Artificial Intelligence - Featured - Healthcare - May 23, 2023

Title: AI in Healthcare: The Revolution Continues

Three years ago, a review found that AI matched human expertise in diagnosing medical conditions based on images, triggering excitement about the technology's potential in healthcare. Now, AI's impact has grown even more significant and varied.

Artificial intelligence (AI) is increasingly impacting healthcare, promising a future where everyone has access to high-quality care at an affordable price. From assisting physicians in diagnosing diseases to predicting patient outcomes, AI is revolutionizing the way healthcare is delivered.

Three years ago, a review found that AI matched human expertise in diagnosing medical conditions based on images, triggering excitement about the technology’s potential in healthcare. Now, AI’s impact has grown even more significant and varied.

AI is now critical in improving data flow and processing both structured and unstructured data, speeding up data analysis by identifying patterns and generating insights that might elude manual discovery. Machine learning (ML), a subset of AI, helps in structuring and indexing this information, enabling healthcare professionals to search and query unstructured data.

With the aid of ML and natural language processing (NLP), healthcare organizations are now able to decipher the meaning of clinical data. The Children’s Hospital of Philadelphia, for example, has employed AWS AI services to share and integrate genomic, clinical, and imaging data. This has empowered researchers to cross-analyze diseases and make new discoveries.

At the Fred Hutchinson Cancer Center in Seattle, NLP was used in Amazon Comprehend Medical to review large amounts of unstructured clinical record data, aiding in the swift matching of patients with clinical cancer studies.

AI has also been instrumental in diagnosing and managing kidney disease and predicting disease trajectories. Dr. Peter Kotanko of the Renal Research Institute (RRI) shared how AI and ML are used to assess images from radiology or histopathology for diagnosis. RRI has also utilized deep learning to analyze images and predict patient outcomes including hospitalization and the likelihood of having COVID-19.

As for future trends, we can expect to see expanded use of virtual assistants for symptom checking and triage, improvements in automated scheduling, and increased use of wearable data combined with omics data for differentiating patient phenotypes. Stricter AI regulation is also anticipated as the FDA decides which medical devices to recognize.

However, as AI continues to permeate healthcare, it’s crucial to properly incorporate AI solutions into workflows to avoid complications and to ensure optimal efficiency and effectiveness.

Healthcare’s transformation through AI is promising, but it’s a journey. AI can aid in making healthcare more affordable, but the transformation is a process that requires continuous learning, improvement, and adaptation. As AI’s capabilities grow, so does our ability to provide equal access to high-quality, affordable healthcare for all.

However, some areas need further research. For instance, how the use of AI in radiology and other medical imaging techniques has advanced since 2020 remains unclear. But what we do know is that the potential of AI in healthcare is enormous and it’s only getting started.