Artificial Intelligence In Healthcare Research Articles

Ethical considerations for large language models in ophthalmology.

This review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology. This review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards. The integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.

Artificial Intelligence in Healthcare: Pros, Cons, and Future Expectations

Artificial intelligence (AI) has rapidly become a globally accessible resource for organizing and processing information, improving efficiency, and increasing effectiveness. In healthcare, AI has shown the potential to identify health risks at early stages and enhance current treatment processes. As AI becomes more widely integrated, it is essential to consider the benefits and risks associated with this largely unproven technology to create an environment where patients and providers universally and equitably experience its advantages.

AI-driven innovations for enhancing mental health care in Tanzania: opportunities and challenges

Purpose This study aims to explore how artificial intelligence (AI) can enhance mental health care in Tanzania, focusing on its potential to enhance mental health services and address challenges in a low-resource setting. Design/methodology/approach A qualitative case study approach was used, with data collected through semi-structured interviews and focus group discussions involving key stakeholders in mental health and AI, including policymakers, technical experts, health-care providers and patient advocacy groups. Thematic analysis was used to identify key themes related to the opportunities and barriers to AI integration in mental health care. Findings This study identified several benefits of AI in mental health care, including improved diagnostic accuracy, personalised treatment and the potential for real-time monitoring of patients. However, significant barriers to AI adoption remain, such as infrastructure limitations, data privacy concerns and the need for training and resources to effectively integrate AI into mental health services. Originality/value This study contributes to the growing literature on AI in health care by focusing on its application in mental health care in Tanzania, a low-resource setting. The research provides valuable insights into how AI can bridge gaps in mental health service delivery, particularly in underserved regions, while highlighting the challenges that must be addressed for successful implementation.

6.Y.2. AI and health care: what are the capabilities and limitations?

Abstract With the rapid advancement of artificial intelligence (AI) technologies and their implementation in health care, there is a critical need to explore the different applications and implications of AI. This workshop seeks to look at different aspects of AI, both potential applications for AI technology in health, public opinions of use of AI in health care, and the reality of implementing AI in different contexts. Understanding health care approaches to AI from different countries is a unique opportunity for shared learning of innovations across various sectors of health care and patient engagement in order to most effectively engage this emerging field. AI, in whichever form it may be incorporated into health care, has the opportunity to impart meaningful, innovative change, but understanding this new technology comes with important questions. This session will dive into research on AI in various areas of health care by 1) looking at different ways AI is being harnessed in health care; 2) discussing lessons which can be learned and adapted from other countries; 3) discussing promising solutions and approaches to implementing AI in health care; 4) discussing the limitations or concerns of integrating AI in health care. The panel will be composed of 1 moderator and 2 panelists. Panelists are experts in their fields, established researchers and practitioners, and alumni of the Harkness Fellowship from Germany and the United Kingdom. A moderator will briefly frame the discussion for the audience and introduce the speakers and their presentations before leading an interactive, engaging question and answer portion. The coherence of this panel lays in its theme of leveraging AI to enhance healthcare delivery and understanding ways we can work across countries to foster ethical, equitable AI technologies that revolutionize healthcare practices with the ultimate goal of improving patient outcomes and health system efficiencies. Dr. Saira Ghafur from the U.K. will begin by presenting her research on evidence-based reasons for implementing AI in health care and Dr. Benedikt Simon from Germany will close out by discussing his work in AI in Germany and the challenges that have prevented robust integration. Each panelist will speak for roughly 8-10 minutes, with about 5 minutes reserved for moderator and audience questions. Key messages • Sharing cross-country experiences and research enables us to better understand how AI can impact health care. • In weighing both the benefits and challenges or limitations of implementing AI, we can best understand effective approaches to utilizing AI in health care.

