Evaluating artificial intelligence in medicine: phases of clinical research

Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology

OBJECTIVES

Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety and accuracy. Our objective is to evaluate the diagnostic safety and effectiveness of an AI-powered virtual assistant (VA) designed for the triage of neurological pathologies.

METHODS

The performance of an AI-powered VA for emergency neurological triage was tested. Ten patients over 18 years old with urgent neurological pathologies were selected. In the first stage, 9 neurologists assessed the safety of the VA using their clinical records. In the second stage, the assistant's accuracy when used by patients was evaluated. Finally, VA performance was compared with ChatGPT 3.5 and 4.

RESULTS

In stage 1, neurologists agreed with the VA in 98.5% of the cases for syndromic diagnosis, and in all cases, the definitive diagnosis was among the top 5 differentials. In stage 2, neurologists agreed with all diagnostic parameters and recommendations suggested by the assistant to patients. The average use time was 5.5 minutes (average of 16.5 questions). VA showed superiority over both versions of ChatGPT in all evaluated diagnostic and safety aspects ( P <0.0001). In 57.8% of the evaluations, neurologists rated the VA as "excellent" (suggesting adequate utility).

CONCLUSIONS

In this study, the VA showcased promising diagnostic accuracy and user satisfaction, bolstering confidence in further development. These outcomes encourage proceeding to a comprehensive phase 1/2 trial with 100 patients to thoroughly assess its "real-time" application in emergency neurological triage.

The critical need for an open medical imaging database in Japan: implications for global health and AI development

Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, contrasting with open databases like the US Cancer Imaging Archive and UK Biobank. The 2018 Next Generation Medical Infrastructure Act attempted to address this through new data-sharing frameworks, but implementation has been limited by strict privacy regulations and institutional resistance. This data gap risks compromising AI system performance for Japanese patients and limits global medical AI advancement. The solution lies not in developing individual AI models, but in democratizing access to well-curated Japanese medical imaging data. By implementing privacy-preserving techniques and streamlining regulatory processes, Japan could enhance domestic healthcare outcomes while contributing to more robust global AI models, ultimately reclaiming its position as a leader in medical innovation.

Towards human-AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmonary tuberculosis diagnosis

Objective

Artificial intelligence (AI) technology promises to be a powerful tool in addressing the global health challenges posed by tuberculosis (TB). However, evidence for its real-world impact is lacking, which may hinder safe, responsible adoption. This case study addresses this gap by assessing the technical performance, usability and workflow aspects, and health impact of implementing a commercial AI system (qXR by Qure.ai) to support Australian radiologists in diagnosing pulmonary TB.

Materials and Methods

A retrospective diagnostic accuracy evaluation was conducted to establish the technical performance of qXR in detecting TB compared to a human radiologist and microbiological reference standard. A qualitative human factors assessment was performed to investigate the user experience and clinical decision-making process of radiologists using qXR. A task productivity analysis was completed to quantify how the radiological screening turnaround time is impacted.

Results

qXR displays near-human performance satisfying the World Health Organization's suggested accuracy profile. Radiologists reported high satisfaction with using qXR based on minimal workflow disruptions, respect for their professional autonomy, and limited increases in workload burden despite poor algorithm explainability. qXR delivers considerable productivity gains for normal cases and optimizes resource allocation through redistributing time from normal to abnormal cases.

Discussion and Conclusion

This study provides preliminary evidence of how an AI system with reasonable diagnostic accuracy and a human-centered user experience can meaningfully augment the TB diagnostic workflow. Future research needs to investigate the impact of AI on clinician accuracy, its relationship with efficiency, and best practices for optimizing the impact of clinician-AI collaboration.

Advancements in microsurgery: A comprehensive systematic review of artificial intelligence applications

