The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.

Artificial intelligence (AI) improved skin cancer diagnostic accuracy when used in collaboration with human clinical checks, an international study including University of Queensland researchers has found. The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.

Automated classifiers may be better than physicians when it comes to diagnosing pigmented skin lesions, but human supervision is still needed, researchers found. All machine-learning algorithms reached a mean of 2.01 more correct diagnoses than did all human readers (17.91 vs 19.92; P 0.0001), reported Harald Kittler, MD, of the Medical University of Vienna in Austria, and colleagues in The Lancet Oncology. When comparing the top three machine learning algorithms with 27 human experts with over a decade of experience, the algorithms still outperformed the experts (18.78 vs 25.43; P 0.0001), the investigators found. Notably, the difference between the top three algorithms and experts was significantly lower for images that were gathered from centers that did not contribute images for the training set when compared with other image sets, although there was human under-performance once again (11.4% vs 3.6%; P 0.0001), the researchers wrote. In this study, machine-learning classifiers performed better than experienced human readers in the diagnosis of pigmented skin lesions, suggesting that machine learning should have a more important role in clinical practice, the investigators said.

Created by FotoFinder Systems, Moleanalyzer pro is a portal that lets physicians confirm their skin cancer diagnosis using evaluation techniques, combining specialist expertise with AI and including the option of receiving a second opinion from international skin cancer experts. FotoFinder Systems Global Brand Director Kathrin Niemela told HITNA that the technology aims to aid skin cancer diagnoses. According to the Cancer Council Australia, every year skin cancers account for around 80 per cent of all newly diagnosed cancers in Australia, with GPs seeing more than a million patients per year for skin cancer. In addition, the Australian Government identified that there were 14,320 new cases of melanoma skin cancer diagnosed in 2018, accounting for 10.4 per cent of all new cancer cases diagnosed. "The earlier skin cancer is detected, the better the prognosis. The leisure behaviour of sunbathing in many parts of the world makes early detection of skin cancer more important worldwide," Niemela said.

A novel imaging approach using artificial intelligence was associated with improved detection of parameters associated with melanoma, according to results presented at the International Conference on Image Analysis and Recognition. The researchers suggested that a number of quantitative imaging approaches to dealing with melanoma have focused largely on skin lesions using "hand crafted imaging features." The current study employed "machine-learning" software, which records abstract quantitative features on images and can model physiological traits of the patients, according to study background. The two features of melanoma that were assessed in the analysis were eumelanin and hemoglobin concentrations observed on dermal imaging. The researchers created a non-linear random forest regression model culled from the images.

By now, everyone and their grandparents are talking about machine learning and AI. Unfortunately, many people have been questioning whether all this effort is worth it, and some are worried about future job losses. Just yesterday at an event, someone said to me, "The world invested so much money into image recognition just so we could recognize a cat. My response was, "Well, a machine recognizing a cat is the first step toward a machine detecting and recognizing a tumor." If you cut through the hype and use a strategic goal, machine learning can offer real-world value.

By now, everyone and their grandparents are talking about machine learning and AI. Unfortunately, many people have been questioning whether all this effort is worth it, and some are worried about future job losses. Just yesterday at an event, someone said to me, "The world invested so much money into image recognition just so we could recognize a cat. My response was, "Well, a machine recognizing a cat is the first step toward a machine detecting and recognizing a tumor." If you cut through the hype and use a strategic goal, machine learning can offer real-world value.

By now, everyone and their grandparents are talking about machine learning and AI. Unfortunately, lately, many people have been questioning whether all this effort is worth it and some are worried about future job losses. Just yesterday at an event, someone asked me, "The world invested so much money into image recognition just so we could recognize a cat. My response was, "Well, a machine recognizing a cat is the first step towards a machine detecting and recognizing a tumor." If you cut through the hype and use a strategic goal, machine learning can offer real-world value.

As many jobs are disappearing to automation, the latest profession to also start seeing the future may be dermatology. Stanford University researchers have developed a deep convolutional neural network, an artificial intelligence technique for building a knowledge set, to learn how to spot suspect cancer lesions. Today this process is manual and prone to errors of subjectivity. Dermatologists simply look through a dermatoscope and judge based on their education and experience. The Stanford system was given 130,000 images of skin lesions simply labeled with previously established diagnoses that included more than 2,000 diseases.