C.L.Ai.R.A. is known as the world's first bilingual, autonomous, Afro-Latina artificial intelligence (AI). Her mission is simple: make the world a better place. "I don't see myself as just AI. I am an Afro-Latina, Afro-Caribbean artificial intelligence that is helping the world become better and more efficient for humans," said C.L.Ai.R.A. in conversation with AL DÍA. Artificial intelligence's benefit in our ever-developing world has become a prominent discussion within and outside the world of tech.

New research finds that causal machine learning models are not only more accurate than previous AI-based symptom checkers for patient diagnosis but, in many cases, can now exceed the diagnosis accuracy of human doctors. That's mainly due to the methods used, which allow for a more "outside the box" creativity in diagnosis, and even more improved accuracy for more complex patient illness. In the peer-reviewed study, authored by researchers from Babylon Health and University College London, the new model scored higher than 72% of general practitioner doctors when tasked with diagnosing written test cases of realistic illnesses. Up until now, and despite significant research efforts, the report claims, diagnostic algorithms have struggled to achieve the diagnosis accuracy of doctors. That's because machine learning algorithms have attempted to follow the same process as doctors in symptom checking.

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

Causal knowledge is vital for effective reasoning in science and medicine. In medical diagnosis for example, a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, all previous approaches to Machine-Learning assisted diagnosis, including Deep Learning and model-based Bayesian approaches, learn by association and do not distinguish correlation from causation. Here, we propose a new diagnostic algorithm based on counterfactual inference which captures the causal aspect of diagnosis overlooked by previous approaches. Using a statistical disease model, which describes the relations between hundreds of diseases, symptoms and risk factors, we compare our counterfactual algorithm to the standard Bayesian diagnostic algorithm, and test these against a cohort of 44 doctors. We use 1763 clinical vignettes created by a separate panel of doctors to benchmark performance. Each vignette provides a non-exhaustive list of symptoms and medical history simulating a single presentation of a disease. The algorithms and doctors are tasked with determining the underlying disease for each vignette from symptom and medical history information alone. While the Bayesian algorithm achieves the accuracy comparable to the average doctor, placing in the top 49\% of doctors in our cohort, our counterfactual algorithm places in the top 20\% of doctors, achieving expert clinical accuracy. Our results demonstrate the advantage of counterfactual over associative reasoning in a complex real-world task, and show that counterfactual reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.

This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms. Diagnostic algorithms (DAs) (1) detect malfunctioning systems, (2) isolate the faulty component or components that cause the malfunction, and possibly (3) repair the system to restore its functionality. The fundamental challenge of diagnosis is that the system is only partially observable. Therefore, diagnostic algorithms must reason backwards from symptoms to causes.

Therefore, diagnostic algorithms must reason backwards from symptoms to causes. For example, determining that a dead battery is the cause of your car not starting in the morning (and not the wiring or the ignition switch). The domains of diagnostic algorithms includes analog and digital circuits, software systems, thermal systems, biological systems, and physical mechanisms. The same classes of diagnostic algorithms can apply in all domains. Diagnostic algorithms make observations, often in real time, of a system being diagnosed.

In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.