Evidence for AI in healthcare

Abstract In healthcare, novel AI solutions are being created to address some of the biggest challenges in the prognosis, diagnosis, and treatment of disease, as well as clinician workflows and service improvement. The emergence of generative AI presents a compelling opportunity to revolutionize diagnostics, treatment planning, medical research, and patient engagement. The hypothesized uses of generative AI are broad, ranging from medical education, providing information to patients, generating synthetic patient data for the validation of AI tools, to the analysis of continuous data from wearables to detect early signs of disease. Adoption of digital solutions and AI in healthcare is slower than in other industries. The majority of clinicians don’t have direct experience with AI technologies, only a quarter have recommended a digital therapeutic, and less than a fifth have prescribed one. Safety, quality and confidence can be built through appropriate governance, testing, careful implementation and appropriate clinical use. There is a responsibility for AI developers, health systems and providers, as well as regulators, to create clear expectations for evidence and solutions. AI solutions require unique model evidence (evidence for the underlying algorithm) and solution evidence (evidence for the product in which the algorithm is embedded). Models will need validation first on internal and then on external datasets (internal and external validation). This will indicate how accurate and reliable the model is, but will be limited to the dataset that a client has access to. By examining ways to evaluate evidence for AI in healthcare, we can develop a better understanding of how best to use AI to its most appropriate advantages.

The assessment of society’s acceptance of artificial intelligence use in healthcare

Abstract Background We observe the surge of interest and application of artificial intelligence (AI) in various areas. AI use in healthcare and medicine is followed by society with interest; however, acceptance varies depending on the area of its application. This study aimed to understand the differences in acceptance depending on the type of solution and explain the factors influencing the attitude toward the use of AI in healthcare. Methods The data from the online survey was collected from a representative sample of 1109 adult Internet users in Poland. The respondents were asked about their acceptance of AI use for several purposes in healthcare. Based on the responses to these items, an ad-hoc score of AI acceptance in healthcare (AIAHC) was developed. The determinants of AIAHC were assessed with uni- (ULRM) and multivariable linear regression (MLRM) models. Results The highest acceptance of AI use was found for monitoring medication intake at home: mean (standard deviation, SD)-3.81 (1.39), the lowest for AI making decisions about the mode of therapy -2.78 (1.35). ULRMs showed that significant predictors of AIAHC were age, gender, chronic disease, vocational status, income, the use of the Internet (IU), and e-health literacy (eHL). In MLRM, only gender, vocational status, income, and eHL maintained a significant relationship with AIAHC. Males showed significantly higher AIAHC than females (B, 95%CI: 2.56, 1.42 - 3.71), and students had higher AIAHC than employees (B, 95%CI: 3.01, 0.14 - 5.89). Persons who refused to reveal their income demonstrated lower acceptance of AI use than those who revealed their income (B, 95%CI: -4.64, -6.54 - -2.74). Finally, greater eHL favored higher AIAHC (B, 95%CI: 0.46, 0.34 - 0.57). Conclusions Evidently, there is a hierarchy of potential areas of application in terms of society’s acceptance. Improving eHL should exert a positive effect on the acceptance of innovative technologies, including AI, in healthcare. Key messages • Citizens show moderate enthusiasm toward the broader use of AI in healthcare. • E-health literacy exerts a positive effect on the introduction of AI-based solutions in healthcare.

Effect on comprehension of an AI patient-friendly hospital discharge letter: a quasi-RCT

