AI Tools for Healthcare Professionals: A Practical Guide

AI That Gives Clinicians Time Back

Healthcare professionals spend up to 50% of their time on documentation and administrative tasks. AI tools are finally mature enough to help — reducing documentation burden while maintaining the accuracy that patient care demands.

Clinical Documentation

1. Nabla

Best for: Ambient clinical documentation. Nabla listens to patient-doctor conversations and generates structured SOAP notes automatically. It integrates with major EHR systems (Epic, Cerner, athenahealth). Clinicians report saving 2+ hours per day.

2. Suki AI

Best for: Voice-first documentation. Suki is a voice-powered assistant that generates clinical notes as you speak naturally. It learns each clinician's style and medical specialty terminology. Works hands-free during examinations.

Clinical Decision Support

3. Glass Health

Best for: Differential diagnosis. Enter patient symptoms and Glass generates a differential diagnosis with supporting evidence. It's a thinking partner, not a replacement — surfacing possibilities that clinicians can evaluate.

4. Infermedica

Best for: Patient triage. Infermedica's symptom checker helps patients and call centres assess urgency and route to appropriate care. It reduces unnecessary ER visits while ensuring serious conditions aren't missed.

Precision Medicine

5. Tempus AI

Best for: Oncology treatment planning. Tempus analyses genomic and clinical data to identify personalised treatment options. It connects clinicians with relevant clinical trials and provides data-driven treatment recommendations.

Patient Engagement

6. Cogito Health

Best for: Behavioural health monitoring. Cogito analyses voice patterns during calls to detect signs of distress, depression, or anxiety. It alerts care teams and supports proactive mental health intervention.

Critical Considerations

• Regulatory compliance: Verify FDA clearance, CE marking, and HIPAA/GDPR compliance
• Clinical validation: Demand peer-reviewed evidence of safety and efficacy
• Liability: Understand the liability model — AI as decision support, not decision maker
• Integration: Ensure compatibility with your EHR/EMR system
• Patient consent: Be transparent about AI use with patients

Browse all healthcare tools on our AI Healthcare page.