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Integrating Artificial Intelligence and Human Factors in Healthcare
Dr. Avishek Choudhury, an assistant professor at West Virginia University. Honored with prestigious accolades such as the Paul Kaplan Award, the Fabrycky-Blanchard Award, and the Excellence Doctoral Fellowship, Dr. Choudhury's exemplary academic performance and research excellence are recognized and lauded across the academic spectrum.
Research on News

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  • Mobile health (mHealth) applications have the potential to improve health awareness. This study reports a quasi-controlled intervention to augment maternal health awareness among tribal pregnant mothers through the mHealth application. Households from 2 independent villages with similar socio-demographics in tribal regions of India were selected as intervention (Village A) and control group (Village B). The control group received government mandated programs through traditional means (orally), whereas the intervention group received the same education through mHealth utilization. Postintervention, awareness about tetanus injections and consumption of iron tablets was significantly improved in the intervention group by 55% and 58%, respectively. Awareness about hygiene significantly increased by 57.1%. In addition, mothers in the intervention group who recognized vaginal bleeding, severe abdominal pain, severe blurring of vision, or convulsions as danger signs during pregnancy significantly increased by 18.30%, 23.2%, 20.0%, and 4.90%, respectively. Our study indicates that despite the low literacy of users, mHealth intervention can improve maternal health awareness among tribal communities.

  • Aim

    This study explores the possible impact of wearables on psychological distress and their implications on designs.


    The study conceptualizes and tests two exploratory models by analyzing the US-based Health Information National Trends Survey of 2019 and 2020. Six variants from the databases were used in the study as predictors. We used models 4 and 6 of the Hayes PROCESS macros to test our conceptual parallel and sequential mediation models, respectively.


    The finding indicates significant and negative indirect effects of ‘Use of wearable device’ on ‘Psychological distress.’ In parallel mediation models, ‘self-care’ and ‘health perception’ were noted to be significant mediators. Wearable devices were associated with improved ‘Health perception,’ ‘Self-care,’ and longer ‘workout duration,’, which in turn helped reduce ‘psychological distress’ (better mental health). The sequential mediation model captured the indirect effect of ‘Use of wearable device’ on ‘Psychological distress’ when sequentially mediated by ‘workout duration,’ ‘BMI,’ ‘self-care,’ and ‘health perception’ in the given order.


    As the adoption of digital wearables is increasing due to their growing potential to augment physiological and psychosocial health, it is critical that these technologies are designed to address the needs of users from diverse backgrounds (race, education level, age).

  • Determinants of user mental health are diverse, interrelated, and often multifaceted. This study explores how internet use, perceived care quality, patient education, and patient centered communication influence mental health, using structural equation modeling. Findings suggest that increased internet use even for health purposes negatively impacts mental health (b= -0:087; b= -0:065; P < 0:001). On the other hand, education level, patient centered-communication (PC-Com) and perception of care quality impact mental health positively (b= 0:082; b= 0:146; b= 0:077; P < 0:001; respectively). Moreover, we also explored the changes across various demographics. The influence of patient education on PC-Com was only significant for Hispanic respondents (b= -0:160; P < 0:001). Internet use for health purposes influenced P C-Com negatively for White American respondents (b= -0:047; P = 0:015). The study reinstated that the internet use, patient centered communication, patient education, and perceived care quality might influence mental health. The society will increasingly seek health information from online sources, so our study provides recommendations to make online health information sources more user friendly and trustworthy, ultimately to minimize negative impact on mental health.


    Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.


    We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care.


    PubMed, IEEE Xplore, Cochrane, and Web of Science databases.


    We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality).


    Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report.


    Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n= 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization.


    Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI’s impact by using established implementation frameworks, standardized metrics, and validated outcome measures.

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Awards and certifications
Academic recognitions

Here are some of Dr. Choudhury's most noteworthy accolades.

Stevens Institute of Technology

May 2022

This award is given in recognition of exemplary academic performance, excellence in research & publications, and extraordinary promise in their pursuits beyond program completion to one student in the entire university.

The Paul Kaplan Award

Stevens Insitute of Technology

May 2022

This honor is awarded annually to a Ph.D. candidate in the School of Systems and Enterprises who has distinguished themselves in the conduct of research.

Outstanding Dissertation Award

Stevens Institute of Technology

August 2021

The Stevens Excellence Doctoral Fellowship supports outstanding doctoral students who have achieved candidacy and are actively working on dissertation research and writing. The objective is to support students who are working on dissertations that make original and impactful scholarly contributions.

Stevens Excellence Doctoral Fellowship *

Stevens Institute of Technology

May 2021

Given in recognition for exceptional academic achievement and outstanding contribution byond the classroom.

SSE Exceptional Achievement Award

Stevens Institute of technology

May 2021

Given in recognition for distinguished conduct of scholarly literature.

Best Paper Award

Stevens Institute of Technology

May 2020

Given in recognition for exemplary academic performance, excellence in research, and promise in the study and practice of systems engineering.

Fabrycky-Blanchard Award

Texas Tech University

January 2016

Graduate Student Award for excellence in academic performance.

Graduate Student Award

Tata Trust

July 2015

Study Abroad Scholarship- Nawajbai Ratan Tata Trust (Rs 10,0000.00), India.

Study Abroad Scholarship

A breakdown of his work through his academic career

An assortment of research publications, covering topics such as advancements in Artificial Intelligence and breakthroughs in healthcare.

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Dr. Avishek Choudhury

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