Abstract Background During transition from hospital to home care, discharge letters serve as a bridge of information between hospital physicians, general practitioners, and patients. However, the complexity of these letters often hinders patient comprehension, leading to medication errors and adverse outcomes. We assessed the effect on patients’ comprehension and satisfaction of using AI to transform discharge letters into patient-friendly versions. Methods An online quasi-randomized controlled parallel-group trial was conducted in Italy. A synthetic discharge letter for a chronic obstructive pulmonary disease patient was drafted. GPT-4 rephrased the letter into an easier-to-understand version. Participants were assigned to receive either the original or AI-generated letter and completed comprehension assessments and satisfaction survey. Results Out of 460, 166 participants received the original letter, and 175 the AI version. The AI group showed significantly higher comprehension scores (median 4;IQR 2-4 vs. 2; IQR1-2, p < 0.001) and outperformed the control group in understanding diagnosis, medications, and antibiotic instructions. Perceived satisfaction was higher in the AI group across all measures (p < 0.001). Health literacy was a good predictor of comprehension in both arms (p = 0.013; p = 0.015). Subgroup analysis revealed the AI letter nearly bridged the comprehension gap between non-healthcare individuals and healthcare professionals receiving the original letter. Conclusions AI-optimized discharge letters improved patient understanding, and satisfaction compared to traditional letters. The study underscores the influence of sociodemographic factors like healthcare experience and health literacy on comprehension, advocating for adaptive communication strategies. These findings contribute to discussions about integrating AI in healthcare to develop patient-centered communication approaches. Key messages • AI-generated patient-friendly discharge letters significantly improved comprehension and satisfaction compared to traditional letters. • Integrating AI in healthcare communication can help bridge comprehension gaps and promote patient-centered care.

Understanding Consumer Intentions to Engage with Artificial Intelligence and Voice Assistants: A Conceptual Framework

Artificial Intelligence (AI) has grown extensively in recent times and is becoming integrated into human lives. This progression opens diverse opportunities in e-health, facilitating healthcare accessibility globally. Voice assistants are AI-driven technologies, which are becoming increasingly important with the growing prominence of e-health services. This study offers an integrated model that assimilates AI attributes with relevant constructs from various theories such as Technology Acceptance Model (TAM) and Health Belief Model (HBM). Key variables under scrutiny include perceived anthropomorphism, perceived intelligence, perceived ease of use, perceived usefulness, perceived enjoyment, attitude, perceived severity, and perceived susceptibility. The article adds to the current research by reviewing the literature about AI in healthcare, and proposing a conceptual framework that binds technological, AI-specific, and health factors, offering implications for policymakers, healthcare administrators, and marketers seeking effective utilization of voice assistant technology.

6.Q. Round table: Artificial Intelligence in healthcare: navigating ethically with equity and workforce empowerment

Abstract Background Artificial Intelligence (AI) is emerging as a pivotal technology with vast promises for healthcare. However, integrating AI into clinical and public health settings must be cautiously approached to ensure that it does not inadvertently exacerbate existing health system problems. The healthcare workers, already under severe stress, could view AI as a threat to job security rather than as a support mechanism. Moreover, past experiences with digital transformations, such as Electronic Health Records, have shown that technological integration can sometimes increase rather than decrease the burden on healthcare workers, leading to burnout and dissatisfaction, and thus worsening the healthcare workforce crisis. Furthermore, equity and ethical considerations are paramount in the deployment of AI in healthcare. Data privacy, patient consent, and algorithmic bias must be addressed to ensure that AI applications are designed to support and enhance human decision-making that is sensitive to the social determinants of health and accountable to equity and social inclusion, to the needs and rights of the healthcare workforce, and the dignity of the patients and populations. While AI presents significant opportunities for health systems and healthcare workers, there is a lack of knowledge, evidence-based policies, and ethical frameworks that support equitable and human-centred approaches to AI implementation. Objectives This round table workshop aligns three major public health challenges: the integration of AI in health systems, the global healthcare workforce crisis, and the improvement of equity and equality. It critically explores capacity building for AI, equity in AI implementation, and regulatory measures for ethical and responsible AI deployment, addressing the following major questions: What are the critical impacts of AI on the healthcare workforce? How can the healthcare workforce be effectively upskilled and supported to adapt to the changes brought by AI technologies? What regulatory frameworks and governance models are necessary to ensure AI’s safe and ethical implementation in healthcare, that is also sensitive to equity, gender equality and the needs of minority groups? Finally, what actionable steps and leadership can public health take to implement AI technologies while addressing the healthcare workforce needs, equity issues, and ethical guidelines? The panellists will illuminate these questions from different disciplinary approaches, helping us to disentangle complexity and to build capacity for evidence-based and socially inclusive AI policies. The workshop contributes to better understand the risks and benefits of AI. It seeks to advance knowledge exchange of good practice experiences and effective implementation. Key messages • The effects of AI on the healthcare workforce must be monitored and strategies adapted to mitigate the healthcare workforce crisis and to upskill and empower healthcare workers. • There is a need for human-centred and ethically responsive AI implementation and governance measures that support equity, gender equality, and diversity in healthcare settings. Speakers/Panelists Abi Sriharan Schulich School of Business York University, York, Canada Kasia Czabanowska Maastricht University, Maastricht, Netherlands Bernadette Kumar Migration Health Unit, Norwegian Institute of Public Health, Oslo, Norway Marius-Ionuț Ungureanu Babeș-Bolyai University, Cluj-Napoca, Romania Farhang Tahzib Faculty of Public Health, Haywards heath, UK

