AI has been rising in popularity and integrated into our daily lives, from self-driving cars to personalized song recommendations. Recently, they have also been integrated into healthcare to answer medical questions and assist patients. But, would you trust AI to make a diagnosis and “be your doctor?”

Types of AI in Healthcare:

• X-rays and MRIs use AI to analyze and interpret medical images

• Machine learning use AI to improve diagnostic accuracy and personalize treatments

• Natural language processing is used to analyze and extract data from clinical notes

• Robotics arms are powered by AI to assist surgeons during operations

Pros of AI in Healthcare:

• Could speed up tasks like analyzing medical images, sorting through patients files, or summarizing notes so doctors have time for other work

• Identify common patterns of a disease, improving early detection 

• Personalizes treatments that would be the most effective for a patient

Cons of AI in Healthcare:

• AI is trained by data created by humans. Since medical data has lacked diversity and the understanding of disparities, AI models can amplify these issues in healthcare. A study showed AI ranked people unfairly based on their race and gender, hence AI would worsen existing disparities

• Most clinical trials consist of white men and lack data about women and people of color, hence some treatments could negatively affect patients

• AI doesn’t clearly explain why it came to a conclusion, making it hard for hospitals to trust their suggestions

AI can make healthcare more precise and faster, but it may cause more harm if it isn’t designed or monitored correctly. The only way to include AI into healthcare is allowing doctors to supervise and work with AI systems to improve healthcare effectively.