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instruction. This review explores cutting-edge developments in microsurgery through the lens of AI applications. By analyzing a wide range of studies, this paper highlights AI's transformative role in enhancing microsurgical techniques and decision-making processes. A systematic literature search was conducted using Ovid MEDLINE, Ovid Embase, Web of Science, and PubMed (2005-2023). Extensive data on ML model function and composition, as well as broader study characteristics, were collected from each study. Study quality was assessed across 7 methodological areas of AI research using an adapted methodological index of nonrandomized studies (MINORS) tool. Seventeen studies met the inclusion criteria. ML was used primarily for prognosis (35%), postoperative assessment (29%), and intraoperative assistance/robotic surgery (24%). Only 2 studies were conducted beyond phase 0 of AI research. Fourteen studies included a training group, but only one of these reported both validation and training sets. ML model performance was assessed most frequently using accuracy, specificity, and sensitivity. Scores for the adapted MINORS criteria ranged from 10 to 14 out of 14, with a median of 12. Through collation of all available preclinical and clinical trials, this review suggests the efficacy of ML for various microsurgical applications. Despite this, widespread adoption of this technology remains scarce, currently limited by methodological flaws of individual studies and structural barriers to disruptive technologies. However, with growing evidence supporting its use, microsurgeons should be receptive to implementing ML-incorporated technologies or may risk falling behind other specialties.

Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial

BACKGROUND

Clinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice.

OBJECTIVE

This study aimed to evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI's assistance, both helpful and unhelpful, and the communication of uncertainty of AI-generated results on pharmacists' cognitive interaction with the prototype.

METHODS

In a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using an online eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with black box help without uncertainty information or uncertainty-aware help, which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness.

RESULTS

Pharmacists shifted 19%-26% of their total fixations to AI-generated regions when these were available, suggesting the integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images.

CONCLUSIONS

Unhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in "black box" systems, but showing more information can add a cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians' original workflow.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06795477; https://clinicaltrials.gov/study/NCT06795477.

The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review

BACKGROUND

Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.

OBJECTIVE

This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use.

METHODS

A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis.

RESULTS

Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems.

CONCLUSIONS

The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.

Diagnosing Allergic Contact Dermatitis Using Deep Learning: Single-Arm, Pragmatic Clinical Trial with an Observer Performance Study to Compare Artificial Intelligence Performance with Human Reader Performance

Background: Allergic contact dermatitis is a common, pruritic, debilitating skin disease, affecting at least 20% of the population. Objective: To prospectively validate a computer vision algorithm across all Fitzpatrick skin types. Methods: Each participant was exposed to 10 allergens. The reference criterion was obtained 5 days after initial patch placement by a board-certified dermatologist. The algorithm processed photographs of the test site obtained on Day 5. Human performance in reading the photographs was also evaluated. Results: A total of 206 evaluable participants [mean age 39 years, 66% (136/206) female, and 47% with Fitzpatrick skin types IV-VI] completed testing. Forty-two percent (87/206) of participants experienced 1 or more allergic reaction resulting in a total of 132 allergic reactions. The model provided high discrimination (AUROC 0.86, 95% CI: 0.82-0.90) and specificity (93%, 95% CI: 92%-94%) but with lower sensitivity (58%, 95% CI: 49%-67%). Human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers. There were no serious adverse events. Conclusions: The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample.

Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review

BACKGROUND

The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient.

OBJECTIVE

The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature.

METHODS

We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed.

RESULTS

We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining "digital twin," 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization.

CONCLUSIONS

We propose defining a patient digital twin as "a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions" and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care.

Development and Validation of a Questionnaire to Assess the Radiologists' Views on the Implementation of Artificial Intelligence in Radiology (ATRAI-14)

Introduction: Artificial Intelligence (AI) is becoming an essential part of modern radiology. However, available evidence highlights issues in the real-world applicability of AI tools and mixed radiologists' acceptance. We aimed to develop and validate a questionnaire to evaluate the attitude of radiologists toward radiology AI (ATRAI-14). Materials and Methods: We generated items based on the European Society of Radiology questionnaire. Item reduction yielded 23 items, 12 of which contribute to scoring. The items were allocated into four domains ("Familiarity", "Trust", "Implementation Perspective", and "Hopes and Fears") and a part related to the respondent's demographics and professional background. As a pre-test method, we conducted cognitive interviews with 20 radiologists. Pilot testing with reliability and validity assessment was carried out on a representative sample of 90 respondents. Construct validity was assessed via confirmatory factor analysis (CFA). Results: CFA confirmed the feasibility of four domains structure. ATRAI-14 demonstrated acceptable internal consistency (Cronbach's Alpha 0.78 95%CI [0.68, 0.83]), good test-retest reliability (ICC = 0.89, 95% CI [0.67, 0.96], p-value < 0.05), and acceptable criterion validity (Spearman's rho 0.73, p-value < 0.001). Conclusions: The questionnaire is useful for providing detailed AI acceptance measurements for making management decisions when implementing AI in radiology.

Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology

In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and effects of these biases, which challenge their reliability and widespread applicability in health care. Using a case study, we highlight the complexities involved in addressing these biases from a clinical viewpoint. The goal of this review is to equip researchers and clinicians with the practical knowledge needed to identify, understand, and mitigate these biases, advocating for the creation of AI solutions that are not just technologically sound, but also fair and effective for all patients.

Should the Use of Adaptive Machine Learning Systems in Medicine be Classified as Research?

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called "update problem," which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects.

Is artificial intelligence for medical professionals serving the patients? : Protocol for a systematic review on patient-relevant benefits and harms of algorithmic decision-making

BACKGROUND

Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)-regardless of the clinical issues.

METHODS

Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion.

DISCUSSION

It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results.

SYSTEMATIC REVIEW REGISTRATION

This study is registered within PROSPERO (CRD42023412156).

An international consensus panel on the potential value of Digital Surgery

OBJECTIVES

The use of digital technology in surgery is increasing rapidly, with a wide array of new applications from presurgical planning to postsurgical performance assessment. Understanding the clinical and economic value of these technologies is vital for making appropriate health policy and purchasing decisions. We explore the potential value of digital technologies in surgery and produce expert consensus on how to assess this value.

DESIGN

A modified Delphi and consensus conference approach was adopted. Delphi rounds were used to generate priority topics and consensus statements for discussion.

SETTING AND PARTICIPANTS

An international panel of 14 experts was assembled, representing relevant stakeholder groups: clinicians, health economists, health technology assessment experts, policy-makers and industry.

PRIMARY AND SECONDARY OUTCOME MEASURES

A scoping questionnaire was used to generate research questions to be answered. A second questionnaire was used to rate the importance of these research questions. A final questionnaire was used to generate statements for discussion during three consensus conferences. After discussion, the panel voted on their level of agreement from 1 to 9; where 1=strongly disagree and 9=strongly agree. Consensus was defined as a mean level of agreement of >7.

RESULTS

Four priority topics were identified: (1) how data are used in digital surgery, (2) the existing evidence base for digital surgical technologies, (3) how digital technologies may assist surgical training and education and (4) methods for the assessment of these technologies. Seven consensus statements were generated and refined, with the final level of consensus ranging from 7.1 to 8.6.

CONCLUSION

Potential benefits of digital technologies in surgery include reducing unwarranted variation in surgical practice, increasing access to surgery and reducing health inequalities. Assessments to consider the value of the entire surgical ecosystem holistically are critical, especially as many digital technologies are likely to interact simultaneously in the operating theatre.

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma

The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. This retrospective study incorporated 26,288 USG images from 5444 patients to train YOLOv5 model for FLLs detection and classification of seven different types of FLLs, including hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), focal fatty infiltration, focal fatty sparing (FFS), cyst, hemangioma, and regenerative nodules. AI model performance was assessed for detection and diagnosis of the FLLs on a per-image and per-lesion basis. The AI achieved an overall FLLs detection rate of 84.8% (95%CI:83.3-86.4), with consistent performance for FLLs ≤ 1 cm and > 1 cm. It also exhibited sensitivity and specificity for distinguishing malignant FLLs from other benign FLLs at 97.0% (95%CI:95. 9-98.2) and 97.0% (95%CI:95.9-98.1), respectively. Among specific FLL types, CCA detection rate was at 92.2% (95%CI:88.0-96.4), followed by FFS at 89.7% (95%CI:87.1-92.3), and HCC at 82.3% (95%CI:77.1-87.5). The specificities and NPVs for regenerative nodules were 100% and 99.9% (95%CI:99.8-100.0), respectively. Our AI model can potentially assist physicians in FLLs detection and diagnosis during USG examinations. Further external validation is needed for clinical application.

A checklist for reporting, reading and evaluating Artificial Intelligence Technology Enhanced Learning (AITEL) research in medical education

Advances in Artificial Intelligence (AI) have led to AI systems' being used increasingly in medical education research. Current methods of reporting on the research, however, tend to follow patterns of describing an intervention and reporting on results, with little description of the AI in the system, or the many concerns about the use of AI. In essence, the readers do not actually know anything about the system itself. This paper proposes a checklist for reporting on AI systems, and covers the initial protocols and scoping, modelling and code, algorithm design, training data, testing and validation, usage, comparisons, real-world requirements, results and limitations, and ethical considerations. The aim is to have a systematic reporting process so that readers can have a comprehensive understanding of the AI system that was used in the research.

