Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study.

Background: The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation. Objective: This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation. Methods: An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022. Results: A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation (P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians (P=.008). Conclusions: Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.

Quantitative assessment of pilot-endured workloads during helicopter flying emergencies: an analysis of physiological parameters during an autorotation.

The procedures to be performed after sudden engine failure of a single-engine helicopter impose high workload on pilots. The maneuver to regain aircraft control and safe landing is called autorotation. The safety limits to conduct this maneuver are based on the aircraft height versus speed diagram, which is also known as "Dead Man's Curve". Flight-test pilots often use subjective methods to assess the difficulty to conduct maneuvers in the vicinity of this curve. We carried out an extensive flight test campaign to verify the feasibility of establishing quantitative physiological parameters to better assess the workload endured by pilots undergoing those piloting conditions. Eleven pilots were fully instrumented with sensors and had their physiological reactions collected during autorotation maneuvers. Our analyses suggested that physiological measurements (heart rate and electrodermal activity) can be successfully recorded and useful to capture the most effort-demanding effects during the maneuvers. Additionally, the helicopter's flight controls displacements were also recorded, as well as the pilots' subjective responses evaluated by the Handling Qualities Rate scale. Our results revealed that the degree of cognitive workload was associated with the helicopter's flight profile concerning the Height-Speed diagram and that the strain intensity showed a correlation with measurable physiological responses. Recording flight controls displacement and quantifying the pilot's subjective responses show themselves as natural effective candidates to evaluate the intensity of cognitive workload in such maneuvers.

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists' diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.

The Neurobiology of Zika Virus: New Models, New Challenges.

The Zika virus (ZIKV) attracted attention due to one striking characteristic: the ability to cross the placental barrier and infect the fetus, possibly causing severe neurodevelopmental disruptions included in the Congenital Zika Syndrome (CZS). Few years after the epidemic, the CZS incidence has begun to decline. However, how ZIKV causes a diversity of outcomes is far from being understood. This is probably driven by a chain of complex events that relies on the interaction between ZIKV and environmental and physiological variables. In this review, we address open questions that might lead to an ill-defined diagnosis of CZS. This inaccuracy underestimates a large spectrum of apparent normocephalic cases that remain underdiagnosed, comprising several subtle brain abnormalities frequently masked by a normal head circumference. Therefore, new models using neuroimaging and artificial intelligence are needed to improve our understanding of the neurobiology of ZIKV and its true impact in neurodevelopment.

Brain connectivity in patients with dystonia during motor tasks.

OBJECTIVE: This study aims to investigate alterations of brain connectivity using multivariate electroencephalographic data to provide new insights of the brain connectivity dynamics of dystonia. APPROACH: We recorded electroencephalography (EEG) of patients with right upper limb idiopathic focal dystonia and paired controls during resting state, writing-from-memory, and finger-tapping tasks. We applied power spectrum analyses considering the mu, beta and gamma rhythms of the motor cortex and analyzed brain connectivity networks and microstates (MS). MAIN RESULTS: The power spectra results showed that patients had a loss of desynchronization of the beta rhythm during the writing task. We observed differences in the structure of the connective core in beta rhythm, as well as, in the intensity of the patient's hubs observed with basis in path length measures in mu and beta rhythms. Abnormalities were also identified in MS of default mode networks of patients associated with its performances during motor tasks. SIGNIFICANCE: The EEG connectivity analyses provided interesting insights about the cortical electrophysiological patterns in dystonia, such as loss of event-related desynchronization, changes in the effective connectivity with similar signature to other neurological diseases, indications of alterations in the default-mode-network. Our findings are consistent with previously described connectivity abnormalities in neuroimaging studies confirming that dystonia is a network disorder.

Spectral characteristics of the hippocampal LFP during contextual fear conditioning.

OBJECTIVE: The hippocampus has an important role in the acquisition and recall of aversive memories. The objective of this study was to investigate the relationship among hippocampal rhythms. METHODS: Microeletrodes arrays were implanted in the hippocampus of Wistar rats. The animals were trained and tested in a contextual fear conditioning task. The training consisted in applying shocks in the legs. The memory test was performed 1 day (recent memory) or 18 days (remote memory) after training. We proposed a measure based on the FFT power spectrum, denominated "delta-theta ratio", to characterize the different behaviors (active exploration and freezing) and the memories types. RESULTS: The delta-theta ratio was able to distinguish recent and remote memories. In this study, the ratio for the 18-day group was smaller than for the 1-day group. Moreover, this measure was useful to distinguish the different behavior states - active exploration and freezing. CONCLUSIONS: The results suggest delta-theta oscillations could reflect the demands on information processing during recent and remote memory recalls.

Spectral characteristics of the hippocampal LFP during contextual fear conditioning / Características espectrais da atividade elétrica hipocampal durante o medo condicionado ao contexto

Objective: The hippocampus has an important role in the acquisition and recall of aversive memories. The objective of this study was to investigate the relationship among hippocampal rhythms. Methods: Microeletrodes arrays were implanted in the hippocampus of Wistar rats. The animals were trained and tested in a contextual fear conditioning task. The training consisted in applying shocks in the legs. The memory test was performed 1 day (recent memory) or 18 days (remote memory) after training. We proposed a measure based on the FFT power spectrum, denominated "delta-theta ratio", to characterize the different behaviors (active exploration and freezing) and the memories types. Results: The delta-theta ratio was able to distinguish recent and remote memories. In this study, the ratio for the 18-day group was smaller than for the 1-day group. Moreover, this measure was useful to distinguish the different behavior states - active exploration and freezing. Conclusions: The results suggest delta-theta oscillations could reflect the demands on information processing during recent and remote memory recalls.

Objetivo: O hipocampo tem um importante papel na aquisição e evocação das memórias aversivas. Pretendeu-se, neste trabalho, investigar a dinâmica entre os ritmos hipocampais durante a evocação das memórias aversivas recente e remota. Métodos: Foram implantadas matrizes de microeletrodos em ratos Wistar. Os animais foram treinados e testados numa tarefa de condicionamento contextual de medo. O treino consistiu em aplicações de choques nas patas. O teste de memória foi realizado 1 dia (memória recente) ou 18 dias (memória remota) após o treino. Nesse trabalho, foi proposta uma medida baseada no espectro de potências, denominada "razão delta-theta", para caracterização dos diferentes comportamentos observados (vigília exploratória e congelamento) e tipos de memórias. Resultados: A razão delta-theta mostrou-se capaz de distinguir os grupos testados para as memórias recente e remota. Neste caso, observou-se, para o grupo testado 18 dias após, um valor menor do que para o grupo testado 1 dia após. Além disso, essa medida mostrou-se útil para distinguir os diferentes estados comportamentais. Conclusões: Os resultados sugerem que a dinâmica entre os ritmos delta e theta pode refletir o processamento da informação durante a evocação das memórias recente e remota.