Shlomo Berkovsky

• PhD
• Macquarie University

Background Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these...

Immunotherapy has emerged as a prominent approach in melanoma treatment, however a substantial number of patients do not respond effectively. This highlights the critical need to accurately predict immunotherapy responses for designing personalised treatment strategies. Current practices rely predominantly on clinical data and the expertise of onco...

Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and exploratory interactions. Yet, the factors shaping user interaction preferences remain underexplored. In this within-subjects study (\(N = 139\)), participants experienced two sc...

Conversational Recommender Systems (CRSs) are receiving growing research attention across domains, yet their user experience (UX) evaluation remains limited. Existing reviews largely overlook empirical UX studies, particularly in adaptive and large language model (LLM)-based CRSs. To address this gap, we conducted a systematic review following PRIS...

This mixed-methods study evaluated clinicians’ user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying levels of user supervision (low, medium, high), focusing on workflow improvements, safety, and accep...

Multiplex immunofluorescence (mIF) imaging plays a crucial role in studying multiple biomarkers and their interactions within the tumour microenvironment. However, acquiring mIF images that include all desired biomarkers presents significant challenges due to the need for specialised equipment and costly reagents, which increase technical complexit...

We assessed the performance of large language models’ summarizing clinical dialogues using computational metrics and human evaluations. The comparison was done between automatically generated and human-produced summaries. We conducted an exploratory evaluation of five language models: one general summarisation model, one fine-tuned for general dial...

In this special issue, we invite comprehensive surveys that rigorously review and analyze core research topics of Interactive Intelligent Technologies (IIT), aiming to establish a structured understanding of the state of the art in this rapidly evolving field. By systematically reviewing and synthesizing the latest research and methodologies, these...

Background The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models. Methods A machine learning approach was applied to predict anxiety disorder r...

Voice assistants (VAs) are increasingly integrated into everyday activities and tasks, raising novel challenges for users and researchers. One emergent research direction concerns proactive VAs, who can initiate interaction without direct user input, offering unique benefits including efficiency and natural interaction. Yet, there is a lack of revi...

The past decade has witnessed an explosive growth in the development and use of artificial intelligence (AI) across diverse fields [...]

Objective cough sound evaluation is useful in the diagnosis and management of respiratory diseases. However, the performance of cough sound analysis models can degrade in the presence of background noises common in everyday environments. This brings forward the need for cough sound denoising. This work utilizes a method for denoising cough sound re...

Background: Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how the...

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-inst...

Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine lea...

Medical image classifiers often suffer from the imbalanced class distribution of datasets. For example, among the 7 classes in the ISIC2018 skin lesion detection dataset, over 67% of the instances belong to melanocytic nevus while only 1% belong to dermatofibroma. Contrastive feature learning has been shown to achieve promising results in enhancing...

General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it r...

The sound of cough is an important indicator of the condition of the respiratory system. Automatic cough sound evaluation can aid the diagnosis of respiratory diseases. Large crowdsourced cough sound datasets have recently been used by several groups around the world to develop cough classification models. However, not all recordings in these datas...

Deep learning has shown its effectiveness in histopathology image analysis, such as pathology detection and classification. However, stain colour variation in Hematoxylin and Eosin (H&E) stained histopathology images poses challenges in effectively training deep learning-based algorithms. To alleviate this problem, stain normalisation methods have...

Purpose To assess current evidence comparing the impact of available coronary interventions in frail patients aged 75 years or older with different subtypes of acute coronary syndrome (ACS) on health outcomes. Methods Scopus, Embase and PubMed were systematically searched in May 2022 for studies comparing outcomes between coronary interventions in...

Immersive virtual reality (iVR) has gained considerable attention recently with increasing affordability and accessibility of the hardware. iVR applications for older adults present tremendous potential for diverse interventions and innovations. The iVR literature, however, provides a limited understanding of guiding design considerations and evalu...

Recent research in behavioral decision making demonstrates the advantages of using eye-tracking to surface insights into users’ underlying cognitive processes. Personality, according to psychology definition, accounts for individual differences in our enduring emotional, interpersonal, experiential, attitudinal, and motivational styles. In recommen...

Fundus images have been widely used in routine examinations of ophthalmic diseases. For some diseases, the pathological changes mainly occur around the optic disc area; therefore, detection and segmentation of the optic disc are critical pre-processing steps in fundus image analysis. Current machine learning based optic disc segmentation methods ty...