Clinical Research Informatics: a Decade-in-Review

BACKGROUND

Clinical Research Informatics (CRI) is a subspeciality of biomedical informatics that has substantially matured during the last decade. Advances in CRI have transformed the way clinical research is conducted. In recent years, there has been growing interest in CRI, as reflected by a vast and expanding scientific literature focused on the topic. The main objectives of this review are: 1) to provide an overview of the evolving definition and scope of this biomedical informatics subspecialty over the past 10 years; 2) to highlight major contributions to the field during the past decade; and 3) to provide insights about more recent CRI research trends and perspectives.

METHODS

We adopted a modified thematic review approach focused on understanding the evolution and current status of the CRI field based on literature sources identified through two complementary review processes (AMIA CRI year-in-review/IMIA Yearbook of Medical Informatics) conducted annually during the last decade.

RESULTS

More than 1,500 potentially relevant publications were considered, and 205 sources were included in the final review. The review identified key publications defining the scope of CRI and/or capturing its evolution over time as illustrated by impactful tools and methods in different categories of CRI focus. The review also revealed current topics of interest in CRI and prevailing research trends.

CONCLUSION

This scoping review provides an overview of a decade of research in CRI, highlighting major changes in the core CRI discoveries as well as increasingly impactful methods and tools that have bridged the principles-to-practice gap. Practical CRI solutions as well as examples of CRI-enabled large-scale, multi-organizational and/or multi-national research projects demonstrate the maturity of the field. Despite the progress demonstrated, some topics remain challenging, highlighting the need for ongoing CRI development and research, including the need of more rigorous evaluations of CRI solutions and further formalization and maturation of CRI services and capabilities across the research enterprise.

AI Quality Standards in Health Care: Rapid Umbrella Review

BACKGROUND

In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies.

OBJECTIVE

This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period.

METHODS

We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies.

RESULTS

We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications.

CONCLUSIONS

Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.

Machine learning in cardiac surgery: a narrative review

Background and Objective

Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support.

Methods

We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery".

Key Content and Findings

ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited.

Conclusions

Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.

Use of artificial intelligence in critical care: opportunities and obstacles

BACKGROUND

Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.

MAIN BODY

Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.

CONCLUSIONS

AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework

BACKGROUND

Nowadays, healthcare systems are coping with the challenge of countering the exponential growth of healthcare costs worldwide, to support sustainability and to guarantee access to treatment for all patients.

METHODS

Artificial Intelligence (AI) is the technology able to perform human cognitive functions through the creation of algorithms. The value of AI in healthcare and its ability to address healthcare delivery issues has been a subject of discussion within the scientific community for several years.

RESULTS

The aim of this work is to provide an overview of the primary uses of AI in the healthcare system, to discuss its desirable future uses while shedding light on the major issues related to implications within international regulatory processes. In this manuscript, it will be described the main applications of AI in various aspects of health care, from clinical studies to ethical implications, focusing on the international regulatory framework in countries in which AI is used, to discuss and compare strengthens and weaknesses.

CONCLUSIONS

The challenges in regulatory processes to facilitate the integration of AI in healthcare are significant. However, overcoming them is essential to ensure that AI-based technologies are adopted safely and effectively.

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

Artificial Intelligence in Pediatrics: Learning to Walk Together

In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Informed consent for artificial intelligence in emergency medicine: A practical guide

As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study

BACKGROUND

AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs.

METHODS

Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers.

RESULTS

Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits.

CONCLUSIONS

Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach

BACKGROUND

Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages.

OBJECTIVE

We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use.

METHODS

At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation.

RESULTS

We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants.

