Four examples of AI in healthcare
AI can increase efficiency through automation, such as speech processing, and contribute to faster and better decision-making. Large amounts of data, such as images or gene sequences, can also be compared, analyzed, and checked for discrepancies or anomalies quickly and with great accuracy. Four main themes of how Artificial Intelligence is advancing healthcare also emerged at this year’s DMEA, Europe’s premier Digital Health event:
- Diagnosing Diseases/ Clinical Decision Support (facilitates treatment decisions).
- Radiology/image analysis (simplifies imaging, helps radiologists make more informed clinical decisions)
- Automated coding (systematic documentation of diagnoses and procedures coding according to ICD)
- AI-based speech recognition for clinical documentation/care documentation via voice input
Diagnostics and decision making
Machine learning methods can analyze scans and biopsy images faster and more accurately than what can be achieved manually. Even in gene sequencing for detecting rare genetic defects, the use of ML algorithms produces a better result. AI thus allows physicians to recognize and make findings and decisions for therapy more quickly, which gives affected individuals earlier certainty and the option for treatment.
Our project with the startup gMendel is an excellent example of how machine learning can improve and speed up genetic defect diagnosis and decision-making while reducing costs. The artificial intelligence-based diagnostic test enables optimized disease management with timely, accurate, and clinically relevant diagnoses. We are pleased to have been able to leverage our AI and machine learning expertise to deliver a solution that adds real value to affected individuals.