If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
AI is a subdomain of Machine Learning (ML). The focus of AI or ML requires math and programming. No-Code options for creating an AI based solution have increased and are in the mainstream within several Microsoft products. No-Code tools provide a graphical interface that provide the same quality solution as scripting in Python. Machine learning is a technique that uses mathematics and statistics to create a model that can predict unknown values.
The food-as-medicine paradigm has gained traction in recent years, partly due to physicians' and clinicians' growing understanding of the importance of using food in chronic illness therapy alongside drugs. Artificial Intelligence (AI) is a game-changing technology that is being used to learn more about individualized diets in the fields of nutrition and wellness. Linear and nonlinear interactions between numerous nutritional elements affecting us have been modeled by experts of AI development company. Food firms and tailored food product services use this approach to create better forecasts and healthy intake. It is accomplished by using deep learning to analyze the genetic information of individuals in order to produce precise forecasts and appropriate food recommendations.
Prof. Yanay Ofran's amazing story about the pursuit of an antibody that will save the world from disease Shlomit Lan and Gali Weinreb Professor Yanay Ofran, founder and CEO of Biolojic Design, a company that develops smart antibodies designed to treat a variety of diseases, is frustrated. "Humanity invests $300 billion each year in drug development, and what do we get? At most, we get a few dozen medications a year, most of which don't solve the problems, and give an additional three weeks of life on average to patients with pancreatic cancer, or manage to inject a medication that to date was given via infusion. Those are the breakthroughs," he says despairingly. But Ofran does not think the pharmaceutical companies are the only culprit. "The drug companies are portrayed as a devil who says, 'I won't cure this because it's not worth my while.' But these companies do have a legal obligation towards their shareholders, not to develop drugs unless there's an economic incentive. The problem, as analyzed by Ofran, is much more complicated and therefore far more difficult to treat. "There are three players sitting around the drug development table: science, regulation and the business world.
AI is a boon to the medical and healthcare industry. Right from diagnostics to surgeries and medical equipment, artificial intelligence is supporting the healing processes of many human lives. The medical device sector is a part of the US$3 trillion healthcare industry in the United States, where researchers and manufacturers are incorporating automation through AI. There are several use cases for AI and automation in the medical device industry. Companies are using machine learning to monitor patients using sensors and automating medicine delivery via connected apps, integrating AI-driven platforms in medical scanning devices to improve the clarity of images and screening, and utilizing IoT to improve patient monitoring and clinical outcomes.
After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time.
Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals1. This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10–21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n = 54) or by physicians (physician arm, n = 54). The results for the primary efficacy measure—the percentage of time spent within the target glucose range (70–180 mg dl−1 (3.9–10.0 mmol l−1))—in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P < 1 × 10−7). The percentage of readings below 54 mg dl−1 (<3.0 mmol l−1) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers. The randomized-controlled trial ADVICE4U demonstrates non-inferiority of an automated AI-based decision support system compared with advice from expert physicians for optimal insulin dosing in youths with type 1 diabetes.
Most people with diabetes need at least two shots of insulin per day, but to to ease this burden, scientists are working on an implantable robot to administer the medication. A team of Italian researchers recently published a study in the journal Science Robotics that outlines a two-component system called PILLSID, which includes an implantable insulin pump that sits in the abdomen area and ingestible magnetic hormone capsules to refill it. When patients need to reload the pump, they swallow a capsule, which is then pulled through the digestive system by magnets inside the insulin device. The device, roughly the size of a flip phone, catches the capsule with a tractable needle, rotates it into a certain position and then extracts the hormone. The capsule continues to move naturally through the digestive track and eventually leaves the body.
This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning with fundamental properties of the Wasserstein metric and the DRO formulation, we explore duality to arrive at tractable formulations and develop finite-sample, as well as asymptotic, performance guarantees. We consider a series of learning problems, including (i) distributionally robust linear regression; (ii) distributionally robust regression with group structure in the predictors; (iii) distributionally robust multi-output regression and multiclass classification, (iv) optimal decision making that combines distributionally robust regression with nearest-neighbor estimation; (v) distributionally robust semi-supervised learning, and (vi) distributionally robust reinforcement learning. A tractable DRO relaxation for each problem is being derived, establishing a connection between robustness and regularization, and obtaining bounds on the prediction and estimation errors of the solution. Beyond theory, we include numerical experiments and case studies using synthetic and real data. The real data experiments are all associated with various health informatics problems, an application area which provided the initial impetus for this work.
Cardiovascular diseases and their associated disorder of heart failure are one of the major death causes globally, being a priority for doctors to detect and predict its onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnosis and treatments. Specifically, explainable AI offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of a prediction model for heart failure survival by using a dataset that comprises 299 patients who suffered heart failure. The model employs a data workflow pipeline able to select the best ensemble tree algorithm as well as the best feature selection technique. Moreover, different post-hoc techniques have been used for the explainability analysis of the model. The paper's main contribution is an explainability-driven approach to select the best prediction model for HF survival based on an accuracy-explainability balance. Therefore, the most balanced explainable prediction model implements an Extra Trees classifier over 5 selected features (follow-up time, serum creatinine, ejection fraction, age and diabetes) out of 12, achieving a balanced-accuracy of 85.1% and 79.5% with cross-validation and new unseen data respectively. The follow-up time is the most influencing feature followed by serum-creatinine and ejection-fraction. The explainable prediction model for HF survival presented in this paper would improve a further adoption of clinical prediction models by providing doctors with intuitions to better understand the reasoning of, usually, black-box AI clinical solutions, and make more reasonable and data-driven decisions.
From the past few years, due to advancements in technologies, the sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age. There are various health impacts of obesity like Diabetes, Heart disease, Blood pressure problems, and many more. Machine learning from the past few years is showing its implications in all expertise like forecasting, healthcare, medical imaging, sentiment analysis, etc. In this work, we aim to provide a framework that uses machine learning algorithms namely, Random Forest, Decision Tree, XGBoost, Extra Trees, and KNN to train models that would help predict obesity levels (Classification), Bodyweight, and fat percentage levels (Regression) using various parameters. We also applied and compared various hyperparameter optimization (HPO) algorithms such as Genetic algorithm, Random Search, Grid Search, Optuna to further improve the accuracy of the models. The website framework contains various other features like making customizable Diet plans, workout plans, and a dashboard to track the progress. The framework is built using the Python Flask. Furthermore, a weighing scale using the Internet of Things (IoT) is also integrated into the framework to track calories and macronutrients from food intake.