Augmented intelligence: A synergy between man and the machine

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis

Mosquito-borne diseases (MBDs) are a major threat worldwide, and public consultation on these diseases is critical to disease control decision-making. However, traditional public surveys are time-consuming and labor-intensive and do not allow for timely decision-making. Recent studies have explored text analytic approaches to elicit public comments from social media for public health. Therefore, this study aims to demonstrate a text analytics pipeline to identify the MBD topics that were discussed on Twitter and significantly influenced public opinion. A total of 25,000 tweets were retrieved from Twitter, topics were modelled using LDA and sentiment polarities were calculated using the VADER model. After data cleaning, we obtained a total of 6,243 tweets, which we were able to process with the feature selection algorithms. Boruta was used as a feature selection algorithm to determine the importance of topics to public opinion. The result was validated using multinomial logistic regression (MLR) performance and expert judgement. Important issues such as breeding sites, mosquito control, impact/funding, time of year, other diseases with similar symptoms, mosquito-human interaction and biomarkers for diagnosis were identified by both LDA and experts. The MLR result shows that the topics selected by LASSO perform significantly better than the other algorithms, and the experts further justify the topics in the discussion.

Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis

OBJECTIVE

To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models.

MATERIALS AND METHODS

A single-center prospective observational study in a tertiary pediatric hospital in Boston, Massachusetts, United States. Nine emergency department (ED) attending physicians participated in the study. Physicians predicted the likelihood of admission for patients in the ED whose hospitalization disposition had not yet been decided. In parallel, a random-forest computer model was developed to predict hospitalizations from the ED, based on data available within the first hour of the ED encounter. The model was tested on the same cohort of patients evaluated by the participating physicians.

RESULTS

198 pediatric patients were considered for inclusion. Six patients were excluded due to incomplete or erroneous physician forms. Of the 192 included patients, 54 (28%) were admitted and 138 (72%) were discharged. The positive predictive value for the prediction of admission was 66% for the clinicians, 73% for the computer model, and 86% for a hybrid model combining the two. To predict admission, physicians relied more heavily on the clinical appearance of the patient, while the computer model relied more heavily on technical data-driven features, such as the rate of prior admissions or distance traveled to hospital.

DISCUSSION

Computer-generated predictions of patient disposition were more accurate than clinician-generated predictions. A hybrid prediction model improved accuracy over both individual predictions, highlighting the complementary and synergistic effects of both approaches.

CONCLUSION

The integration of computer and clinician predictions can yield improved predictive performance.

Transcending human frailties with technological enhancements and replacements: Transhumanist perspective in nursing and healthcare

As human beings age, they become weak, fragile, and feeble. It is a slowly progressing yet complex syndrome in which old age or some disabilities are not prerequisites; neither does loss of human parts lead to frailty among the physically fit older persons. This paper aims to describe the influences of transhumanist perspectives on human-technology enhancements and replacements in the transcendence of human frailties, including those of older persons, in which technology is projected to deliver solutions toward transcending these frailties. Through technologies including genetic screening and other technological manipulations, intelligent machines and augmented humans improve, maintain, and remedy human-linked susceptibilities. Furthermore, other technologies replace parts fabricated through inorganic-mechanical processes such as 3D-printing. Advancing technologies are reaching the summit of technological sophistication contributing to the transhumanist views of being human in a technological world. Technologies enhance the transcendence of human frailties as essential expressions of the symbiosis between human beings and technology in a transcendental world.

Use of theory to guide development and application of sensor technologies in Nursing

Sensor technologies for health care, research, and consumers have expanded and evolved rapidly. Many technologies developed in commercial or engineering spaces, lack theoretical grounding and scientific evidence to support their need, safety, and efficacy. Theory is a mechanism for synthesizing and guiding knowledge generation for the discipline of nursing, including the design, implementation, and evaluation of sensors and related technologies such as artificial intelligence and machine learning. In this paper, three nurse scientists summarize their presentations at the Council for the Advancement of Nursing Science 2019 Advanced Methods Conference on Expanding Science of Sensor Technology in Research discussing the theoretical underpinnings of sensor technologies development and use in nursing research and practice. Multiple theories with diverse epistemological roots guide decision-making about whether or not to apply sensors to a given use; development of, components of, and mechanisms by which sensor technologies are expected to work; and possible outcomes.