AI Tools for Healthcare: 2025 Overview

By

Dora Gurova

Updated:

September 13, 2025

AI has already changed AI, not just in theory, but in everyday tools that help professionals make better decisions, automate tedious tasks, and deliver more personalized patient care.

We created this list of the most compelling AI tools for healthcare, what they do, real-world use cases, and how emerging tools can empower healthcare providers and institutions.

Tool	Key features	Use cases
UI Bakery – AI App Generator	Generate functional, data-driven web apps from a single prompt (CRUD apps, admin panels, internal tools). Offers drag-and-drop UI builder, visual editing, templates, and integrations with databases & APIs.	Building internal dashboards for monitoring patient metrics, hospital operations, claim statistics. Patient portals (appointments, records) with scheduling and history. Admin tools: appointment calendars, PDF report generation, data intake forms. Rapid prototyping of healthcare tools without heavy development overhead.
Ada Health	AI-driven symptom checker / diagnostic assistant that asks about symptoms and suggests possibilities.	Patient self-assessment, triage, guiding whether to seek medical attention or emergency care.
Aiddison (Merck)	AI tool for drug discovery: molecular modeling (structure- & ligand-based), predictions.	Pharmaceutical R&D: discovering new drug leads, optimizing candidate selection.
BioMorph	Predictive analytics and modelling for how chemical compounds interact; predicts outcomes in cells/tissues.	Pre-clinical drug discovery, narrowing down which compounds to test in labs, reducing costs/time.
MONAI (Medical Open Network for AI)	Open-source framework for medical imaging: segmentation, classification, registration, etc.	Hospitals and research centers for imaging-based diagnosis (tumors, organ anomalies, lung nodules, etc.); building custom imaging pipelines.
Heidi Health	AI scribe and documentation assistant: transcribes consultations, structures notes, integrates with EHRs.	Reducing clinician documentation time; improving note accuracy; freeing up more patient-facing time.

Use cases: putting tools into practice

Below are concrete scenarios showing how different AI tools for healthcare professionals are being used in real settings:

Ambient documentation: Doctors using tools like DAX Copilot or Heidi Health to transcribe patient visits automatically, generate summaries, referral letters, and reduce after-visit workload.
Diagnostics & imaging: Tools like MONAI or BioMorph used in hospitals for radiology workflows (detecting tumors or lung nodules, etc.), helping radiologists with segmentation and classification tasks.
Patient self-assessment & triage: Ada Health used by patients to assess symptoms; risk stratification tools integrated into virtual care platforms, helping decide urgency of care.
Clinical decision support / evidence access: OpenEvidence gives physicians quick access to medical literature and evidence for decision making. Helps reduce time reading individual papers.
Medical administration & claims: Counterforce Health helping with appeals; UI Bakery used to build internal tools for tracking claims, monitoring denials, generating reports; tools to automate scheduling and billing.
R&D / drug discovery: Aiddison and BioMorph helping pharma companies find molecule leads, predict compound behaviors.
Healthcare dashboards & portals: UI Bakery templates used to launch patient portals, dashboards for care metrics, internal admin views (appointment calendars, monitoring dashboards).

Best practices & criteria when choosing AI tools for healthcare

When selecting an AI tool for healthcare, or evaluating the best AI tools for healthcare to adopt, consider:

Regulatory compliance: Does it meet GDPR, HIPAA, local data protection requirements?
Validation & accuracy: Is there published evidence? FDA approvals? Peer-reviewed studies or performance metrics?
Data security: Secure integration with EHRs; data encryption; audit trails.
Usability & integration: How easily it fits into existing workflows; ease of use for clinicians; how much training is required.
Transparency & explainability: Especially with diagnostic tools; ability to see how decisions/suggestions are made.
Support & maintenance: What vendor support is available; capacity for updates; how errors are handled.

Wrapping up

There is a wide spectrum of AI tools for healthcare, ranging from diagnostic / imaging solutions (MONAI, BioMorph) to documentation helpers (Heidi Health, DAX Copilot), to administrative claim-processing (Counterforce Health), to knowledge support (OpenEvidence).

For healthcare professionals, using the best AI tools for healthcare means selecting tools not just for innovation, but for reliability, privacy, integration, and evidence.