Artificial Intelligence Health Research Articles

Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft

Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft

Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft

Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft

The ménage à trois of healthcare: the actors in after-AI era under patient consent.

Artificial intelligence has become an increasingly powerful technological instrument in recent years, revolutionizing many sectors, including public health. Its use in this field will inevitably change clinical practice, the patient-caregiver relationship and the concept of the diagnosis and treatment pathway, affecting the balance between the patient's right to self-determination and health, and thus leading to an evolution of the concept of informed consent. The aim was to characterize the guidelines for the use of artificial intelligence, its areas of application and the relevant legislation, to propose guiding principles for the design of optimal informed consent for its use. A classic review by keywords on the main search engines was conducted. An analysis of the guidelines and regulations issued by scientific authorities and legal bodies on the use of artificial intelligence in public health was carried out. The current areas of application of this technology were highlighted, divided into sectors, its impact on them, as well as a summary of current guidelines and legislation. The ethical implications of artificial intelligence in the health care system were assessed, particularly regarding the therapeutic alliance between doctor and patient, and the balance between the right to self-determination and health. Finally, given the evolution of informed consent in relation to the use of this new technology, seven guiding principles were proposed to guarantee the right to the most informed consent or dissent.

A Bibliographic Dataset of Health Artificial Intelligence Research.

Objective: The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artificial Intelligence (HAI) research. Data Source: We integrated HAI-related bibliographic records, including publications, open research datasets, patents, research grants, and clinical trials from Medline and Dimensions. Methods: Searching: Relevant documents were identified using Medical Subject Headings (MeSH) and Field of Research (FoR) indexed by 2 bibliographic databases, Medline and Dimensions. Extracting: MeSH terms annotated from the aforementioned bibliographic databases served as the primary information for our processing. For document records lacking MeSH terms, we re-extracted them using the Medical Text Indexer (MTI). Mapping: In order to enhance interoperability, HAI multi-documents were organized using a mapping system incorporating MeSH, FoR, The International Classification of Diseases (ICD-10), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). Integrating: All documents were curated based on a pre-defined ontology of health problems and AI technologies from the MeSH hierarchy. Results: We collected 96,332 HAI documents (publications: 75,820, open research datasets: 638, patents: 11,226, grants: 6,113, and clinical trials: 2,535) during 2009 to 2021. On average, 75.12% of the documents were tagged with at least one label related to either health problems or AI technologies (with 92.9% of publications tagged). Summary: This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR (Findable, Accessible, Interoperable, Reusable) standard, offering a valuable multidimensional collection for the community. This dataset serves as a crucial resource for horizontally scanning the funding, research, clinical assessments, and innovations within the HAI field.

Chief Editor’s foreword to the inaugural issue of Artificial Intelligence in Health

Chief Editor’s foreword to the inaugural issue of Artificial Intelligence in Health

Achieving the promise of artificial intelligence in health and medicine: Building a foundation for the future

Achieving the promise of artificial intelligence in health and medicine: Building a foundation for the future

A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study

A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency attendances and streamlining case referral when necessary. DOTS was built using retrospective tabular data from 11,315 attendances between July 1st, 2021, to June 15th, 2022at Moorfields Eye Hospital Emergency Department (MEH) in London, UK. Demographic and clinical features were used as inputs and a triage recommendation was given ("see immediately", "see within a week", or "see electively"). DOTS was validated temporally and compared with triage nurses' performance (1269 attendances at MEH) and validated externally (761 attendances at the Federal University of Minas Gerais - UFMG, Brazil). It was also tested for biases and robustness to variations in disease incidences. All attendances from patients aged at least 18 years with at least one confirmed diagnosis were included in the study. For identifying ophthalmic emergency attendances, on temporal validation, DOTS had a sensitivity of 94.5% [95% CI 92.3-96.1] and a specificity of 42.4% [38.8-46.1]. For comparison within the same dataset, triage nurses had a sensitivity of 96.4% [94.5-97.7] and a specificity of 25.1% [22.0-28.5]. On external validation at UFMG, DOTS had a sensitivity of 95.2% [92.5-97.0] and a specificity of 32.2% [27.4-37.0]. In simulated scenarios with varying disease incidences, the sensitivity was ≥92.2% and the specificity was ≥36.8%. No differences in sensitivity were found in subgroups of index of multiple deprivation, but the specificity was higher for Q2 when compared to Q4 (Q4 is less deprived than Q2). At MEH, DOTS had similar sensitivity to triage nurses in determining attendance priority; however, with a specificity of 17.3% higher, DOTS resulted in lower rates of patients triaged to be seen immediately at emergency. DOTS showed consistent performance in temporal and external validation, in social-demographic subgroups and was robust to varying relative disease incidences. Further trials are necessary to validate these findings. This system will be prospectively evaluated, considering human-computer interaction, in a clinical trial. The Artificial Intelligence in Health and Care Award (AI_AWARD01671) of the NHS AI Lab under National Institute for Health and Care Research (NIHR) and the Accelerated Access Collaborative (AAC).

