In this special guest feature, Akshay Sharma, Executive Vice President of Artificial Intelligence (AI) at Sharecare, highlights advancements and impact of federated AI and edge computing for the healthcare sector as it ensures data privacy and expands the breadth of individual, organizational, and clinical knowledge. Sharma joined Sharecare in 2021 as part of its acquisition of doc.ai, the Silicon Valley-based company that accelerated digital transformation in healthcare. Sharma previously held various leadership positions including CTO, and vice president of engineering, a role in which he developed several key technologies that power mobile-based privacy products in healthcare. In addition to his role at Sharecare, Sharma serves as CTO of TEDxSanFrancisco and also is involved in initiatives to decentralize clinical trials. Sharma holds bachelor's degrees in engineering and engineering in information science from Visvesvaraya Technological University.
The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.
Missing values exist in nearly all clinical studies because data for a variable or question are not collected or not available. Inadequate handling of missing values can lead to biased results and loss of statistical power in analysis. Existing models usually do not consider privacy concerns or do not utilise the inherent correlations across multiple features to impute the missing values. In healthcare applications, we are usually confronted with high dimensional and sometimes small sample size datasets that need more effective augmentation or imputation techniques. Besides, imputation and augmentation processes are traditionally conducted individually. However, imputing missing values and augmenting data can significantly improve generalisation and avoid bias in machine learning models. A Bayesian approach to impute missing values and creating augmented samples in high dimensional healthcare data is proposed in this work. We propose folded Hamiltonian Monte Carlo (F-HMC) with Bayesian inference as a more practical approach to process the cross-dimensional relations by applying a random walk and Hamiltonian dynamics to adapt posterior distribution and generate large-scale samples. The proposed method is applied to a cancer symptom assessment dataset and confirmed to enrich the quality of data in precision, accuracy, recall, F1 score, and propensity metric.
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, censorship, language, online safety, health, and employment to study the positive and negative effects of artificial intelligence on queer communities. These issues underscore the need for new directions in fairness research that take into account a multiplicity of considerations, from privacy preservation, context sensitivity and process fairness, to an awareness of sociotechnical impact and the increasingly important role of inclusive and participatory research processes. Most current approaches for algorithmic fairness assume that the target characteristics for fairness--frequently, race and legal gender--can be observed or recorded. Sexual orientation and gender identity are prototypical instances of unobserved characteristics, which are frequently missing, unknown or fundamentally unmeasurable. This paper highlights the importance of developing new approaches for algorithmic fairness that break away from the prevailing assumption of observed characteristics.
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user's monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user's personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.
A multi-nation study finds that many of us consider biohacking exciting, but fears concerning hacking and privacy remain. Human augmentation can describe many things. Hearing aids, pacemakers, and prosthetics are already in use, but in the future, we could be using the term for implants that improve cognitive abilities; chips that connect us to our smart devices, or bionic eyes that can restore lost sight, and more. When it comes to future applications, countries worldwide are pushing ahead with the development of new technologies which could result in enhancements to the human body. For example, Japan has recently set $1 billion on the table for researchers willing to pursue everything from human augmentation to longevity, due to the need to tackle an aging workforce and shrinking population.
Advances in China's standard of living provide more people with access to healthcare. Nonetheless, with life expectancies now averaging 76.5 years, medical costs are on the rise. And while the number of top-tier hospitals throughout the country has more than doubled, the annual number of outpatient visits increased almost fourfold during that same period. Improving patient outcomes now relies on the use of new technologies such as real-time analytics, facial recognition, and the IoT. Innovation enables more people to get better access to healthcare information and advice without going to a hospital or waiting to see a doctor. It can also reduce the strain on overburdened medical personnel and resources by automating collection, transmission, and storage of healthcare data used in patient records.
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.
Just over two weeks after an unprecedented hack led to the compromise of the Twitter accounts of Bill Gates, Elon Musk, Barack Obama, and dozens more, authorities have charged three men in connection with the incident. The alleged "mastermind" is a 17-year-old from Tampa, who will be tried as an adult. There are still plenty of details outstanding about how they might have pulled it off, but court documents show how a trail of bitcoin and IP addresses led investigators to the alleged hackers. A Garmin ransomware hack disrupted more than just workouts during a days-long outage; security researchers see it as part of a troubling trend of "big game hunting" among ransomware groups. In other alarming trends, hackers are breaking into news sites to publish misinformation through their content management systems, giving them an air of legitimacy.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.