging Diagnostics HubThis AI solution covers AI systems that interpret medical images to detect, classify, and quantify diseases, then surface structured findings and recommendations to clinicians. By automating image review, triage, and decision support, these tools improve diagnostic accuracy, shorten turnaround times, and enable more personalized, data-driven treatment. The result is higher throughput for imaging departments, better utilization of specialist time, and improved clinical outcomes at lower per‑scan cost.Manual → Vision31 use cases
  2. Clinical Decision Support Intelligence HubAI Clinical Decision Intelligence uses machine learning and generative AI to analyze patient data, guidelines, imaging, and real‑world evidence to recommend diagnosis, treatment, and care pathway options at the point of care. It supports physicians, nurses, and patients across specialties and settings—from oncology to emergency medicine—reducing variation, improving outcomes, and accelerating time‑to‑decision while optimizing resource use and reimbursement performance.Expert → AI22 use cases
  3. Neuroimaging Diagnostics and ReportingNeuro-Imaging AI Diagnostics applies deep learning and multimodal models to interpret brain and neurovascular imaging, generate structured reports, and provide real-time decision support across the neuroradiology workflow. It enhances diagnostic accuracy, speeds fracture and stroke detection, and links imaging to genomics and outcomes for precision oncology. This improves care quality, reduces time-to-diagnosis, and supports scalable training and benchmarking for radiologists and life sciences teams.Manual → Vision14 use cases
  4. Radiology Diagnostic Interpretation SupportRadiology diagnostics support refers to software applications that assist radiologists and clinicians in interpreting medical images and related clinical data to reach faster, more accurate diagnoses. These tools analyze modalities such as X‑ray, CT, MRI, PET, SPECT/CT, and digital pathology, highlighting potential abnormalities, quantifying findings, prioritizing urgent cases, and standardizing reports. They are tightly integrated into radiology workflows and clinical decision support systems, with the human radiologist retaining final responsibility for interpretation and communication. This application matters because imaging volumes are growing much faster than radiologist capacity, increasing the risk of missed findings, delayed reports, and inconsistent reads across clinicians and sites. By reducing manual, repetitive reading tasks and providing a second set of “eyes” on complex images, radiology diagnostics support improves diagnostic accuracy, speeds turnaround times, and enables earlier disease detection—especially for high‑impact conditions like cancer and cardiovascular disease. It also supports precision medicine by offering more consistent measurements, treatment response assessments, and structured reporting across large patient populations.Manual → Vision11 use cases
  5. Precision Drug Selection AssistantThis AI solution uses AI to identify, design, and select the most effective drugs for individual patients by integrating clinical data, genomics, microbiome profiles, and real‑time trial outcomes. It accelerates drug discovery, optimizes clinical trial design and adaptivity, and powers precision medicine decision support at the point of care. Healthcare organizations gain better treatment outcomes, reduced trial and development costs, and faster time-to-approval for novel therapies.Expert → AI8 use cases
  6. Healthcare Capacity and Scheduling OptimizerThis application area focuses on forecasting patient demand and optimally assigning appointments, staff, and clinical resources in healthcare settings. It brings together demand prediction, capacity planning, and workflow optimization to ensure the right providers, rooms, and time slots are available when and where patients need them. By replacing static, manual scheduling rules with data‑driven, dynamic optimization, hospitals and clinics can reduce wait times, smooth patient flow, and improve utilization of scarce clinical resources. It matters because healthcare operations are chronically constrained: staff shortages, limited rooms and beds, and unpredictable patient arrivals lead to long waits, no‑shows, overtime, and rushed care. AI‑enabled scheduling and capacity optimization models use historical and real‑time data to predict appointment demand, no‑show risk, and workload, then automatically recommend or execute optimal schedules and staffing plans. This improves access to care, clinician productivity, and patient experience while lowering operational costs and burnout risk. Batch → RT6 use cases
All 47 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. HIPAA · cloud handling of protected health information

    Law or regulation

    United States · covered entities and business associates

    A cloud provider creating, receiving, maintaining or transmitting ePHI on behalf of a covered entity may be a business associate. Applicable agreements, permitted uses and security safeguards must be addressed; not every health app is HIPAA-covered.

    HHS: HIPAA and cloud computing

    Source checked

    Project planning note

    Map PHI flows, vendor roles, permitted processing and required agreements before connecting patient records.

  2. FDA · medical-device software classification

    Law or regulation

    United States · software with relevant medical intended uses

    FDA regulates medical devices, including certain AI-enabled software, according to intended use and risk. Some software functions are excluded; applicable premarket pathways are not limited to 510(k) and De Novo.

    FDA: Artificial Intelligence-Enabled Medical Devices

    Source checked

    Project planning note

    Define each software function’s intended use and determine its regulatory status before planning a clinical rollout.

07The AI graveyard

One failure, and the lesson it paid for.

  1. IBM Watson Health

    2022 · Source

    $4B+

    What went wrong

    Overpromised on cancer diagnosis capabilities. Couldn't deliver accurate recommendations at scale. Physicians rejected black-box suggestions that contradicted clinical experience.

    The lesson

    Start narrow, earn clinical trust, then expand. Never override physician judgment.

Your next step

Find your first move in healthcare.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 47 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call