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Artificial Intelligence in Healthcare

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Wikipedia defines artificial intelligence in healthcare as the use of complex algorithms and software to emulate human cognition in the analysis, interpretation and comprehension of complicated medical and healthcare data. This "emulation" is done in less time and at a fraction of the cost. Artificial intelligence in healthcare was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026. Reinventing and reinvigorating healthcare through the use of artificial intelligence is happening predominantly through assisting in better diagnosis, better processes, drug development and robot-assisted surgery. In 2015 misdiagnosing illness and medical error accounted for 10% of all U.S. deaths.


Machine Learning: Decision Trees

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This blog covers another interesting machine learning algorithm called Decision Trees and it's mathematical implementation. At every point in our life, we make some decisions to proceed further. Similarly, this machine learning algorithm also makes the same decisions on the dataset provided and figures out the best splitting or decision at each step to improve the accuracy and make better decisions. This, in turn, helps in giving valuable results. A decision tree is a machine learning algorithm which represents a hierarchical division of dataset to form a tree based on certain parameters.


Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients

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Background/aims Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. Methods Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. Results Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. Conclusion The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.


Split a Decision Tree

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Decision trees are simple to implement and equally easy to interpret. And decision trees are idea for machine learning newcomers as well! If you are unsure about even one of these questions, you've come to the right place! Decision Tree is a powerful machine learning algorithm that also serves as the building block for other widely used and complicated machine learning algorithms like Random Forest, XGBoost, and LightGBM. You can imagine why it's important to learn about this topic!


Human-Artificial intelligence collaborations best for skin cancer diagnosis

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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 histologic diagnosis of CNS tumors with machine learning

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A new mass discovered in the CNS is a common reason for referral to a neurosurgeon. CNS masses are typically discovered on MRI or computed tomography (CT) scans after a patient presents with new neurologic symptoms. Presenting symptoms depend on the location of the tumor and can include headaches, seizures, difficulty expressing or comprehending language, weakness affecting extremities, sensory changes, bowel or bladder dysfunction, gait and balance changes, vision changes, hearing loss and endocrine dysfunction. A mass in the CNS has a broad differential diagnosis, including tumor, infection, inflammatory or demyelinating process, infarct, hemorrhage, vascular malformation and radiation treatment effect. The most likely diagnoses can be narrowed based on patient demographics, medical history, imaging characteristics and adjunctive laboratory studies. However, accurate histopathologic interpretation of tissue obtained at the time of surgery is frequently required to make a diagnosis and guide intraoperative decision making. Over half of CNS tumors in adults are metastases from systemic cancer originating elsewhere in the body [1]. An estimated 9.6% of adults with lung cancer, melanoma, breast cancer, renal cell carcinoma and colorectal cancer have brain metastases [2].


Human-Artificial intelligence collaborations best for skin cancer diagnosis

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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.



Do Decision Trees need Feature Scaling?

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Machine Learning algorithms have always been on the path towards evolution since its inception. Today the domain has come a long way from mathematical modelling to ensemble modelling and more. This evolution has seen more robust and SOTA models which is almost bridging the gap between potentials capabilities of human and AI. Ensemble modelling has given us one of those SOTA model XGBoost. Recently I happened to participate in a Machine Learning Hiring Challenge where the problem statement was a classification problem.


The use of artificial intelligence in medicine is growing rapidly – IAM Network

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Everyday use of artificial intelligence for health diagnosis could still be years away, but the field is robust right now. "We still have a lot of unknowns in terms of generalizing and validation of these systems before we can start using them as standard of care," Dr. Matthew Hanna, a pathologist at Memorial Sloan Kettering Cancer Center in New York City, told United Press International earlier this month. On the one hand, this is not surprising: The history of artificial intelligence (AI) is a history of overcommitment and underdelivery in real-world "production" environments. But on closer inspection, AI is highly useful in medicine as opposed to other domains and will rapidly increase in usage. The UPI article highlights people's desire to see a human doctor and not trusting a machine's subtleties as a principal factor in their choosing a person rather than the AI. Additionally, it points to the additional long-term testing needed before autonomous AI diagnostic systems can be widely installed.