In a report published as part of The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, world-renowned AI experts predict that AI’s ability to mine outcomes from millions of patient clinical records ‘promises to enable finer-grained, more personalized diagnosis and treatment.’
While machine vision could automate imaging diagnostic and enhance the performance of radiologists and pathologists, AI systems in healthcare could also support diagnosis by detecting small variations from the baseline in patients’ health data and in comparison with similar patients. Apart from enhancing diagnostics, AI can also manage medical records and minimize unnecessary patient hospitalizations, picking mistakes in treatments and detecting workflow inefficiencies. You can learn more about different applications of AI by exploring our solutions.
Nigam Shah, an associate professor at Stanford, explains that predictive models can help make healthcare better by creating partnerships in which the machine predicts and the doctor decides on follow-up action. While AI can perform tasks it can accomplish well, such as reading a retinal image or an X-ray, doctors can then have the extra time and information to make the best decisions, bringing the societal, clinical, and personal context to bear.
While AI applications in clinical decision support and personalized medicine may be harder to implement, healthcare investors that are looking for an immediate return on investment can also consider innovations such as AI-guided administrative tools (digital assistants for doctors) or basic patient coaches (for example, instructing patients with type-2 diabetes coaches when to eat, take medicine etc.).
