Years of medical training are required to correctly diagnose diseases. Diagnostics is still a time-consuming and difficult process. The demand for expertise in many disciplines vastly outnumbers the supply. This puts doctors under duress and frequently causes life-saving patient diagnoses to be delayed.
Machine Learning algorithms, particularly Deep Learning algorithms, have lately made significant advancements in autonomously identifying diseases, reducing the cost and accessibility of diagnostics.
You will almost certainly interface with a medical artificial intelligence (AI) system in the next few years. The same AI that powers self-driving cars, home voice assistants, and self-tagging photo galleries is rapidly advancing in the field of health care, with the first medical AI systems already being deployed in clinics.
How do machines learn to diagnose?
Deep learning, a branch of computer science that learns from examples to interpret complicated types of data, is the technology underpinning these advancements. Unlike prior generations of AI, these systems can sense the environment through sight, sound, and the written word in the same way that humans do.
While most individuals take these abilities for granted, they are crucial to human expertise in fields such as medicine. Many medical activities are now solved by artificial intelligence thanks to deep learning, which gives computers these capacities.
Researchers have discovered computer systems that can diagnose diabetic eye illness, skin cancer, and arrhythmias at least as effectively as human doctors in the previous 12 months. These three scenarios depict three different ways in which patients will interact with medical AI in the future.
The first of these three approaches is the most common, and it is used when specialized equipment is required to make a diagnosis. You will schedule a test appointment, visit the clinic, and obtain a report. The patient experience will not be affected by the report being written by a computer.
Diabetic eye disease in Google This technique is shown by artificial intelligence (AI). It was taught to recognise the leaky, weak blood vessels that develop in the rear of the eye in patients with poorly controlled diabetes, and it is now being tested on real patients in various Indian hospitals.
Current smartphone apps that claim to diagnose disease are unlikely to be accurate, albeit one is on the way.
Because many diagnostic procedures do not require any particular equipment, the second approach of interfacing with medical AI will be the most innovative. Stanford researchers are already working on a smartphone app for their skin cancer detector, which is as accurate as dermatologists.
People will soon be able to take selfies of their skin lesions and have their imperfections examined on the spot. This AI is leading the race to be the first app to accurately assess your health without the involvement of a human doctor.
The third mode of communication is in the middle. While an electrocardiogram (ECG) is required to identify cardiac rhythms, these sensors can be integrated into inexpensive wearable equipment and connected to a smartphone. A patient may wear a monitor every day, recording every heartbeat, and just see their doctor once in a while to discuss the results. If something catastrophic happens and the rhythm abruptly changes, the patient and their doctor may be contacted right away.
Soon, more advanced AI diagnoses will be available.
Machine Learning is only just getting started in diagnostics; more ambitious systems combine many data sources (CT, MRI, genomes and proteomics, patient data, and even handwritten files) to analyze a disease or its course.
Doctors will not be replaced by AI anytime soon.
It’s doubtful that AI will completely replace doctors. Instead, AI technologies will alert experts to potentially cancerous tumors or risky cardiac patterns, allowing doctors to concentrate on the interpretation of those signals.
Artificial intelligence is already assisting us in diagnosing diseases, developing drugs, personalizing treatments, and even editing genes.
But this is only the start. The more we digitize and integrate our medical data, the more AI can assist us in identifying useful patterns — patterns that can be used to make correct, cost-effective judgments in complicated analytical procedures.