Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

BACKGROUND

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

OBJECTIVE

This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

METHODS

Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

RESULTS

Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

CONCLUSIONS

The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework

Large language models (LLMs) can potentially transform healthcare, particularly in providing the right information to the right provider at the right time in the hospital workflow. This study investigates the integration of LLMs into healthcare, specifically focusing on improving clinical decision support systems (CDSSs) through accurate interpretation of medical guidelines for chronic Hepatitis C Virus infection management. Utilizing OpenAI's GPT-4 Turbo model, we developed a customized LLM framework that incorporates retrieval augmented generation (RAG) and prompt engineering. Our framework involved guideline conversion into the best-structured format that can be efficiently processed by LLMs to provide the most accurate output. An ablation study was conducted to evaluate the impact of different formatting and learning strategies on the LLM's answer generation accuracy. The baseline GPT-4 Turbo model's performance was compared against five experimental setups with increasing levels of complexity: inclusion of in-context guidelines, guideline reformatting, and implementation of few-shot learning. Our primary outcome was the qualitative assessment of accuracy based on expert review, while secondary outcomes included the quantitative measurement of similarity of LLM-generated responses to expert-provided answers using text-similarity scores. The results showed a significant improvement in accuracy from 43 to 99% (p < 0.001), when guidelines were provided as context in a coherent corpus of text and non-text sources were converted into text. In addition, few-shot learning did not seem to improve overall accuracy. The study highlights that structured guideline reformatting and advanced prompt engineering (data quality vs. data quantity) can enhance the efficacy of LLM integrations to CDSSs for guideline delivery.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort

Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers.

Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread, and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing of infected patients. Medical imaging, such as X-ray and computed tomography (CT), combined with the potential of artificial intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their ensemble, using majority voting, have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods-occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT-and using a global technique-neuron activation profiles. The mean micro F1 score of the models for COVID-19 classifications ranged from 0.66 to 0.875, and was 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model.

Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

BACKGROUND

Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice.

OBJECTIVE

This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools.

METHODS

A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed.

RESULTS

Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption.

CONCLUSIONS

The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.

AI and machine learning ethics, law, diversity, and global impact

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.

Visualization design of health detection products based on human-computer interaction experience in intelligent decision support systems

In order to meet the needs of the human-computer interaction experience of health testing products and improve the decision-making efficiency of intelligent decision support systems, we visualized the design of health testing products. We summarized the design methods for the human-computer interaction experience of health testing products, analyzed health testing data visualization requirements in terms of thematic databases, data visualization diagrams, thematic dashboards and knowledge management systems, and introduced the general process of monitoring information visualization. The visual health testing product information display interface is designed to visualize the testing data in three aspects: information architecture, interaction mode and visual language presentation. The visual intelligent decision support system and the visual interface design are combined for the functional design of the visual intelligent decision support system. The experimental part of the study investigates the effectiveness of the visualized health testing product of the intelligent decision support system, using the questionnaire method and health data measurement method to collect results on the interactivity, convenience, health decision accuracy and product satisfaction of the health monitoring product, with the data presented as a percentage system. The experimental results show that the interactivity, convenience and health decision accuracy of the intelligent decision support visual health monitoring product are higher than those of traditional health monitoring products, with interactivity evaluation results above 85% and high satisfaction with product use, indicating that the product can provide new and innovative design ideas in home healthcare.

The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use

Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution.Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume.External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance.

Significance

ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.

Mapping the Landscape of Care Providers' Quality Assurance Approaches for AI in Diagnostic Imaging

The discussion on artificial intelligence (AI) solutions in diagnostic imaging has matured in recent years. The potential value of AI adoption is well established, as are the potential risks associated. Much focus has, rightfully, been on regulatory certification of AI products, with the strong incentive of being an enabling step for the commercial actors. It is, however, becoming evident that regulatory approval is not enough to ensure safe and effective AI usage in the local setting. In other words, care providers need to develop and implement quality assurance (QA) approaches for AI solutions in diagnostic imaging. The domain of AI-specific QA is still in an early development phase. We contribute to this development by describing the current landscape of QA-for-AI approaches in medical imaging, with focus on radiology and pathology. We map the potential quality threats and review the existing QA approaches in relation to those threats. We propose a practical categorization of QA approaches, based on key characteristics corresponding to means, situation, and purpose. The review highlights the heterogeneity of methods and practices relevant for this domain and points to targets for future research efforts.

Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol

INTRODUCTION

Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.

METHODS AND ANALYSIS

This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.

ETHICS AND DISSEMINATION

Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.

The Reasons for Physicians and Pharmacists' Acceptance of Clinical Support Systems in Saudi Arabia

This research aims to identify the technological and non-technological factors influencing user acceptance of the CDSS in a group of healthcare facilities in Saudi Arabia. The study proposes an integrated model that indicates the factors to be considered when designing and evaluating CDSS. This model is developed by integrating factors from the "Fit between Individuals, Task, and Technology" (FITT) framework into the three domains of the human, organization, and technology-fit (HOT-fit) model. The resulting FITT-HOT-fit integrated model was tested using a quantitative approach to evaluate the currently implemented CDSS as a part of Hospital Information System BESTCare 2.0 in the Saudi Ministry of National Guard Health Affairs. For data collection, a survey questionnaire was conducted at all Ministry of National Guard Health Affairs hospitals. Then, the collected survey data were analyzed using Structural Equation Modeling (SEM). This analysis included measurement instrument reliability, discriminant validity, convergent validity, and hypothesis testing. Moreover, a CDSS usage data sample was extracted from the data warehouse to be analyzed as an additional data source. The results of the hypotheses test show that usability, availability, and medical history accessibility are critical factors influencing user acceptance of CDSS. This study provides prudence about healthcare facilities and their higher management to adopt CDSS.

New frontiers in embryo selection

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles grows impressively year over year. Efforts to alleviate infertility using advanced technology are gaining traction rapidly as infertility has an enormous impact on couples and the potential to destabilize entire societies if replacement birthrates are not achieved. Artificial intelligence (AI) technologies, leveraged by the highly advanced assisted reproductive technology (ART) industry, are a promising addition to the armamentarium of tools available to combat global infertility. This review provides a background for current methodologies in embryo selection, which is a manual, time-consuming, and poorly reproducible task. AI has the potential to improve this process (among many others) in both the clinician's office and the IVF laboratory. Embryo selection is evolving through digital methodologies into an automated procedure, with superior reliability and reproducibility, that is likely to result in higher pregnancy rates for patients. There is an emerging body of data demonstrating the utility of AI applications in multiple areas in the IVF laboratory. AI platforms have been developed to evaluate individual embryologist performance; to provide quality assurance for culture systems; to correlate embryologist's assessments and AI systems; to predict embryo ploidy, implantation, fetal heartbeat, and live birth outcome; and to replace the current "analogue" system of embryo selection with a digital paradigm. AI capability will distinguish high performing, high profit margin, low-cost, and highly successful IVF clinic business models. We think it will become the standard, "new normal" in IVF labs, as rapidly and thoroughly as vitrification, blastocyst culture, and intracytoplasmic sperm injection replaced their predecessor technologies. At the time of this review, the AI technology to automate embryo evaluation and selection has robustly matured, and therefore, it is the main focus of this review.

User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals

Objective

Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX).

Methods

We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment.

Results

The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7  ±  14.1 and 65  ±  26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale.

Conclusions

The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.

Leveraging law and ethics to promote safe and reliable AI/ML in healthcare

Artificial intelligence and machine learning (AI/ML) is poised to disrupt the structure and delivery of healthcare, promising to optimize care clinical care delivery and information management. AI/ML offers potential benefits in healthcare, such as creating novel clinical decision support tools, pattern recognition software, and predictive modeling systems. This raises questions about how AI/ML will impact the physician-patient relationship and the practice of medicine. Effective utilization and reliance on AI/ML also requires that these technologies are safe and reliable. Potential errors could not only pose serious risks to patient safety, but also expose physicians, hospitals, and AI/ML manufacturers to liability. This review describes how the law provides a mechanism to promote safety and reliability of AI/ML systems. On the front end, the Food and Drug Administration (FDA) intends to regulate many AI/ML as medical devices, which corresponds to a set of regulatory requirements prior to product marketing and use. Post-development, a variety of mechanisms in the law provide guardrails for careful deployment into clinical practice that can also incentivize product improvement. This review provides an overview of potential areas of liability arising from AI/ML including malpractice, informed consent, corporate liability, and products liability. Finally, this review summarizes strategies to minimize risk and promote safe and reliable AI/ML.

