Artificial intelligence (AI) is transforming healthcare in various ways, from improving data flow and recognizing structured and unstructured data to generating insights and identifying patterns that might elude manual efforts. According to a December 2021 survey by Optum, 85% of healthcare executives have an AI strategy, and almost half of them now use the technology.
AI is playing a significant role in improving healthcare outcomes, such as addressing chronic illness, workforce shortages, and hospital readmissions. For instance, Amazon HealthLake is a service that enables searching and querying of unstructured data, which can help healthcare professionals structure and index unstructured information like X-rays and medical records attached to slides.
Moreover, machine learning (ML) and natural language processing (NLP) help healthcare organizations understand the meaning of clinical data. For example, the Children’s Hospital of Philadelphia used Amazon Comprehend Medical to integrate and facilitate the sharing of genomic, clinical, and imaging data to help researchers cross-analyze diseases, develop new hypotheses, and make discoveries.
Similarly, the Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical to review mountains of unstructured clinical record data at scale to quickly match patients with clinical cancer studies. NLP helped physicians review about 10,000 medical charts per hour to find patients with the right inclusion criteria, removing the “heavy lifting.”
AI is also helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients. Nephrologists and other medical disciplines use AI and ML to assess images from radiology or histopathology, as well as images taken by smartphones to diagnose a patient’s condition.
In summary, AI is transforming healthcare by enabling targeted diagnostics, personalized care, and improved patient outcomes. With the help of ML, NLP, and conversational AI, healthcare professionals can structure and index unstructured data, understand the meaning of clinical data, and generate insights that might elude manual efforts.