Textbook of Digital Health

Companies that apply machine learning to molecular, genomic, and clinical data to guide diagnosis and tailor treatment, most prominently in oncology.

Tools that listen to clinician-patient conversations and automatically draft clinical notes, aiming to reduce the documentation burden on clinicians.

Predictive models embedded in clinical systems that flag patient risk or deterioration, such as early signs of sepsis, ahead of conventional vital-sign scores.

Connected sensors and consumer or medical devices that capture physiological data outside the clinic, from continuous glucose monitors to smart rings and ECG patches.

Software-based, clinically validated interventions that prevent, manage, or treat specific conditions, often delivered through apps and sometimes requiring a prescription.

Companies that build the core electronic health record and health information systems on which hospitals and clinics run.

Companies building large language models and AI agents for clinical question-answering, patient communication, and other generative tasks.

Firms that aggregate, connect, and analyse health data across sources to support population health, research, and operational decision-making.

Companies that apply AI to radiology, pathology, and cardiac imaging to detect findings, triage urgent cases, and quantify disease.

Providers of digital and blended mental-health care and benefits, connecting people to therapy, coaching, and medication management.

Companies automating administrative and payer-facing workflows such as prior authorisation, revenue-cycle management, and patient intake.

Companies delivering clinical care remotely through video, telephone, and asynchronous channels, including direct-to-consumer services.