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infec...

Anxiety disorders are common among youth, posing risks to physical and mental health development. Early screening can help identify such disorders and pave the way for preventative treatment. To this end, the Youth Online Diagnostic Assessment (YODA) tool was developed and deployed to predict youth disorders using online screening questionnaires fi...

Hematoxylin and Eosin (H&E) stained histopathology images provide important clues for diagnostic and prognostic assessment of diseases. However, similar tissues can be stained with inconsistent colours which significantly hinder the diagnostic process and training of deep learning models. Various Generative Adversarial Network (GAN) based stain nor...

Speech summarization techniques take human speech as input and then output an abridged version as text or speech. Speech summarization has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps close to 2 decades of speech sum...

Objective: We conduct a first large-scale analysis of mobile health (mHealth) apps available on Google Play with the goal of providing a comprehensive view of mHealth apps' security features and gauging the associated risks for mHealth users and their data. Materials and methods: We designed an app collection platform that discovered and downloa...

Smartphone technology has drastically improved over the past decade. These improvements have seen the creation of specialized health applications, which offer consumers a range of health-related activities such as tracking and checking symptoms of health conditions or diseases through their smartphones. We term these applications as Symptom Checkin...

Background and objective: Pertussis (whooping cough), a respiratory tract infection, is a significant cause of morbidity and mortality in children. The classic presentation of pertussis includes paroxysmal coughs followed by a high-pitched intake of air that sounds like a whoop. Although these respiratory sounds can be useful in making a diagnosis...

Purpose: Sleep apnea causes heart rate variability (HRV). HRV can be detected from the electrocardiography (ECG) signal and descriptors of HRV during sleep have been shown to be useful predictors of sleep apnea. In this work, we study the use of raw ECG signal and deep one-dimensional residual neural network (1-D ResNet) for end-to-end sleep apnea...

Objectives To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy. Design Cross sectional study Setting Health related apps developed for the Android mobile platf...

The current agenda in health personalisation research mainly revolves around supporting lifestyle and wellbeing. Personalised recommendations for patients and consumers have been explored for areas like physical activity, food intake, mental support, and health information consumption. Strikingly little attention has been paid to personalised medic...

Audio signal classification finds various applications in detecting and monitoring health conditions in healthcare. Convolutional neural networks (CNN) have produced state-of-the-art results in image classification and are being increasingly used in other tasks, including signal classification. However, audio signal classification using CNN present...

Purpose The estimation of brain volumetric measurements based on Synthetic MRI (SyMRI) is easy and fast, however, the consistency of brain volumetric and morphologic measurements based on SyMRI and 3D T1WI should be further addressed. The current study evaluated the impact of spatial resolution on brain volumetric and morphologic measurements using...

BACKGROUND Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. OBJECTIVE This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information...

Medical practitioners need to understand the critical features of ECG beats to diagnose and identify cardiovascular conditions accurately. This would be greatly facilitated by identifying the significant features of frequency components in temporal ECG wave-forms using computational methods. In this study, we have proposed a novel ECG beat classifi...

An object can be consisting of various attributes, such as illuminance, appearance, shape, orientation, etc. Separately extract these attributes has enormous value in visual effects modeling, attribute-specific retrieval and recognition. Essentially, these attributes can be fairly abstract and thus need labels to extract. However, sometimes the lab...

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases...

Background: Coronavirus disease 2019 (COVID-19) has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, so that resources can be mobilized and treatment can be escalated. Objective: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based...

BACKGROUND COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. OBJECTIVE This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. METHODS...

Objective: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and methods: Co-design workshops with ge...

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literat...

Objectives This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Materials and Methods Clinical data, including demographics, signs, symptoms, comorbidities and blood test results and chest CT scans of 346 patients from two hospitals in the Hubei province, C...

Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infec...

The use of feature extraction and selection from EEG signals has shown to be useful in the detection of epileptic seizure segments. However, these traditional methods have more recently been surpassed by deep learning techniques, forgoing the need for complex feature engineering. This work aims to extend the conventional approach of epileptic seizu...

Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionna...

Feature extraction from ECG-derived heart rate variability signal has shown to be useful in classifying sleep apnea. In earlier works, time-domain features, frequency-domain features, and a combination of the two have been used with classifiers such as logistic regression and support vector machines. However, more recently, deep learning techniques...

