Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent solution due to their success in numerous applications aimed at enhancing the medical field, including addressing the shortage of echography technicians. However, the limited availability of medical data presents a significant barrier to applying ML in cardiology, particularly regarding US images of the heart. This paper addresses this challenge by introducing the first open graded dataset for Cardiac Assessment and ClassificaTion of UltraSound (CACTUS), which is available online. This dataset contains images obtained from scanning a CAE Blue Phantom and representing various heart views and different quality levels, exceeding the conventional cardiac views typically found in the literature. Additionally, the paper introduces a Deep Learning (DL) framework consisting of two main components. The first component classifies cardiac US images based on the heart view using a Convolutional Neural Network (CNN). The second component uses Transfer Learning (TL) to fine-tune the knowledge from the first component and create a model for grading and assessing cardiac images. The framework demonstrates high performance in both classification and grading, achieving up to 99.43% accuracy and as low as 0.3067 error, respectively. To showcase its robustness, the framework is further fine-tuned using new images representing additional cardiac views and compared to several other state-of-the-art architectures. The framework's outcomes and performance in handling real-time scans were also assessed using a questionnaire answered by cardiac experts.

AI models are undoubtedly solving a lot of real world problems, be it in any field. Building a machine learning model that is genuinely accurate during real world applications and not only during training and testing is what matters. Using state-of-the-art techniques for developing models might not suffice to develop a model that is trained on irregular, biased, or unreliable data. Data shows that nearly a quarter of companies reported up to 50% of AI project failure rate. In another study, nearly 78% of AI or ML projects stall at some stage before deployment, and 81% of the process of training AI with data is more difficult than they expected.

"We do not want schizophrenia researchers knowing a lot about software engineering," said Amy Winecoff, data scientist and Princeton's Centre for IT Policy. Research asserts that a basic understanding of machine learning and other software engineering principles might be a desirable trait for medical practitioners, but these skills should not come at the expense of expertise in domain knowledge. Many new startups and enterprises sell their products boasting about incorporating AI/ML techniques in the development. Though this is an issue in the developer and business market, the bigger worry is misapplied AI/ML algorithms in the field of science and healthcare as it causes real world consequences. Sayash Kapoor and Arvind Narayanan of Princeton University published a research paper--Leakage and the Reproducibility Crisis in ML-based Science, pointing out the problem of "data leakage" in various researches using pools of data to train and test their development's performance.

Chronic pain is a pervasive disorder which is often very disabling and is associated with comorbidities such as depression and anxiety. Neuropathic Pain (NP) is a common sub-type which is often caused due to nerve damage and has a known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is described as musculoskeletal, diffuse pain that is widespread through the body. The pathophysiology of FM is poorly understood, making it very hard to diagnose. Standard medications and treatments for FM and NP differ from one another and if misdiagnosed it can cause an increase in symptom severity. To overcome this difficulty, we propose a novel framework, PainPoints, which accurately detects the sub-type of pain and generates clinical notes via summarizing the patient interviews. Specifically, PainPoints makes use of large language models to perform sentence-level classification of the text obtained from interviews of FM and NP patients with a reliable AUC of 0.83. Using a sufficiency-based interpretability approach, we explain how the fine-tuned model accurately picks up on the nuances that patients use to describe their pain. Finally, we generate summaries of these interviews via expert interventions by introducing a novel facet-based approach. PainPoints thus enables practitioners to add/drop facets and generate a custom summary based on the notion of "facet-coverage" which is also introduced in this work.

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. Such systems would facilitate the traceability of care and would free clinicians' time. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxSLU. It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.

Founded in 2020, WellAI, an AI health-tech company, is the developer of scientifically and technologically advanced medical applications. WellAI's engineers, fresh off the development of a COVID-19 research tool (presented at the IFCC annual conference) leveraged their expertise into developing an advanced clinical diagnostic tool (triage solution) for physicians, caregivers, and employees/individuals. The company is the developer of the Digital Health Triage Assistant, WellAI for Medical Providers, and the Adaptive AI Diagnostic Engine. It also provides custom solutions. The AI Diagnostic Engine has uniquely assimilated 30 million medical studies and has the ability to diagnose, with 83% average accuracy, more than 500 health conditions including pediatric specific conditions using simple spoken language in less than 1 minute.

Artificial Intelligence can do a lot of things. But, did you know that now AI could be used for the analysis of urine samples collected in a contact-less manner? Yes, a healthcare startup Olive Diagnostics has launched an AI-based urine analysis device that works in sync with the IoT sensors. The Olive WatchOS is a self-diagnosis AI tool for urine analysis. This tool connects to an AI-based real-time urine analysis device that mounts to any toilet.

The Healthcare sector is among the largest and most critical service sectors, globally. Recent events like the Covid-19 pandemic have furthered the challenge to handle medical emergencies with contemplative capacity and infrastructure. Within the healthcare domain, healthcare equipment supply and usage have come under sharp focus during the pandemic. The sector continues to grow at a fast pace and will record a 20.1% CAGR of surge; plus, it is estimated to surpass $662 billion by 2026. Countries like the US spend a major chunk of their GDP on healthcare.

Prime Minister Yoshihide Suga's administration has made digitalization a top priority in an effort to transform the nation's economy and society, but as far as the medical care sector is concerned, steps toward that goal are not off to a smooth start. Beginning in March, the government started rolling out My Number cards embedded with IC chips that can double as health care insurance cards. The Health, Labor and Welfare Ministry says use of the new cards, which all Japan residents can apply for, would simplify administrative procedures at hospitals and streamline applications for tax deductions for medical expenses. But the rate of uptake for the cards is low at around 25% and, due to the pandemic, many health care providers are not in a rush to install the facial recognition systems used to scan My Number cards. In order to use their My Number cards as a health insurance card, users must apply for the card and register online, while health care providers need to install proper card-reading equipment and overhaul their computer systems.

A pandemic came with a lot of unknowns. For example, it was being said that those with underlying conditions are more vulnerable to the coronavirus and must be more cautious. However, we heard that the coronavirus affects young people with no known underlying conditions. This shows many unknowns exist. This is where artificial intelligence or AI can enter to help us find some of our answers if we use it correctly. The underlying conditions include almost everything such as heart diseases, hypertension, diabetes, and chronic respiratory diseases.