Trust criteria for artificial intelligence in health: normative and epistemic considerations

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that...

Artificial intelligence in health and science: an introspection

INTRODUCTION: Whenever there has been a technological revolution, there have been advantages and disadvantages associated with it. Artificial intelligence is now going through this phase of uncertainty. There are groups that agree and embrace this new technology, and others that simply refuse to let it into our daily lives. OBJECTIVE: The purpose of this reflection will be to understand the advantages and disadvantages of the use of artificial intelligence in health and science.

THE IMPACT OF AI IN HEALTH CARE: DIAGNOSIS AND PATIENT CARE

An exciting new era in healthcare has begin with the introduction of Artificial Intelligence (AI), which holds the promise of resolving some of the industry’s most serious problems. The present review delves into the various aspects of artificial intelligence’s impact on healthcare, primarily emphasising its function in patient care and diagnosis. This review explores the development of AI technologies, their use in medical diagnostics, and their significant influence on patient care procedures, drawing on a wide range of research papers. The study addresses the benefits, drawbacks, and potential applications of AI-driven healthcare technologies. In addition, it discusses the necessity of a cordial collaboration between AI and medical professionals as well as the ethical and legal ramifications of AI in healthcare

Artificial intelligence in health and bioethical implications: a systematic review

Resumo A presença de inteligência artificial na saúde vem crescendo, ajudando em diagnósticos e tomadas de decisão, mas suas implicações geram dúvidas relacionadas à ética. Esta revisão sistemática, baseada nas diretrizes Prisma, identificou os usos de inteligência artificial na saúde e suas implicações bioéticas. Foi realizada busca nas bases de dados Science Direct e Scopus usando os descritores “artificial intelligence”, “bioethics” e “health”. Trabalhos em inglês, publicados entre 2017 e 2021 foram considerados, resultando em 102 artigos. Após aplicação dos critérios estabelecidos, 11 foram selecionados. Os estudos discutiram os princípios bioéticos da beneficência, não maleficência, autonomia e justiça, adicionando o elemento explicabilidade. Inteligência artificial mostrou correlação com imprevisibilidade, previsibilidade, confiança, papel do médico, desenvolvimento de sistemas, privacidade, segurança de dados, e aspectos sociais e financeiros. Desenvolvedores, profissionais da saúde e pacientes devem maximizar os benefícios e limitar os riscos das ferramentas que usam essa tecnologia.

Inteligência artificial em saúde e implicações bioéticas: uma revisão sistemática

Abstract The presence of artificial intelligence in healthcare is growing, helping in diagnosis and decision making. However, its application raises doubts, mostly related to ethics. This study aimed to identify its uses in health and its bioethical implications from a systematic literature review using the PRISMA guidelines. The ScienceDirect and Scopus databases were searched, using the descriptors “artificial intelligence,” “bioethics” and “health.” Works in English, published between 2017 and 2021 were considered, resulting in 102 articles found and, after applying the established criteria, 11 were selected. The studies reported on the bioethical principles of beneficence, non-maleficence, autonomy and justice, adding an element, explainability. Relationships were found between artificial intelligence in health and unpredictability, predictability, trust, physicians’ role, systems development, privacy, data security, financial and social aspects. Developers, healthcare professionals and patients must maximize the benefits and limit the risks of tools that use this technology.

Artificial intelligence in health and bioethical implications: a systematic review

Resumen El uso de la inteligencia artificial en salud va en aumento por facilitar el diagnóstico y la toma de decisiones, pero sus implicaciones plantean dudas relacionadas con la ética. Esta revisión sistemática desde las directrices Prisma identificó los usos de la inteligencia artificial en salud y sus implicaciones bioéticas. Las búsquedas se realizaron en Science Direct y Scopus utilizando los descriptores “artificial intelligence”, “bioethics” y “health”. De los trabajos en inglés publicados entre 2017 y 2021, se obtuvo 102 artículos. Aplicados los criterios, quedaron 11. Los estudios abordaron los principios bioéticos de beneficencia, no maleficencia, autonomía y justicia, añadiendo el elemento explicabilidad. La inteligencia artificial se correlacionó con la imprevisibilidad, previsibilidad, confianza, papel de los médicos, desarrollo de sistemas, privacidad, seguridad de los datos y aspectos financieros y sociales. Los desarrolladores, los profesionales sanitarios y los pacientes deben maximizar los beneficios y limitar los riesgos que involucra esta tecnología.

Artificial Intelligence in Health: Enhancing a Return to Patient-Centered Communication.

The medical environment is on the verge of a dramatic transformation as artificial intelligence (AI) evolves. With the inevitable shift toward AI in health care delivery, there are concerns around its implementation, including ethics, privacy, data representation, and the potential for eliminating physicians. However, AI cannot replicate a physician's knowledge and understanding of the patient as a person and the conditions in which he or she lives. Therefore, provider-patient communication will be paramount in providing safe and effective health care. This piece describes the importance of patient-centered communication and the unintentional move away from this in recent times. We argue that patient-provider communication is vital in the age of AI as it will integrate into the way medicine is practiced, thus leading to more time with the patient to build rapport, trust, and empathy. This will ultimately lead to optimal health-related outcomes.