Exercising has various health benefits and it has become an integral part of the contemporary lifestyle. However, some workouts are complex and require a trainer to demonstrate their steps. Thus, there are various workout video tutorials available online. Having access to these, people are able to independently learn to perform these workouts by im...

Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image class...

A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intra-transaction dependency and inter-transaction dependency. Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current tr...

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematox...

Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly ans...

BACKGROUND The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (i.e., pseudoprogression) is of paramount importance in the management of glioma patients. OBJECTIVE This research aims to develop a deep-learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on...

Background The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. Objective This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on rou...

Conversational systems are inherently disadvantaged when indicating either what capabilities they have or the state they are in. The notion of habitability, the appropriate balancing in design between the language people use and the language a system can accept, emerged out of these early difficulties with conversational systems. This literature re...

Background Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. Objective This paper empirically analyses whether text in medical discharge reports follow Zipf’s law, a commonly assumed statistical property of language where word frequency follows a discrete power-law distribution. Meth...

Background: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks o...

Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization h...

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognitio...

Phishing emails are a widespread cybersecurity attack method. Their breadth and depth have been on the rise as they target individuals and organisations with increased sophistication. In particular, social engineering in phishing focuses on human vulnerabilities by exploiting established psychological and behavioural cues to increase the credibilit...

BACKGROUND Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely-used examples include voice-activated systems like Apple Siri, Google Assistant, Amazon Alexa, or Microsoft Cortana. The use of CAs in healthcare has been on the rise, but concerns about their potential safety risks often re...

BACKGROUND The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. OBJECTIVE The goal of this systematic review was to understand the ways in which personalization has...

Personality is an established domain of research in psychology, and individual differences in various traits are linked to a variety of real-life outcomes and behaviours. Personality detection is an intricate task that typically requires humans to fill out lengthy questionnaires assessing specific personality traits. The outcomes of this, however,...

In the human-machine collaboration context, understanding the reason behind each human decision is critical for interpreting the performance of the human-machine team. Via an experimental study of a system with varied levels of accuracy, we describe how human trust interplays with system performance, human perception and decisions. It is revealed t...

User system trust is critical to the uptake of recommendations, and several factors of trust have been identified and compared. In this paper we present a cross-cultural, crowdsourced study examining user perceptions of nine factors of trust and link the observed differences to trust development processes and cultural dimensions. While some factors...

Open data initiatives and policies have triggered a dramatic increase in the volume of available research data. This, in turn, has brought to the fore the challenge of helping users to discover relevant datasets. Research data repositories support data search primarily through keyword search and faceted navigation. However, these mechanisms may sui...

Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system’s output serves as one of the inputs for the users’ decision making processes. In this chapter, we study the dynamics of explicit and implicit user trust in a simulated automated qu...

Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiv...

Phishing is an especially challenging cyber security threat as it does not attack computer systems, but targets the user who works on that system by relying on the vulnerability of their decision-making ability. Phishing attacks can be used to gather sensitive information from victims and can have devastating impact if they are successful in deceiv...

It is important for recommender system designers and service providers to learn about ways to generate accurate recommendations while at the same time respecting the privacy of their users. In this tutorial, we analyze common privacy risks imposed by recommender systems, survey privacy-enhanced recommendation techniques, and discuss implications fo...

Customer behavior modeling is important for businesses in order to understand, attract and retain customers. It is critical that the models are able to track the dynamics of customer behavior over time. We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentati...

Weather affects our mood and behavior, and through them, many aspects of our life. When it is sunny, people become happier and smile, but when it rains, some get depressed. Despite this evidence and the abundance of weather data, weather has mostly been overlooked in the machine learning and data science research. This work shows how causal analysi...

Physiological responses contain rich affective information even when humans are not expressing any external signs. In this paper, we investigate the use of the Blood Volume Pulse (BVP) signals for indexing cognitive load. An experiment, which introduced cognitive load as a secondary task in a decision making context was conducted in the study. BVP...

Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, p...

Trust is a key factor affecting the way people rely on automated systems. On the other hand, system performance has comprehensive implications on a user's trust variations. This paper examines systems of varied levels of accuracy, in order to reveal the relationship between system performance, a user's trust and reliance on the system. In particula...

How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces an...

Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evidence and the abundance of data, weather has mostly been overlooked in the machine learning and data science research. This work presents a causal analysis of how weather a...