Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Although AI practitioners use various methods to reduce representation bias, their effectiveness is often constrained by insufficient domain knowledge in the debiasing process. To address this gap, this paper introduces a set of generic design guidelines for effectively involving domain experts in representation debiasing. We instantiated our proposed guidelines in a healthcare-focused application and evaluated them through a comprehensive mixed-methods user study with 35 healthcare experts. Our findings show that involving domain experts can reduce representation bias without compromising model accuracy. Based on our findings, we also offer recommendations for developers to build robust debiasing systems guided by our generic design guidelines, ensuring more effective inclusion of domain experts in the debiasing process.

In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, the extent to which various global model-centric and data-centric explanations can effectively assist domain experts in detecting and resolving potential data-related issues for the purpose of model improvement has remained largely unexplored. In this technical report, we summarise the key findings of our two user studies. Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations. To empirically investigate these dynamics, we conducted two user studies, comprising quantitative analysis involving a sample size of 70 healthcare experts and qualitative assessments involving 30 healthcare experts. These studies were aimed at illuminating the influence of different explanation types on three key dimensions: trust, understandability, and model improvement. Results show that global model-centric explanations alone are insufficient for effectively guiding users during the intricate process of data configuration. In contrast, data-centric explanations exhibited their potential by enhancing the understanding of system changes that occur post-configuration. However, a combination of both showed the highest level of efficacy for fostering trust, improving understandability, and facilitating model enhancement among healthcare experts. We also present essential implications for developing interactive machine-learning systems driven by explanations. These insights can guide the creation of more effective systems that empower domain experts to harness the full potential of machine learning

Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centric explanations in aiding domain experts to detect and resolve potential data issues for model improvement remains unexplored. This research investigates the influence of data-centric and model-centric global explanations in systems that support healthcare experts in optimising models through automated and manual data configurations. We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts to explore the impact of different explanations on trust, understandability and model improvement. Our results reveal the insufficiency of global model-centric explanations for guiding users during data configuration. Although data-centric explanations enhanced understanding of post-configuration system changes, a hybrid fusion of both explanation types demonstrated the highest effectiveness. Based on our study results, we also present design implications for effective explanation-driven interactive machine-learning systems.

Digital health and mobile health apps have been a hype topic, ever since Apple's App Store began the app-craze in 2008. The initial hype about mHealth has now cooled, which is good news in a way because it shows that mHealth has made the leap from hype to reality. The hype of healthcare apps has since been replaced by other hot topics. So, what is the "next big thing"? We want to have a closer look at chatbots in healthcare. What are these so-called chatbots capable of doing?

The concept of automation in business and non-business functions has undergone more than a few evolutions along the way. The earliest types of automation-related applications could only carry out repetitive tasks such as printing and basic calculations. In a bid to save time and minimize human error, such applications were used by businesses and individuals to automate the tasks that, according to organizations, employees didn't need to waste their energy on. The eventually widespread adoption of IoT, AI and robotics resulted in the growth of cognitive automation to execute more challenging, diverse and multifaceted functions such as supply chain operations, robotic surgery, architecture and construction. The sheer accuracy and consistency of cognitive automation tools powered by AI and robotics allow organizations to evaluate data at lightning-quick speed, predict future trends in consumer demand patterns and formulate robust strategies and frameworks for improved operational efficiency and regulatory compliance.

The growing involvement of technologies such as AI and computer vision in healthcare enables health experts to predict and track the advancement of Alzheimer's disease in patients. The mere possibility of being diagnosed with Alzheimer's is enough to fill patients' minds with a deep sense of foreboding. After all, this is a disease that increasingly limits the functioning of a patient's brain, leading them, eventually, into a perpetually vegetative state of existence. In 2021, one in nine persons in the US aged 65 and older are living with Alzheimer's dementia. The progression of Alzheimer's in a patient is closely linked with their age, and hence, at least for now, there is no known cure for it.

Artificial intelligence's applicability in healthcare settings may not have lived up to corporate ... [ ] and investor hype yet, but AI experts believe we're still in the very early stages The "AI in healthcare: myth versus reality" discussion has been happening for well over a decade. From AI bias and data quality issues to considerable market failures (e.g., the notorious missteps and downfall of IBM's Watson Health unit), the progress and efficacy of AI in healthcare continues to face extreme scrutiny. John Halamka, M.D., M.S., is President of The Mayo Clinic Platform As President of the Mayo Clinic Platform, John Halamka, M.D., M.S., is "not disappointed in the least" about AI's progress in healthcare. "I think of it as a maturation process," he said. But can your three-year-old add a column of numbers?

Stem cell therapy comes with its own set of challenges despite being a revolutionary discovery in modern healthcare. As we know, AI has several applications in the deep and diverse field of healthcare. So, stem cell AI's incredible capabilities can overcome those challenges and truly improve stem cell therapy in hospitals. Stem cell therapy, or regenerative cell therapy, is an operation that attempts to repair or replace diseased, dysfunctional or ruptured tissues in the body using stem cells. Stem cell therapy is an evolution of organ transplantation.

The world is changing rapidly and so does technology. Artificial intelligence which was once just a word of sci-fi is now catching its speed into reality. According to the Globe and Mail " Artificial intelligence research leads to new applications" in reforming every sector starting from education, e-commerce, business, construction, healthcare, etc, thus impacting them to their core. Artificial intelligence in the times of COVID-19 has had a huge impact on the healthcare industry. Thanks to AI, technical advancements are helping medical professionals to treat their patients with ease and accuracy. Moreover, a study by Statista states that the market of AI in the field of healthcare will surge up to $28 million by the year 2025.

With IBM's recent exploration to sell off its health business unit IBM Watson Health, The Wall Street Journal highlighted several issues with AI in healthcare that can hinder tech companies' innovation efforts. Despite spending several billion dollars on acquisitions to scale Watson Health, IBM's health business currently isn't profitable and is looking to sell, according to the Journal. IBM declined to comment on the sale, but offered the following statement to the publication about its successes over the past decade. "This work began nearly 10 years ago, at the beginning of the AI revolution, and we explored groundbreaking space in helping physicians advance healthcare through AI," the company said. "IBM is continuing to evolve the Watson Health business, based on our decade of experience, to meet the needs of patients and physicians."