Algorithmic bias and research integrity; the role of nonhuman authors in shaping scientific knowledge with respect to artificial intelligence: a perspective

Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review

AIM

This study systematically explores the ethical and regulatory considerations surrounding the integration of artificial intelligence (AI) and machine learning (ML) in nursing practice, with a focus on patient autonomy, data privacy, algorithmic bias, and accountability.

BACKGROUND

AI and ML are transforming nursing practice by enhancing clinical decision-making and operational efficiency. However, these technologies present significant ethical challenges related to ensuring patient autonomy, safeguarding data privacy, mitigating algorithmic bias, and ensuring transparency in decision-making processes. Current frameworks are not sufficiently tailored to nursing-specific contexts.

METHODS

A systematic review was conducted, adhering to PRISMA guidelines. Six major databases were searched for studies published between 2000 and 2024. Seventeen studies met the inclusion criteria and were included in the final analysis.

RESULTS

Five key themes emerged from the review: enhancement of clinical decision-making, promotion of ethical awareness, support for routine nursing tasks, challenges in algorithmic bias, and the importance of public engagement in regulatory frameworks. The review identified critical gaps in nursing-specific ethical guidelines and regulatory oversight for AI integration in practice.

DISCUSSION

AI technologies offer substantial benefits for nursing, particularly in decision-making and task efficiency. However, these advantages must be balanced against ethical concerns, including the protection of patient rights, algorithmic transparency, and bias mitigation. Current regulatory frameworks require adaptation to meet the ethical needs of nursing.

CONCLUSION AND IMPLICATIONS FOR NURSING AND HEALTH POLICY

The findings emphasize the need for the development of nursing-specific ethical guidelines and robust regulatory frameworks to ensure the responsible integration of AI technologies into nursing practice. AI integration must uphold ethical principles while enhancing the quality of care.

Are we ready to integrate advanced artificial intelligence models in clinical laboratory?

The application of advanced artificial intelligence (AI) models and algorithms in clinical laboratories is a new inevitable stage of development of laboratory medicine, since in the future, diagnostic and prognostic panels specific to certain diseases will be created from a large amount of laboratory data. Thanks to machine learning (ML), it is possible to analyze a large amount of structured numerical data as well as unstructured digitized images in the field of hematology, cytology and histopathology. Numerous researches refer to the testing of ML models for the purpose of screening various diseases, detecting damage to organ systems, diagnosing malignant diseases, longitudinal monitoring of various biomarkers that would enable predicting the outcome of each patient's treatment. The main advantages of advanced AI in the clinical laboratory are: faster diagnosis using diagnostic and prognostic algorithms, individualization of treatment plans, personalized medicine, better patient treatment outcomes, easier and more precise longitudinal monitoring of biomarkers, etc. Disadvantages relate to the lack of standardization, questionable quality of the entered data and their interpretability, potential over-reliance on technology, new financial investments, privacy concerns, ethical and legal aspects. Further integration of advanced AI will gradually take place on the basis of the knowledge of specialists in laboratory and clinical medicine, experts in information technology and biostatistics, as well as on the basis of evidence-based laboratory medicine. Clinical laboratories will be ready for the full and successful integration of advanced AI once a balance has been established between its potential and the resolution of existing obstacles.

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study

BACKGROUND

Pediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study explores the use of an advanced artificial intelligence program, the Generative Pre-trained Transformer (GPT), to enter pediatric stroke data into the IPSS.

METHODS

The most recent 50 clinical notes of patients with ischemic stroke or cerebral venous sinus thrombosis at the UTHealth Pediatric Stroke Clinic were deidentified. Domain-specific prompts were engineered for an offline artificial intelligence program (GPT) to answer IPSS questions. Responses from GPT were compared with the human rater. Percent agreement was assessed across 50 patients for each of the 114 queries developed from the IPSS database outcome questionnaire.

RESULTS

GPT demonstrated strong performance on several questions but showed variability overall. In its early iterations it was able to match human judgment occasionally with an accuracy score of 1.00 (n = 20, 17.5%), but it scored as low as 0.26 in some patients. Prompts were adjusted in four subsequent iterations to increase accuracy. In its fourth iteration, agreement was 93.6%, with a maximum agreement of 100% and minimum of 62%. Of 2400 individual items assessed, our model entered 2247 (93.6%) correctly and 153 (6.4%) incorrectly.

CONCLUSIONS

Although our tailored generative model with domain-specific prompt engineering and ontological guidance shows promise for research applications, further refinement is needed to enhance its accuracy. It cannot enter data entirely independently, but it can be employed in tandem with human oversight contributing to a collaborative approach that reduces overall effort.