Assessment of the Effect on Thromboprophylaxis with Multifaceted Quality Improvement Intervention based on Clinical Decision Support System in Hospitalized Patients: A Pilot Study

Background: To explore the feasibility and effectiveness of multifaceted quality improvement intervention based on the clinical decision support system (CDSS) in VTE prophylaxis in hospitalized patients. Methods: A randomized, department-based clinical trial was conducted in the department of respiratory and critical care medicine, orthopedic, and general surgery wards. Patients aged ≥18 years, without VTE in admission, were allocated to the intervention group and received regular care combined with multifaceted quality improvement intervention based on CDSS during hospitalization. VTE prophylaxis rate and the occurrence of hospital-associated VTE events were analyzed as primary and secondary outcomes. Results: A total of 3644 eligible residents were enrolled in this trial. With the implementation of the multifaceted quality improvement intervention based on the CDSS, the VTE prophylaxis rate of the intervention group increased from 22.93% to 34.56% (p < 0.001), and the incidence of HA-VTE events increased from 0.49% to 1.00% (p = 0.366). In the nonintervention group, the VTE prophylaxis rate increased from 24.49% to 27.90% (p = 0.091), and the incidence of HA-VTE events increased from 0.47% to 2.02% (p = 0.001). Conclusions: Multifaceted quality improvement intervention based on the CDSS strategy is feasible and expected to facilitate implementation of the recommended VTE prophylaxis strategies and reduce the incidence of HA-VTE in hospital. However, it is necessary to conduct more multicenter clinical trials in the future to provide more reliable real-world evidence.

Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making

Background

Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision‐making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision‐making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it.

Objective

To explore (1) the extent to which patient preferences are taken into consideration in non‐small‐cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM.

Design

Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations.

Setting and Participants

NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics.

Results

We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations.

Conclusion

Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized.

Patient or Public Contribution

This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled ‘My Best Treatment’ that improves patient‐centeredness and the lung cancer patient pathway in the Netherlands.

The Mediating Role of the IS/IT Auditor for Quality Assurance in the Selection of Accounting Software Packages for SMEs in Developing Economies

This research designed and tested a model for the selection adoption, customization, and implementation of the best accounting software among the lot that has flooded the software market recently in developing countries. The model assessed the mediating role of the Information Systems (IS) Auditor to mitigate the risks of selecting the wrong packaged software to the barest minimum in developing countries. The researchers analyzed the association that exists between eight variables — Basic Functionality (BF), General Determinants (GD), Package Features (PF), Customization Capability (CC), Financial Reporting Capabilities (FRC) and the IS Auditor (IS/IT Auditor. The study was cross-sectional which purposively sampled professional experts from selected SMEs across Ghana to evaluate the eight variables in the model. 260 experts participated in the assessment. The Delphi Technique was used to reach a consensus on the applicability of the variables in the model. The variable measurement items in the model were subjected to three rounds of scrutiny as required by the Delphi Technique. Multiple regression analysis was also computed in Smart PLS 3 to determine the correlation among the variables in making an appropriate CASP selection decision. The internal consistency and reliability of the dataset were estimated using Statistical Package for Social Sciences.

The results point to a positive association between all the variables assessed. The values for BF, GM, PF, CC, FRC and IS/IT Auditor, were 0.509, 0.621, 0.511, 0.632, 0.507 0.454. 0.596 and 0.590, respectively. This is substantiated by the average Kendall’s [Formula: see text] of 0.17. Chi-square ([Formula: see text] values ranging between 134.55 and 497.28 for all the three rounds of evaluation and intra-class correlation values ranging from 0.56 to 0.95 affirmed that consensus was achieved. It means that none of these variables could be included in the CASPs decision without the IS/IT Auditor mediating to check its strength in appropriate CASPs selection. Cronbach’s [Formula: see text] values ranging between 0.719 and 0.924 were computed to determine the internal consistency and reliability of the datasets. The average variance extracted (AVE) for each variable was higher than 0.5 which implies that the convergent validity of the construct is still adequate. The triangulations of these variables using different statistical values have proven the robustness and resilience with which IS/IT Auditor could assist in selecting the best CASPs package with lesser risks of implementation failures in developing economies. The model is expected to help SMEs in developing countries select the best CASPs packages among the lots for successful implementation. It is expected that it will serve as a wake-up call for policymakers to adopt a roadmap to enable SMEs to make the best CASPs buying decision. It is also envisaged that academic institutions will also fill the software adoption illiteracy gap by training more graduates in hands-on computerized accounting to increase selection and implementation awareness.