Challenges to implementing AI in health care

Abstract Germany, renowned for its robust healthcare system, finds itself at the early stages of AI integration. This is a reality for many European countries, emphasizing that, despite general technological prowess, AI adoption remains in its absolute infancy. However, in Germany there are areas where AI shows promise, notably in fields like radiology. Beyond such niche applications though, Germany grapples with a significant challenge - the scarcity of robust data essential for meaningful AI utilization, particularly in the realm of public health. While these insights are specific to the German context, understanding the intricate hurdles impeding widespread AI implementation is a meaningful takeaway for countries around the world. In order to effectively leverage new technologies, there is an acute need for advancements in data infrastructure and governance to unlock the full potential of AI in healthcare. Overcoming barriers and charting a path towards a future where AI serves as a transformative force in German healthcare, could one day lead to enhanced system efficiencies and patient experiences, but for now we have to be thoughtful about the realities of implementing AI within current existing health system infrastructure.

Exploring the Impacts of Artificial Intelligence Interventions on Provider Practice practice

Abstract Background Despite the growing prevalence of AI in healthcare, there has been a lack of recent studies exploring its impact on providers’ practices and patient outcomes. This study investigates healthcare providers’ perceptions of AI interventions’ influence on their practice efficiency and patient outcomes, as well as the correlation between providers’ perceptions of AI and its effects on patient outcomes. Methods A self-administered questionnaire was mailed to 38 healthcare providers at a rural medical center in north Texas, with a 70% percent response rate (n = 27). In addition to descriptive statistics, multivariable logistic regression test was conducted to discern the relationship between their perceptions of AI and its impact on patient outcomes. Results The findings revealed that a majority of providers perceived AI to still be in its early stages, posing challenges for practice and having a limited impact on patient outcomes. Additionally, various factors such as age, gender, a user-centered design approach, AI experience, along with perceptions of workplace support and stress, significantly shape providers’ attitudes toward AI and ultimately affecting patient outcomes. Conclusions Before AI technology can fulfill its promise of transforming healthcare through integration with other technologies, the healthcare sector must address numerous hurdles. It is crucial to approach the development and testing of intricate systems like AI-integrated electronic health record (EHR) systems with caution to ensure their reliability and dependability in clinical decision-making. Additionally, navigating medico-legal responsibilities and pursuing fair distribution of benefits are equally crucial. Key messages • This study offers insights into the extent of AI interventions’ impact on healthcare. • This study specifically focuses on enhancing patient care, reducing costs, and improving the efficiency of the healthcare system.

Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice

PurposeThis study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice.Design/methodology/approachA quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare.FindingsThe study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI’s role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology.Originality/valueThis study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

Digital health and some thematic shifts in bioethics in academic publications after the pandemic

Abstract Background Digital health (DH) presents vast opportunities, but also dynamic shift in viewpoints, which is reflected in academic works. Since DH concerns patient data, a major issue is complying with the bioethical principles, and ensuring patients’ privacy and security. The aim of this research is to explore avenues, in which ethics and privacy are reflected in DH, especially in terms of research, legislation, artificial intelligence (AI) and blockchain applications in medicine and public health. Methods Literature search for full text publications in PubMed, Scopus and ScienceDirect focused on ethics and privacy in DH in the period 2019-2023 found 325 articles in English authored by academicians. Six thematic categories were identified: ethics (fundamental principles; ethics in using novel DH tools); privacy (in use of apps/wearables; as a challenge/barrier to DH interventions/technologies); ethics/privacy in: DH research (data gathering, use, sharing); policy and legislation (for DH applications); AI and blockchains (in medicine and public health). Results Articles were from 32 countries: USA-24.3%; UK-13.5%; Germany-7.7%; Canada-7.4%; Australia-6.7%; Netherlands, Italy, Switzerland-4.7% each; China, India-3.0% each and 20.3% from 22 other countries; 86.5% in medical/DH journals. Only 10.2% were published in 2019; 17.8% - 2020; 19.4% - 2021; 27.1% - 2022 and 25.5% - 2023. Privacy was discussed in 26.5%; ethics-23.7%; research-19.4%; AI-15.1%; policy and legislation-9.5% and blockchains-5.5% of the articles. The publication activity of all six categories increased after the unfold of the pandemic in 2020. In 2023 publications for ethics (13%), privacy (5.8%) and research (6.3%) decreased; for legislation (12.9%), AI (14.3%) and blockchains in healthcare (16.8%) increased. Conclusions The pandemic and its aftermath present a change in academic interest. Traditional ethical fundaments in DH slightly lose position in favor of top notch technologies and apt legislation. Key messages • The pandemic and modern technologies enforced new digital health applications. Further research will reveal the needs of AI and blockchain applications respecting privacy and the ethical principles. • Besides existing legislation and regulations, in order to catch up with the dynamic developments in DH, the legal base needs to become more flexible. Technologies change, privacy importance does not.

Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.

Artificial intelligence in rheumatology: perspectives and insights from a nationwide survey of U.S. rheumatology fellows.

Artificial Intelligence (AI) is poised to revolutionize healthcare by enhancing clinical practice, diagnostics, and patient care. Although AI offers potential benefits through data-driven insights and personalized treatments, challenges related to implementation, barriers, and ethical considerations necessitate further investigation. We conducted a cross-sectional survey using Qualtrics from October to December 2023 to evaluate U.S. rheumatology fellows' perspectives on AI in healthcare. The survey was disseminated via email to program directors, who forwarded it to their fellows. It included multiple-choice, Likert scale, and open-ended questions covering demographics, AI awareness, usage, and perceptions. Statistical analyses were performed using Spearman correlation and Chi-Square tests. The study received IRB approval and adhered to STROBE guidelines. The survey aimed to reach 528 U.S. rheumatology fellows. 95 fellows accessed the survey with response rate to each question varying between 85 and 95 participants. 57.6% were females, 66.3% aged 30-35, and 60.2% in their first fellowship year. There was a positive correlation between AI familiarity and confidence (Spearman's rho = 0.216, p = 0.044). Furthermore, 67.9% supported incorporating AI education into fellowship programs, with a significant relationship (p < 0.005) between AI confidence and support for AI education. Fellows recognized AI's benefits in reducing chart time (86.05%) and automating tasks (73.26%), but expressed concerns about charting errors (67.86%) and over-reliance (61.90%). Most (84.52%) disagreed with the notion of AI replacing them. Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications. Addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.

Fetal Face: Enhancing 3D Ultrasound Imaging by Postprocessing With AI Applications: Myth, Reality, orLegal Concerns?

