Artificial intelligence in medicine and research – the good, the bad and the ugly

Artificial intelligence (AI) broadly refers to machines that simulate intelligent human behavior, and research into this field is exponential and worldwide, with global players such as Microsoft battling with Google for supremacy and market share. This paper reviews the “good” aspects of AI in medicine for individuals who embrace the 4P model of medicine (Predictive, Preventive, Personalized, and Participatory) to medical assistants in diagnostics, surgery, and research. The “bad” aspects relate to the potential for errors, culpability, ethics, data loss and data breaches, and so on. The “ugly” aspects are deliberate personal malfeasances and outright scientific misconduct including the ease of plagiarism and fabrication, with particular reference to the novel ChatGPT as well as AI software that can also fabricate graphs and images. The issues pertaining to the potential dangers of creating rogue, super‑intelligent AI systems that lead to a technological singularity and the ensuing perceived existential threat to mankind by leading AI researchers are also briefly discussed.peer-reviewe

Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used Bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic Bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons

STROBE, CONSORT, PRISMA, MOOSE, STARD, SPIRIT, and other guidelines – Overview and application

The purpose of research is to seek answers and new knowledge. When conducted properly and systematically, research adds to humanity's corpus of knowledge and hence to our general advancement. However, this is only possible if reported research is accurate and transparent. Guidelines for all the major types of studies (STROBE, CONSORT, PRISMA, MOOSE, STARD, and SPIRIT) have been developed and refined over the years, and their inception, development, and application are briefly discussed in this paper. Indeed, there are currently over 250 of these guidelines for various types of medical research, and these are published by the EQUATOR network. This paper will also briefly review progress in acceptance and adoption of these guidelines

Pathophysiology of a scientific paper

Scientific paper writing for science journals is highly adroit, competitive, and laborious process. Scientific writing has a constant design, which is confounding for apprentice science writers. The huge amount of impediments is associated with scientific writing which may be reduced by applying some practices and guidelines. The basic structure of scientific articles mainly comprises of the title, abstract, keywords, introduction, methods, results, discussion, conclusion, acknowledgments, and references. The pathophysiological aspects which minimize the chances of publication of an academic paper are rarely discussed in the literature. Early career of physicians and researchers is not well acquainted with the components of scientific paper. This study established an approach to understand the basic characteristics of pathophysiology of scientific writing

Impact of unstable environment on the brain drain of highly skilled professionals, healthcare workers, researchers, and research productivity in Pakistan

Background: The geo-strategic position of Pakistan on the world map is incredibly important and idyllic as the country is considered the gateway to central Asia. Pakistan has faced political instability for the last three decades, causing a brain drain and adversely affecting socioeconomic growth. This study aims to investigate the impact of an unstable environment on the brain drain of highly skilled professionals, healthcare workers, researchers, and research productivity in Pakistan from January 2000 to December 2022. Material and Methods: The data were recorded from the World Bank, the Higher Education Commission (HEC) Pakistan, the Pakistan Medical and Dental Council (PMDC), the Bureau of Emigration and Overseas Employment (BEOS), Pakistan, Academic Ranking of World Universities (ARWU), and Web of Science Clarivate Analytics. Initially, 32 documents were selected in this study, and finally, eight fact sheets, official government websites, and international organizations were included. Results: The result revealed that due to political instability, in 2022 about 832,339 highly qualified and accomplished experts headed abroad, among them 17976 (2.15%) were highly qualified and 20865 (2.50%) were highly competent professionals. These include accountants 7197 (0.86%), engineers 6,093 (0.73%), agricultural experts 3,110 (0.37%), doctors 2,464 (0.29%), computer experts 2,147 (0.25%), nurses and paramedics 1768 (0.21%), technicians 23347 (2.80%), electricians 20322 (2.44%), and schools and university faculty 1004 (0.12%). Pakistan has a total of 380 Higher Education Commission-indexed academic journals, among them 11 (2.89%) academic journals were indexed in the Web of Science and 23 journals were placed in the Web of Science emerging indexing. Among these journals, only one journal surpassed the impact factor of more than 2.0. The quartile ranking of Pakistani journals is 01 journal in Q2; 02 in Q3; and the remaining 08 journals in Q4. From August 1947 to December 2022, Pakistan produced a total of 259249 research articles, and from January 2000 to December 2022, the number of articles published was 248457 (95.83%). Since the last 22 years, the trend of research publications was continuously increased; however, the rising trend decreased in 2022 with a declined rate of 1263 (3.42%). Conclusion: The unstable sociopolitical environment in Pakistan caused a brain drain of highly qualified and skilled professionals and impaired the global standing of universities, academic journals, and research productivity in Pakistan. Pakistan must resolve the instability and establish sustainable policies to minimize the brain drain of highly qualified and skilled experts and convalesce their academic institutes and their research productivity for the development of the nation