Exploring how AI can transform healthcare

Integrating artificial intelligence (AI) into healthcare has the potential to revolutionize the industry by supporting and enhancing clinical decision-making by clinicians. AI can also significantly improve healthcare’s administrative activities, streamlining processes and unlocking immense cost savings. But while AI can bring numerous benefits to patient care, it is crucial to approach its implementation with ethical considerations and clinical appropriateness at the forefront. This report explores the application and benefits of ethical and clinically appropriate AI in healthcare and delves into the transformative effects AI can have on cost-saving efforts and patient care, particularly in the administrative functions among payers and providers.

AI and its subcategories

There are many subcategories under the broad umbrella of artificial intelligence that vary in purpose and application. The following four areas play a pivotal role in the application of AI in healthcare: structured machine learning, unstructured machine learning, natural language processing (NLP), and generative AI. Structured and unstructured machine learning differ by the types of data they process and the types of conclusions they help healthcare practitioners draw.

Structured machine learning can use data like height, weight, diagnoses, treatment plans and procedures to predict how patients may respond to certain treatments, or it can use patient inflow data, bed occupancy data and staff availability data to predict how hospitals can best allocate resources.

Unstructured machine learning uses more nuanced data like medical images, handwritten medical notes, and discharge summaries to summarize and highlight years of clinical notes or help with medical image-based diagnosis by detecting abnormalities in magnetic resonance images and X-rays.

NLP manages and interprets unstructured data, bridging the gap between unstructured and structured machine learning models and facilitating more efficient data extraction and research.

Generative AI, likely the least adopted and newest application of AI in healthcare, uses frameworks like generative adversarial networks to generate novel data samples that are similar to input data by using two neural networks, the generator and the discriminator, where the generator has the goal to produce data that is indistinguishable from real data and the discriminator has the goal to distinguish between real and fake data produced by the other model. Over time, as the generator network receives feedback from the discriminator network, it becomes highly effective at generating data like the input data. While the use of generative AI in healthcare is still in its early stages, it could ultimately be used to generate more personalized care pathways, to simulate disease progression, or in treatment regimen simulation to better understand how different regimens may affect a patient.