Will the EU Medical Device Regulation help to improve the safety and performance of medical AI devices?

Concerns have been raised over the quality of evidence on the performance of medical artificial intelligence devices, including devices that are already on the market in the USA and Europe. Recently, the Medical Device Regulation, which aims to set high standards of safety and quality, has become applicable in the European Union. The aim of this article is to discuss whether, and how, the Medical Device Regulation will help improve the safety and performance of medical artificial intelligence devices entering the market. The Medical Device Regulation introduces new rules for risk classification of the devices, which will result in more devices subjected to a higher degree of scrutiny before entering the market; more stringent requirements on clinical evaluation, including the requirement for appraisal of clinical data; new requirements for post-market surveillance, which may help spot early on any new, unexpected side effects and risks of the devices; and requirements for notified bodies, including for expertise of the personnel and consideration of relevant best practice documents. The guidance of the Medical Device Coordination Group on clinical evaluation of medical device software and the MEDDEV2.7 guideline on clinical evaluation also attend to some of the problems identified in studies on medical artificial intelligence devices. The Medical Device Regulation will likely help improve the safety and performance of the medical artificial intelligence devices on the European market. The impact of the Regulation, however, is also dependent on its adequate enforcement by the European Union member states.

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.

Artificial intelligence in the embryology laboratory: a review

The goal of an IVF cycle is a healthy live-born baby. Despite the many advances in the field of assisted reproductive technologies, accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. To overcome this, it is common practice to transfer more than one embryo, potentially resulting in high-risk multiple pregnancies. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal. Utilization of artificial intelligence (AI) has become increasingly popular in the medical field and is increasingly being leveraged in the embryology laboratory to help improve IVF outcomes. Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review summarizes recent AI advancements in the embryology laboratory.

Machine learning in medicine: It has arrived, let's embrace it

Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient therapeutic setpoints. This commentary discusses a recent application leveraging machine learning to predict one-year patient survival following orthotopic heart transplantation. This modeling approach has significant implications in terms of improving clinical decision-making, patient counseling, and ultimately organ allocation and has been shown to significantly outperform pre-existing algorithms. This commentary also discusses how adoption and advancement of this modeling approach in the future can provide increased personalization of patient care. The continued expansion of information systems and growth of electronic patient data sources in health care will continue to pave the way for increased use and adoption of data science in medicine. Personalized medicine has been a long-standing goal of the healthcare community and with machine learning and AI now being continually incorporated into clinical settings and practice, this technology is well on the pathway to make a considerable impact to greatly improve patient care in the near future.

Quality-in-use characteristics for clinical decision support system assessment

BACKGROUND

Clinical decision support systems (CDSSs) are developed to support healthcare practitioners with decision-making about therapy and diagnosis' confirmation, among others. Although there are many advantages of using CDSSs, there are still many challenges in their adoption. Therefore, it is essential to ensure the quality of the system, so that it can be used confidently and securely.

OBJECTIVE

This study aims to propose a set of (sub)characteristics which should be considered in evaluating the quality-in-use of CDSSs, based on the ISO/IEC 25010 standard and on existing literature.

METHODS

We reviewed the existing literature on CDSS assessment and presented a list of quality characteristics evaluated.

RESULTS

Ten quality characteristics and 56 sub-characteristics were identified and selected from the literature, in which usability was evaluated the most. An example of a scenario has been presented to illustrate our assessment approach of satisfaction and efficiency as important quality-in-use characteristics to be applied in the evaluation of a CDSS.

CONCLUSION

The proposed approach will contribute in bridging the gap between the quality of CDSSs and their adoption.