CONCLUSIONS

As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment

Healthcare is one of the sectors where artificial intelligence (AI) is currently viewed as a crucial driving factor. Patient care, medical research, and clinical trial enrollment could all significantly improve due to AI's incorporation into electronic health records (EHRs). This short communication highlights how AI may improve the recruitment process regarding speed, accuracy, and overall cancer clinical trial efficiency. AI can automate this procedure by utilizing machine learning (ML) algorithms, identifying potential trial participants quickly and precisely. Many challenges could be addressed due to this integration, including data privacy and security that can be resolved through cutting-edge encryption techniques and differential privacy algorithms that ensure data anonymization. Another significant obstacle is the lack of common EHR formats and interoperability that can be addressed by creating a standardized structured layout. Automating and improving recruitment processes with AI may speed up research, increase the effectiveness of clinical trials, and open the door to more specialized cancer treatments.

Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?

Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.

A normative framework for artificial intelligence as a sociotechnical system in healthcare

Artificial intelligence (AI) tools are of great interest to healthcare organizations for their potential to improve patient care, yet their translation into clinical settings remains inconsistent. One of the reasons for this gap is that good technical performance does not inevitably result in patient benefit. We advocate for a conceptual shift wherein AI tools are seen as components of an intervention ensemble. The intervention ensemble describes the constellation of practices that, together, bring about benefit to patients or health systems. Shifting from a narrow focus on the tool itself toward the intervention ensemble prioritizes a "sociotechnical" vision for translation of AI that values all components of use that support beneficial patient outcomes. The intervention ensemble approach can be used for regulation, institutional oversight, and for AI adopters to responsibly and ethically appraise, evaluate, and use AI tools.

Physicians' and Machine Learning Researchers' Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study

BACKGROUND

Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance.

OBJECTIVE

This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.

METHODS

Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.

RESULTS

Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.

CONCLUSIONS

These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.

Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study

OBJECTIVES

Standardized nursing terminologies (SNTs) are necessary to ensure consistent knowledge expression and compare the effectiveness of nursing practice across settings. This study investigated whether SNTs can support semantic interoperability and outcoming tracking over time by implementing an AI-powered CDS tool for fall prevention across multiple EMR systems.

MATERIALS AND METHODS

The study involved 3 tertiary academic hospitals and 1 public hospital with different EMR systems and nursing terms, and employed an AI-powered CDS tool that determines the fall risk within the next hour (prediction model) and recommends tailored care plans (CDS functions; represented by SNTs). The prediction model was mapped to local data elements and optimized using local data sets. The local nursing statements in CDS functions were mapped using an ICNP-based inpatient fall-prevention catalog. Four implementation models were compared, and patient outcomes and nursing activities were observed longitudinally at one site.

RESULTS

The postimplementation approach was practical for disseminating the AI-powered CDS tool for nursing. The 4 hospitals successfully implemented prediction models with little performance variation; the AUROCs were 0.8051-0.9581. The nursing process data contributed markedly to fall-risk predictions. The local nursing statements on preventing falls covered 48.0%-86.7% of statements. There was no significant longitudinal decrease in the fall rate (P = .160, 95% CI = -1.21 to 0.21 per 1000 hospital days), but rates of interventions provided by nurses were notably increased.

CONCLUSION

SNTs contributed to achieving semantic interoperability among multiple EMR systems to disseminate AI-powered CDS tools and automatically track nursing and patient outcomes.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Translation of AI into oncology clinical practice

Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.

Cell projection plots: A novel visualization of bone marrow aspirate cytology

Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce novel, automatically generated visual summaries of bone marrow aspirate specimens called cell projection plots (CPPs). Encompassing relevant biological patterns such as neutrophil maturation, CPPs provide a compact summary of bone marrow aspirate cytology. To gauge clinical relevance, CPPs were inspected by 3 hematopathologists, who decided whether corresponding diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with a matching degree of 85%. Our finding suggests CPPs can represent clinically relevant information from bone marrow aspirate specimens and may be used to efficiently summarize bone marrow cytology to pathologists. CPPs could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic-support tool for digital pathology workflows.

Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework

OBJECTIVE

To derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing AI frameworks and integrated with reporting standards for clinical AI research.

MATERIALS AND METHODS

(1) Derive a provisional implementation framework based on the taxonomy of Stead et al and integrated with current reporting standards for AI research: TRIPOD, DECIDE-AI, CONSORT-AI. (2) Undertake a scoping review of published clinical AI implementation frameworks and identify key themes and stages. (3) Perform a gap analysis and refine the framework by incorporating missing items.

