what doctor specialties will get automated?

doctor specialties automation

Which medical specialties will be replaced by robots/AI in the future?

Physicians are not really at greater risk of being automated in the near term, but a lot of the work physicians do is, and that’s good for everyone.


It’s difficult to predict exactly which specialties will be fully automated in the future, as the pace of technological progress is constantly changing. However, some medical specialties are where automation and artificial intelligence (AI) is already starting to play a role and are likely to become more prevalent in the coming years. These include:

  • Radiology: AI is already being used in radiology to assist with the interpretation of medical images, such as x-rays, CT scans, and MRIs.
  • Pathology: Automated systems are being developed to analyze pathology slides, helping to speed up the diagnostic process and improve accuracy.
  • Dermatology: AI is being used to diagnose skin conditions by analyzing images of skin lesions.
  • Ophthalmology: Automated systems are being developed to assist with the diagnosis of eye diseases and conditions, such as age-related macular degeneration and glaucoma.


We won’t see a large replacement of radiologists soon, but a lot of the easier cases will be screened by AI and algorithms in the next decade, so we’ll see somewhat less demand.

It’s important to note that while automation and AI are becoming more prevalent in these specialties, they are not likely to replace doctors and other medical professionals entirely. Instead, they are likely to augment and support their work, helping to improve the accuracy and speed of diagnoses and freeing up time for doctors to focus on other aspects of patient care.

Radiology is a medical specialty that involves the use of imaging technologies, such as X-rays, CT scans, MRI, PET, and ultrasound, to diagnose and treat diseases and injuries. In recent years, the field of radiology has seen the integration of artificial intelligence (AI) and machine learning algorithms to assist radiologists in their work.

For example, AI algorithms can be used to analyze medical images and highlight areas of interest that may require further examination by a radiologist. This can help radiologists to work more efficiently and accurately, and may improve the speed and accuracy of diagnoses. In some cases, AI algorithms are even capable of making preliminary diagnoses on their own.

However, it’s important to note that AI in radiology is still in its early stages, and that radiologists will continue to play a critical role in the diagnosis and treatment of medical conditions. While AI has the potential to improve the accuracy and efficiency of radiology, it is not yet advanced enough to replace radiologists entirely. Rather, AI is likely to be used to augment the work of radiologists and support their decision-making.