Understanding How ChatGPT May Become a Clinical Administrative Tool Through an Investigation on the Ability to Answer Common Patient Questions Concerning Ulnar Collateral Ligament Injuries

Background

The consumer availability and automated response functions of chat generator pretrained transformer (ChatGPT-4), a large language model, poise this application to be utilized for patient health queries and may have a role in serving as an adjunct to minimize administrative and clinical burden.

Purpose

To evaluate the ability of ChatGPT-4 to respond to patient inquiries concerning ulnar collateral ligament (UCL) injuries and compare these results with the performance of Google.

Study Design

Cross-sectional study.

Methods

Google Web Search was used as a benchmark, as it is the most widely used search engine worldwide and the only search engine that generates frequently asked questions (FAQs) when prompted with a query, allowing comparisons through a systematic approach. The query "ulnar collateral ligament reconstruction" was entered into Google, and the top 10 FAQs, answers, and their sources were recorded. ChatGPT-4 was prompted to perform a Google search of FAQs with the same query and to record the sources of answers for comparison. This process was again replicated to obtain 10 new questions requiring numeric instead of open-ended responses. Finally, responses were graded independently for clinical accuracy (grade 0 = inaccurate, grade 1 = somewhat accurate, grade 2 = accurate) by 2 fellowship-trained sports medicine surgeons (D.W.A, J.S.D.) blinded to the search engine and answer source.

Results

ChatGPT-4 used a greater proportion of academic sources than Google to provide answers to the top 10 FAQs, although this was not statistically significant (90% vs 50%; P = .14). In terms of question overlap, 40% of the most common questions on Google and ChatGPT-4 were the same. When comparing FAQs with numeric responses, 20% of answers were completely overlapping, 30% demonstrated partial overlap, and the remaining 50% did not demonstrate any overlap. All sources used by ChatGPT-4 to answer these FAQs were academic, while only 20% of sources used by Google were academic (P = .0007). The remaining Google sources included social media (40%), medical practices (20%), single-surgeon websites (10%), and commercial websites (10%). The mean (± standard deviation) accuracy for answers given by ChatGPT-4 was significantly greater compared with Google for the top 10 FAQs (1.9 ± 0.2 vs 1.2 ± 0.6; P = .001) and top 10 questions with numeric answers (1.8 ± 0.4 vs 1 ± 0.8; P = .013).

Conclusion

ChatGPT-4 is capable of providing responses with clinically relevant content concerning UCL injuries and reconstruction. ChatGPT-4 utilized a greater proportion of academic websites to provide responses to FAQs representative of patient inquiries compared with Google Web Search and provided significantly more accurate answers. Moving forward, ChatGPT has the potential to be used as a clinical adjunct when answering queries about UCL injuries and reconstruction, but further validation is warranted before integrated or autonomous use in clinical settings.

A large language model's assessment of methodology reporting in head and neck surgery

OBJECTIVE

The aim of this study was to assess the ability of a Large Language Model - ChatGPT 3.5 to appraise the quality of scientific methodology reporting in head and neck specific scientific literature.

METHODS

Authors asked ChatGPT 3.5 to create a grading system for scientific reporting of research methods. The language model produced a system with a max of 60 points. Individual scores were provided for Study Design and Description, Data Collection and Measurement, Statistical Analysis, Ethical Considerations, and Overall Clarity and Transparency. Twenty articles were selected at random from The American Head and Neck Society's (AHNS) fellowship curriculum 2.0 for interrogation and each 'Methods' section was input into ChatGPT 3.5 for scoring. Analysis of variance (ANOVA) was performed between different scoring categories and a post-hoc tukey HSD test was performed.

RESULTS

Twenty articles were assessed, eight were categorized as very good and nine as good based on cumulative score. Lowest mean score was noted with category of statistical analysis (Mean = 0.49, SD = 0.02). On ANOVA a significant difference between means of the different scoring categories was noted, F(4, 95) = 13.4, p ≤ 0.05. On post-hoc Tukey HSD test, mean scores for categories of data collection (Mean = 0.58, SD = 0.06) and statistical analysis (Mean = 0.49, SD = 0.02) were significantly lower when compared to other categories.

CONCLUSION

This article showcases the feasibility of employing a large language model such as ChatGPT 3.5 to assess the methods sections in head and neck academic writing.

LEVEL OF EVIDENCE: 4

Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review

BACKGROUND AND AIMS

Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low- and middle-income countries (LMICs).

METHODS

In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision-making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.

RESULTS

In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource-constrained LMICs.

CONCLUSION

While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data-sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations

BACKGROUND

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery.

AIM

This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them.

METHODOLOGY

Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties.

RESULTS

The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare.

CONCLUSION

Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.