The use of artificial intelligence (AI) platforms is revolutionizing the performance in managing metadata and big data. Medicine is another field where AI is spreading. However, this technological advancement is not amenable to errors or fraudulent misconducts. International organization and recently the European Union have released principles and recommendations for an appropriate use of AI in healthcare. In prenatal ultrasound diagnosis, the use of AI in daily practice is having a revolutionary impact. Notwithstanding, the diagnostic enhancement should be regulated, and AI applications should be developed to guarantee correct imaging acquisition and further postprocessing.

Chatbots in medicine: certification process and applied use case.

Chatbots are computer programs designed to engage in natural language conversations in an easy and understandable way. Their use has been accelerated recently with the advent of large language models. However, their application in medicine and healthcare has been limited due to concerns over data privacy, the risk of providing medical diagnoses, and ensuring regulatory and legal compliance. Medicine and healthcare could benefit from chatbots if their scope is carefully defined and if they are used appropriately and monitored long-term. The confIAnce chatbot, developed at the Geneva University Hospitals and the University of Geneva, is an informational tool aimed at providing simplified information to the general public about primary care and chronic diseases. In this paper, we describe the certification and regulatory aspects applicable to chatbots in healthcare, particularly in primary care medicine. We use the confIAnce chatbot as a case study to explore the definition and classification of a medical device and its application to chatbots, considering the applicable Swiss regulations and the European Union AI Act. Chatbots can be classified anywhere from non-medical devices (informational tools that do not handle patient data or provide recommendations for treatment or diagnosis) to Class III medical devices (high-risk tools capable of predicting potentially fatal events and enabling a pre-emptive medical intervention). Key considerations in the definition and certification process include defining the chatbot's scope, ensuring compliance with regulations, maintaining security and safety, and continuously evaluating performance, risks, and utility. A lexicon of relevant terms related to artificial intelligence in healthcare, medical devices, and regulatory frameworks is also presented in this paper. Chatbots hold potential for both patients and healthcare professionals, provided that their scope of practice is clearly defined, and that they comply with regulatory requirements. This review aims to provide transparency by outlining the steps required for certification and regulatory compliance, making it valuable for healthcare professionals, scientists, developers, and patients.

Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.

Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require large sets of healthcare data for training. Embedded bias introduced into AI and ML models not only has disastrous consequences for quality of care but also may perpetuate and exacerbate health disparities. The lack of test harmonization, which is defined as the ability to produce comparable results and the same interpretation irrespective of the method or instrument platform used to produce the result, may introduce aggregation bias into algorithms with potential adverse outcomes for patients. Limited interoperability of laboratory results at the technical, syntactic, semantic, and organizational levels is a source of embedded bias that limits the accuracy and generalizability of algorithmic models. Population-specific issues, such as inadequate representation in clinical trials and inaccurate race attribution, not only affect the interpretation of laboratory results but also may perpetuate erroneous conclusions based on AI and ML models in the healthcare literature.

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

BackgroundThe global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.PurposeThis review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety.MethodsWe deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.ResultsWe report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.ConclusionsOur findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Artificial Intelligence in Radiology, Emergency, and Remote Healthcare: A Snapshot of Present and Future Applications

This paper critically examines artificial intelligence in the healthcare sector and aims to identify concrete points of challenges and business value propositions first in radiology and then across healthcare more broadly. It discusses current applications in radiology and future uses of AI in healthcare, focusing on three main areas: (i) emergency incidents handling, (ii) intensive care unit treatment and (iii) augmented telemedicine, to which emergency radiology is a critical success factor. Despite some risks and compliance issues that need to be taken care of, this paper clearly shows that AI has the potential (a) to reengineer the business processes of the healthcare sector, using AI-assisted radiology as a driver and (b) to improve the effectiveness of the healthcare system as well as (c) to increase the quality provision of healthcare services. Despite its slow adoption, AI-assisted healthcare can indeed offer business/operational solutions that benefit all healthcare stakeholders.