The COVID-19 Pandemic and Artificial Intelligence (AI) Applications in Health: How Much Are We Interested in?

Objective New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed "Artificial Intelligence in Health During COVID-19 Pandemic Process" organized online by İzmir Bakırçay University and İzmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications. 
 Materials and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting. 
 Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued.
 Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.

Multimodal biomedical AI.

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

The Current State and Future of Artificial Intelligence in Health

An overview of the current situation of AI applications in the health field is presented from several aspects, such as whole-cycle health management and infectious disease prevention and control, and an analysis of the prospects for the integration of AI with the health field and the challenges faced in terms of data and technology.

Towards a data collection methodology for Responsible Artificial Intelligence in health: A prospective and qualitative study in pregnancy

A medical field that is increasingly benefiting from Artificial Intelligence applications is Gyne- cology and Obstetrics. In previous work, we exposed that Artificial Intelligence (AI) technology and obstetric control by physicians can enhance pregnancy health, leading to better pregnancy outcomes and overall better experience, also reducing any possible long-term effects that can be produced by complications. This work presents a data collection methodology for responsible AI in Health and a case study in the pregnancy domain. It is a qualitative descriptive study on the preferences and expectations expressed by pregnant women regarding responsible AI and affective computing. A 41-items structured interview was distributed among 150 pregnant pa- tients attending prenatal care at Hospital Virgen del Rocío and the Clinic Victoria Rey (Seville, Spain) during the months of October and November 2020. A substantial interest in intelligent pregnancy solutions among pregnant women has been revealed in this study. Participants with a lower level of interest reported privacy concerns and lack of trust towards AI solutions. Re- garding affective computing based intelligent solutions specifically, most participants reported positively and no significant difference was found between women having a healthy or a high risk pregnancy on this matter. Our findings also suggest that a high demand of personalized intelligent solutions exists among participants. On the topic of sharing pregnancy data with the healthcare provider in favor of scientific research, pregnant women assisting public health- care services were found to be more likely to share their data when the provider was a public healthcare system rather than a private entity. Pregnant women who are interested in using an AI pregnancy application share a strong idea that it needs to be responsible, trustworthy, useful, and safe. Likewise, we found that pregnant women would change their mind about their concerns and they would feel more confident if the intelligent solution gives explanations about the system decisions and recommendations, as XAI approach promotes.

Effects Of Artificial Intelligence Used In Healthcare On Confidence On Patient And Physician

Artificial intelligence and robotic systems are rapidly entering healthcare, playing key roles in certain medical functions, including diagnostics and clinical treatments. The focus in the development of health technology has been on human-machine interactions. This has led to a number of technology-centric problems. This study focuses on the impact of these technologies on the patient-doctor relationship and human-organization in the healthcare. It’s argued that artificial intelligence in health can have significant effects on patient-doctor trust. It focuses on three main drivers of trust that is potentially supported or disrupted by the introduction of artificial intelligence or robotic systems in healthcare. First, doctors are certified and licensed to practice medicine. A license; indicates that some individuals have certain skills, knowledge, and a high level of value. Second, physicians appear to play a role as part of an active duo tasked with providing care that promotes patient value. Finally, a patient's experiences with their doctor are a positive or negative driver of trust between patient-physician understanding. When a doctor interacts with a patient, he builds social and experiential "capital" with him, and this understanding leads to increased trust. It’s argued that healthcare artificial intelligence systems should be considered as assistive technologies that go beyond the usual functions of medical devices. As a result, AI systems in health need to be regulated to provide relevant values. It’s suggested for patients and physicians that three high-level principles can guide this effort. Medical professionals must be licensed in healthcare AI systems; Alternative care methods should be provided until the approval and “standard of care” given by the patient or caregiver prior to its implementation and in terms of artificial intelligence is accepted. In prioritizing these functions in regulatory measures, medical community favour the appropriate societal and interpersonal deployment and implementation of such health technologies.

The applications and position of artificial intelligence in health and medicine: a short review

In our developing and changing world, as in every field, technological breakthroughs, investments, and software have shown significant improvement in the health and medicine sector. As specialized software has made an enormous leap forward in the health sector, the health system has become more modernized, technological, and functional. With technological development,diagnosis and treatment became more accessible, and more positive results were obtained. Modernized hospitals and health institutions have been established with the development of Artificial Intelligence (AI) technology. In these modernizing health institutions, much healthier results are obtained with robotic surgical procedures, and faster recovery is achieved in patients with minimum error margin. In this study, the importance of AI technologies in medicine and health, the innovations, application areas, advantages-disadvantages, and artificial intelligence studies used in the fight against Covid-19 were examined. Among these applications, Virtual Reality, Augmented Reality, Artificial Intelligence Supported Chatbots, Virtual Nursing Assistants,Natural Language Processing, and Surgical Operation Support Robots are discussed.