Use of AI in treatment of Breast Cancer

Few decades ago medical science not able to find cure of many serious disease. Breast cancer is one of it’s kind and with the passage of time it’s ratio is increase overwhelming. Now with the help of AI scientist and Doctors made huge development to find cure and some possible precautions to prevent from  this.

The world’s largest experiment using AI in medical science field of its kind found that using artificial intelligence for breast cancer screening is useful and safe, can reduce radiologists’ workload by almost half and work result more accurate and reliable.

World Health Organization share a report recently in which they share stats, with more than 2.4 million women getting the disease each year, breast cancer is the most common cancer in the world.

By detecting breast cancer at an earlier, more treatable stage through screening, prognosis can be improved, and mortality can be decreased. According to early findings from a significant study done under the umbrella of WHO, AI screening is about twice as effective as two radiologists working together, does not raise false positives, and reduces workload.

The king Edward medical association journal has published the results of the interim safety survey and after compile results share analysis of the first randomized controlled trial of its kind, which involved more than 86,000 women.

Previous research that looked at whether AI could reliably identify breast cancer in mammograms evaluated scans that had already been examined by clinicians.

The most recent research, however, examined women from Sweden with an average age of 54 and directly compared AI-supported screening with standard care.

The remaining three-quarters of the photos were screened using AI and subsequently interpreted by one or more radiologists, while the other two-thirds of the images were examined by two radiologists.

A total of 244 women (28%) recalled by AI-supported screening were discovered to have cancer, as opposed to 203 women (25%) recalled from routine screening. As a result, 41 more tumors—of which 19 were invasive and 22 were in situ—were found using AI.

The application of AI show almost 100% accurate analysis and results and not in an increase in false positives, which occur when a scan is wrongly classified as abnormal. In both groups, the false-positive rate was 1.6%.

Radiologists in the AI group performed 36,886 fewer screen readings than those in the group receiving normal care, which resulted in a 44% decrease in the effort associated with screen reading, according to the authors.

The results will determine whether AI can decrease the number of interval cancers, which are instances discovered between screenings and typically have a worse prognosis. They will also determine whether using AI in screening is warranted.

However, the reports and data analysis states that “AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, which indicate that the use of AI in mammography screening is safe.”

Dr. Kristina Lang, the lead author from Lund University in Sweden, said: “These encouraging interim safety results should be used to guide new trials and program-based evaluations to address the severe radiologist shortage in many countries. However, they are not recommended on their own to make sure that AI is developed to be implemented in mammography screening.

“There is still more need to learn extra about the effects on patient outcomes and wait for some weeks to observe change in patient recovery cycle, particularly whether integrating radiologists’ knowledge with AI might assist detect interval tumors that are similarly overlooked by traditional screening, as well as the technology’s affordability.

The greatest potential of AI at the moment is that it might relieve radiologists of the load of excessive reading. The majority of mammograms would no longer need to be double read, which would relieve workload pressure and free radiologists to concentrate on more complex diagnostics while reducing patient wait times. This AI-supported screening system does, however, require at least one radiologist to be in charge of detection.”

While applauding the “high quality” research, Stephen Duffy, a professor of cancer screening at Queen Mary University of London who was not involved in the trial, noted that there may be worries that AI-driven improvements in breast cancer diagnosis may lead to the over detection of very benign tumors.

For instance, he pointed out that the paper’s findings showed a rise in the discovery of ductal carcinoma in situ, which is regarded to be perhaps over diagnosed.

The final trial results will eventually establish if AI can help enhance breast cancer screening, according to Dr. Krystyna Temcinaite, director of research communications at the nonprofit Breast Cancer Now.

She stated that in the interim, “urgent issues” with breast screening programmes, such as out-of-date IT systems that eat up valuable staff time and stall improvements, must be addressed.

A representative for the English NHS called the research “very encouraging” and stated that the organization was already looking at how AI may help women receive diagnoses more quickly, identify cancers earlier, and save more lives.

” According to Dr. Katharine Halliday, president of the Royal College of Radiologists, “AI holds enormous promise and could save clinicians time by maximizing our efficiency, supporting our decision-making, and helping identify and prioritise the most urgent cases.”

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