The most useful and accurate AI models are also more complex, and the more complex a model is, the more challenging it is to comprehend and trust. Why did it make that prediction? AI is not infallible, and it increasingly operates in an opaque way. This severely limits the adoption of advanced AI models in critical settings. The goal of Explainable AI (XAI) is to develop techniques to help users better understand and trust AI models.
Use of a deep-learning convolutional neural network (CNN) -- a form of artificial intelligence -- can help reveal patterns on chest computed tomography (CT) scans that identify smokers at high long-term risk for lung cancer well beyond the Centers for Medicare & Medicaid Services (CMS) criteria for lung screening eligibility, according to the results of an analysis published in the Annals of Internal Medicine. Investigators sought to create and validate a CNN -- that is, the CXR-LC model -- with the ability to predict long-term incident lung cancer via the use of data typically available in a patient's electronic medical record, including chest radiographs, sex, age, and current smoking status. The CXR-LC model was developed in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, which included to total of 41,856 patients. The final CXR-LC model was validated in additional smokers from the PLCO study (n 5615; 12-year follow-up) and National Lung Screening Trial (NLST) heavy smokers (n 5493; 6-year follow-up). There were more current smokers (50.4% vs 20.2%, respectively) and higher mean pack-years (55.7 vs 35.4,
On September 18th The Lancet Digital Health released an article called " Artificial intelligence in COVID-19 drug repurposing" (written by Yadi Zhou, PhD, Prof Fei Wang, PhD, Prof Jian Tang, PhD, Prof Ruth Nussinov, PhD, Prof Feixiong Cheng, PhD) in the article it explained how artificial intelligence is being used for drug reproposing,which is where an already existing is drug used to fight novel diseases such as COVID-19. Artificial intelligence is being used to speed up this process, with the exponential growth in computing power, memory storage and a plethora of data its only right for the medical sector to use this to speed up a process that help fight against the worlds latest threat that is COVID-19. So how is artificial intelligence being used to speed up this process, well the article explains how artificial intelligence used for extracting hidden patterns and evidence from biomedical data, which otherwise would have been done manually saving a considerable amount of time. In connection to the medical sector, artificial intelligence in medicine may be racially biased. Just like explained above artificial intelligence has transformed the healthcare and has really cut the time on many aspects of medicine such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged in a matter of second which is just incredible, but like all good things it comes with its flaws.
According to GHO (Global Health Observatory (GHO), the high prevalence of a large variety of diseases such as Ischaemic heart disease, stroke, lung cancer disease and lower respiratory infections have remained the top killers during the past decade. The growth in the number of mortalities caused by these disease is due to the very delayed symptoms'detection. Since in the early stages, the symptoms are insignificant and similar to those of benign diseases (e.g. the flu ), and we can only detect the disease at an advanced stage. In addition, The high frequency of improper practices that are harmful to health, the hereditary factors, and the stressful living conditions can increase the death rates. Many researches dealt with these fatal disease, and most of them applied advantage machine learning models to deal with image diagnosis. However the drawback is that imagery permit only to detect disease at a very delayed stage and then patient can hardly be saved. In this Paper we present our new approach "DeepLCP" to predict fatal diseases that threaten people's lives. It's mainly based on raw and heterogeneous data of the concerned (or under-tested) person. "DeepLCP" results of a combination combination of the Natural Language Processing (NLP) and the deep learning paradigm.The experimental results of the proposed model in the case of Lung cancer prediction have approved high accuracy and a low loss data rate during the validation of the disease prediction.
Image classification is a challenging task for the visual content, particularly microscopic images for example histopathological images due to high convolution of inter-intraclass dependencies. The underlying structures are complex and interwoven due to similar structural morphological textures. Figure 1 presents some of the complex textures present in histopathology of images. Deep learning is prevalent due to its ability to learn features directly from the input, providing us a window to avoid arduous feature extraction processes Bengio et al.. One of the key features of deep learning is to discover abstract level features and then deep dive for extracting structural semantics in the feature map.
A deep learning model--a form of artificial intelligence (AI)--was more accurate than the current clinical standard at predicting a person's 12-year risk of developing lung cancer. The model's predictions are based on chest radiograph images (CXRs) and basic demographic data (age, sex, and current smoking status) commonly available in electronic health records (EHRs). The findings are published in Annals of Internal Medicine. Lung cancer screening with chest computed tomography (CT) scans can prevent lung cancer death. However, Medicare's current standard to determine who is eligible for lung cancer screening CT misses most lung cancers. Furthermore, lung cancer screening participation is poor, with an estimated less than 5 percent of screening-eligible persons being screened.
Lung cancer is the number one cancer killer in the United States. It's often found too late and difficult to treat, but now new technology in North Texas, is giving patients the upper hand on the disease and in some cases, it can cure them in less than a day. Whether a dip in the pool or a workout in the gym, life as a retiree for 85-year-old Jere Bone is active and comfortably predictable. What he didn't predict was what doctors told him during a recent visit. "I was getting ready to leave and the doctor said, 'oh, by the way, has anyone mentioned that spot in your lung that looks like a might be a tumor?'" said Bone.
The technological advancements in the global Healthcare industry are hurtling at light speed. As the medical industry is undergoing immense changes, Healthcare OEMs look forward to the growing technological trends to improve all aspects of patient care. Today, Artificial Intelligence (AI) play significant roles in the evolution of the healthcare industry, so much that algorithms can now predict and detect the root cause of a certain disease, making an accurate and timely diagnosis. For example, AI can detect the underlying cause of cancer, which can eventually help pharmaceutical scientists develop new drugs accordingly. In one recent study, published by Healthcare IT News, "Google and medical partners including Northwestern University have unveiled a new AI-based tool that can create a better model of a patient's lung from the CT scan images. This 3-D image gives better predictions about the malignancy of tumors and incorporates learning from previous scans, enabling the AI to help clinicians in spotting lung cancer in earlier stages when it is vastly more treatable".