Artificial intelligence is becoming a part of everyday healthcare in many countries. Hospitals, clinics, and research centers are increasingly using AI tools for tasks that range from reading medical images to helping with paperwork. These tools are designed to support healthcare professionals, not replace them, and many are already part of normal clinical routines.
In this article, we highlight AI tools and platforms already in use across healthcare, aligned with the kinds of use cases modern institutions are adopting.
Administrative and Workflow AI Tools
Healthcare organizations handle large volumes of data, reports, claims, and documentation. AI tools in this category aim to reduce manual work and improve efficiency in administrative processes.
AI Documentation and Scribing
These tools automatically capture clinician–patient interactions and generate structured clinical documentation. Their primary goal is to reduce time spent on note-taking and allow clinicians to focus more on patient care. Many solutions use ambient listening to draft notes in real time for later review.
Example tools:
Tandem Health – AI scribe that captures clinician‑patient conversations and generates clinical notes, structured according to normal documentation formats such as SOAP (a common format that stands for Subjective, Objective, Assessment, Plan). It can also suggest relevant diagnosis and procedure codes.
Freed AI Medical Scribe – a web‑based AI scribe tool that listens to patient visits, transcribes audio, and summarizes clinically relevant information into structured documentation (e.g., SOAP format) that can be copied into any EHR system. It offers a free trial so users can try the tool in a browser before subscribing.
Where used:
- General practice clinics
- Hospital outpatient departments
- Telehealth visits
- Specialty and high‑volume care settings
Current impact:
AI scribes are widely adopted because documentation makes up a large portion of clinicians’ workload. According to industry reports, physicians can spend up to twice as much time on EHR documentation as they spend in direct patient care. Clinicians using these tools report significant reductions in documentation time, less after‑hours note work, improved patient interaction, and higher quality of clinical notes.
Automated Chart Sorting and Routing
AI systems analyse large volumes of medical records, referrals, faxes, and clinical documents, then automatically sort them by priority and route them to the appropriate department or clinician. These tools use machine learning classification to distinguish urgent cases, routine referrals, and administrative documents, and integrate with EHR (Electronic Health Record) systems to place information directly into workflows.
Example tools:
n8n (open-source) – a no‑code workflow automation platform that can be self‑hosted and adapted to classify incoming documents, trigger actions, and route tasks such as referrals or patient requests to the right team.
Diagna – a healthcare‑focused AI workflow tool that handles incoming documents such as referrals, faxes, and authorizations, sorting and directing them into the correct clinical queues with integration to EMR (Electronic Medical Record) systems.
Where used:
- Large outpatient clinics
- Hospital medical records departments
- Multi-specialty healthcare facilities
- Referral management and intake workflows
Current impact:
These systems help shorten turnaround times, reduce administrative workload, and improve how quickly patient cases reach the right clinician, especially in high-volume environments where delays often occur due to manual handling of records.
Data Extraction and De‑identification
Processing medical records and identifying meaningful information is an everyday challenge for health systems. AI tools support structured data extraction from unstructured text and protect patient privacy through de‑identification.
Visual De-identification
AI tools remove PHI (Protected Health Information – personal medical data such as names, dates of birth, and ID numbers) from medical images and documents. This includes both metadata (e.g., DICOM headers) and text embedded directly in images. The goal is to make datasets safe for research, sharing, or AI training without exposing patient identity.
Example tools:
RSNA DICOM Anonymizer – a standalone tool developed by the radiology community that removes or modifies identifiers in DICOM files. It supports batch processing, works offline, and integrates with PACS (Picture Archiving and Communication System – hospital imaging storage system).
Redactor AI – a commercial solution that uses OCR (Optical Character Recognition – extracting text from images) and computer vision to automatically detect and remove PHI from PDFs, scanned documents, and medical images.
Where used:
- Research data preparation
- Sharing datasets between institutions
- Preparing datasets for AI model training
- Radiology systems (PACS) before exporting imaging data
Current impact:
De-identification is required by regulations such as HIPAA (Health Insurance Portability and Accountability Act – US data protection law) and GDPR (General Data Protection Regulation – EU privacy law), which mandate removal of identifiable patient data before sharing.
Text Extraction and NLP from Clinical Notes
AI systems that analyze unstructured clinical text (such as progress notes) extract structured clinical entities (diseases, medications, symptoms) and transform them into usable data.
Example tools:
Apache cTAKES – an open‑source natural language processing tool that parses clinical notes to recognize drugs, diseases, and clinical observations.
Where used:
- Electronic health record (EHR) systems
- Research environments
- Clinical data warehouses
Current impact:
NLP tools significantly speed up data retrieval and reporting, aiding analytics and reducing manual chart review. Structured data also supports analytics and decision support systems across care settings.
Diagnostic and Clinical AI Tools
One of the most widely recognized uses of AI in healthcare today is in clinical support, particularly in interpreting medical images and assisting with diagnosis.
Radiology and Urgent Imaging Triage
AI models analyze imaging studies and flag findings for clinicians. These tools do not replace radiologists but help prioritize urgent cases and reduce turnaround time.
Example tools:
Aidoc – an AI platform that continuously analyzes medical imaging and identifies critical findings (e.g., intracranial hemorrhage, pulmonary embolism) in CT and X‑rays. It is used in hundreds of hospitals and imaging centers worldwide.
Where used:
- Hospital radiology departments
- emergency imaging services
Current impact:
Hospitals using these systems report faster identification of urgent cases and smoother workflows, particularly in high‑volume settings where radiologists face large backlogs.
Research, Trials and Multimodal AI Support
Beyond clinical care, AI assists with research workflows, multimodal analysis, and clinical trial design.
Multimodal Research Frameworks
Frameworks like MONAI (Medical Open Network for AI) provide libraries of deep learning tools optimized for medical imaging tasks and support research and development of new clinical AI models. MONAI has been applied in imaging contexts such as mammography reading and liver lesion analysis, helping reduce wait times and support clinical decisions.
Where used:
- Research institutions
- Academic hospitals
Current impact:
By providing a standardized AI framework, research teams can develop and test clinical models faster and more consistently.
Conclusion
AI is no longer an experimental technology in healthcare—it is already embedded in everyday workflows. While these tools significantly improve efficiency and support clinical decision-making, their success depends on responsible implementation, data privacy, and continued human oversight. As adoption grows, AI will increasingly act as a support layer that enhances, rather than replaces, healthcare professionals.