RESULTS

The provisional AI implementation framework, called SALIENT, was mapped to 5 stages common to both the taxonomy and the reporting standards. A scoping review retrieved 20 studies and 247 themes, stages, and subelements were identified. A gap analysis identified 5 new cross-stage themes and 16 new tasks. The final framework comprised 5 stages, 7 elements, and 4 components, including the AI system, data pipeline, human-computer interface, and clinical workflow.

DISCUSSION

This pragmatic framework resolves gaps in existing stage- and theme-based clinical AI implementation guidance by comprehensively addressing the what (components), when (stages), and how (tasks) of AI implementation, as well as the who (organization) and why (policy domains). By integrating research reporting standards into SALIENT, the framework is grounded in rigorous evaluation methodologies. The framework requires validation as being applicable to real-world studies of deployed AI models.

CONCLUSIONS

A novel end-to-end framework has been developed for implementing AI within hospital clinical practice that builds on previous AI implementation frameworks and research reporting standards.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions

OBJECTIVE

To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting.

BACKGROUND

To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown.

METHODS

Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2000; 7 of these 8 articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic or accuracy. Overall, 29 articles (80.6%) performed internal validation only, 5 (13.8%) performed external validation, and 2 (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy.

CONCLUSIONS

Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Clinical use of artificial intelligence requires AI-capable organizations

Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.

Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives

INTRODUCTION

Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed.

AREAS COVERED

AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies.

EXPERT OPINION

The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.

Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System

Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.

Approaches to Sampling for Quality Control of Artificial Intelligence in Biomedical Research

The aim of the study is to evaluate the efficacy of approaches to sampling during periodic quality control of the artificial intelligence (AI) results in biomedical practice.

Materials and Methods

The approaches to sampling based on point statistical estimation, statistical hypothesis testing, employing ready-made statistical tables, as well as options of the approaches presented in GOST R ISO 2859-1-2007 "Statistical methods. Sampling procedures for inspection by attributes" have been analyzed. We have considered variants of sampling of different sizes for general populations from 1000 to 100,000 studies.The analysis of the approaches to sampling was carried out as part of an experiment on the use of innovative technologies in computer vision for the analysis of medical images and their further application in the healthcare system of Moscow (Russia).

Results

Ready-made tables have specific statistical input data, which does not make them a universal option for biomedical research. Point statistical estimation helps to calculate a sample based on given statistical parameters with a certain confidence interval. This approach is promising in the case when only a type I error is important for the researcher, and a type II error is not a priority. Using the approach based on statistical hypothesis testing makes it possible to take account of type I and II errors based on the given statistical parameters. The application of GOST R ISO 2859-1-2007 for sampling allows using ready-made values depending on the given statistical parameters.When evaluating the efficacy of the studied approaches, it was found that for our purposes, the optimal number of studies during AI quality control for the analysis of medical images is 80 items. This meets the requirements of representativeness, balance of the risks to the consumer and the AI service provider, as well as optimization of labor costs of employees involved in the process of quality control of the AI results.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals

PURPOSE

The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals.

METHOD

In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team.

RESULTS

Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

CONCLUSIONS

The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Digital public health leadership in the global fight for health security

The COVID-19 pandemic highlighted the need to prioritise mature digital health and data governance at both national and supranational levels to guarantee future health security. The Riyadh Declaration on Digital Health was a call to action to create the infrastructure needed to share effective digital health evidence-based practices and high-quality, real-time data locally and globally to provide actionable information to more health systems and countries. The declaration proposed nine key recommendations for data and digital health that need to be adopted by the global health community to address future pandemics and health threats. Here, we expand on each recommendation and provide an evidence-based roadmap for their implementation. This policy document serves as a resource and toolkit that all stakeholders in digital health and disaster preparedness can follow to develop digital infrastructure and protocols in readiness for future health threats through robust digital public health leadership.

DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology

DECIDE-AI is a new, stage-specific reporting guideline for the early and live clinical evaluation of decision-support systems based on artificial intelligence (AI). It answers a need for more attention to the human factors influencing clinical AI performance and more transparent reporting of clinical studies investigating AI systems. Given the rapid expansion of AI systems and the concentration of related studies in radiology, these new standards are likely to find a place in radiological literature in the near future. This review highlights some of the specificities of AI as complex intervention, why a new reporting guideline was needed for early stage, live evaluation of this technology, and how DECIDE-AI and other AI reporting guidelines can be useful to radiologists and researchers.