From clinical decision support to clinical reasoning support systems

Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. Clinical decision making involves a reasoning process in which clinicians combine different types of information into a coherent and adequate 'picture of the patient' that enables them to draw explainable and justifiable conclusions for which they bear epistemological responsibility. Therefore, we suggest that it is more appropriate to think of a CDSS as clinical reasoning support systems (CRSS). Developing CRSS that support clinicians' reasoning process therefore requires that: (a) CRSSs are developed on the basis of relevant and well-processed data; and (b) the system facilitates an interaction with the clinician. Therefore, medical experts must collaborate closely with AI experts developing the CRSS. In addition, responsible use of an CRSS requires that the data generated by the CRSS is empirically justified through an empirical link with the individual patient. In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.

Emerging role of artificial intelligence in nuclear medicine

The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Evaluating predictive models in reproductive medicine

Predictive modeling has become a distinct subdiscipline of reproductive medicine, and researchers and clinicians are just learning the skills and expertise to evaluate artificial intelligence (AI) studies. Diagnostic tests and model predictions are subject to evaluation. Their use offers potential for both harm and benefit in terms of diagnosis, treatment, and prognosis. The performance of AI models and their potential clinical utility hinge on the quality and size of the databases used, the types and distribution of data, and the particular AI method applied. Additionally, when images are involved, the method of capturing, preprocessing, and treatment and accurate labeling of images becomes an important component of AI modeling. Inconsistent image treatment or inaccurate labeling of images can lead to an inconsistent database, resulting in poor AI accuracy. We discuss the critical appraisal of AI models in reproductive medicine and convey the importance of transparency and standardization in reporting AI models so that the risk of bias and the potential clinical utility of AI can be assessed.

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

Procurement, commissioning and QA of AI based solutions: An MPE's perspective on introducing AI in clinical practice

PURPOSE

In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA.

MATERIAL AND METHODS

The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods.

RESULTS

Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as self-learning AI tools would enter the medical practice.

DISCUSSION

MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.

Building Resilient Health Systems: Patient Safety during COVID-19 and Lessons for the Future

Background:

The COVID-19 pandemic has profoundly impacted the country’s health systems and diminished its capability to provide safe and effective healthcare. This article attempts to review patient safety issues during COVID-19 pandemic in India, and derive lessons from national and international experiences to inform policy actions for building a ‘resilient health system’.

Methods:

Systematic review of existing published articles, government and media reports was undertaken. Online databases were searched using key terms related to patient safety during COVID-19 and health systems resilience. Seventy-three papers were included dependent on their relevance to research objectives.

Findings:

Patient safety was impacted during COVID-19, owing to sub-optimal infection prevention and control measures coupled with reduced access to essential health services. This was largely due to inadequate infrastructure, human and material resources resulting from chronic underinvestment in public health systems, paucity of reliable data for evidence-based actions and limited leadership and regulatory capacity.

Conclusions:

India’s health systems were found ill prepared to tackle large-scale pandemic, which has major implications for patient safety. The shortcomings observed in the COVID-19 response must be rectified and comprehensive health sector reforms should be initiated for building agile and resilient health systems that can withstand future pandemics.

Artificial Intelligence in Medical Sensors for Clinical Decisions

Due to the limited ability of conventional methods and the limited perspective of human diagnostics, patients are often diagnosed incorrectly or at a late stage as their disease condition progresses. They may then undergo unnecessary treatments due to inaccurate diagnoses. In this Perspective, we offer a brief overview on the integration of nanotechnology-based medical sensors and artificial intelligence (AI) for advanced clinical decision support systems to help decision-makers and healthcare systems improve how they approach information, insights, and the surrounding contexts, as well as to promote the uptake of personalized medicine on an individualized basis. Relying on these milestones, wearable sensing devices could enable interactive and evolving clinical decisions that could be used for evidence-based analysis and recommendations as well as for personalized monitoring of disease progress and treatment. We present and discuss the ongoing challenges and future opportunities associated with AI-enabled medical sensors in clinical decisions.

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R² and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D₉₈, D₉₅, D₁ and uniformity index (UI). The OAR dose was compared using the maximum dose, V₃₀ and V₄₀. The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D_98,D_95,D₁ and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R², MAE, the maximum dose, V₃₀ and V₄₀ (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.