Publications

@article{purkayastha2023evaluating,

title = {Evaluating user acceptance of an open-source mobile app for hospital price transparency rule},

author = {Saptarshi Purkayastha and Regina Merine and Vyona Dsouza and Pallavi Singh and Judy Gichoya},

year  = {2023},

date = {2023-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kathiravelu2023telehealth,

title = {The Telehealth Dilemma\textemdashHealth-Care Deserts Meet the Internet’s Remote Regions},

author = {Pradeeban Kathiravelu and Dalibor Fonovi\'{c} and Tihana Galinac Grbac and Zachary Zaiman and Lu\'{i}s Veiga and Judy Wawira Gichoya and Saptarshi Purkayastha and Babak Mahmoudi},

year  = {2023},

date = {2023-01-01},

journal = {Computer},

volume = {56},

number = {9},

pages = {39\textendash49},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{khairat2023foundational,

title = {Foundational domains and competencies for baccalaureate health informatics education},

author = {Saif Khairat and Sue S Feldman and Arif Rana and Mohammad Faysel and Saptarshi Purkayastha and Matthew Scotch and Christina Eldredge},

year  = {2023},

date = {2023-01-01},

journal = {Journal of the American Medical Informatics Association},

pages = {ocad147},

publisher = {Oxford University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{burns2023ability,

title = {Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts},

author = {John Lee Burns and Zachary Zaiman and Jack Vanschaik and Gaoxiang Luo and Le Peng and Brandon Price and Garric Mathias and Vijay Mittal and Akshay Sagane and Christopher Tignanelli and others},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Medical Imaging},

volume = {10},

number = {6},

pages = {061106\textendash061106},

publisher = {Society of Photo-Optical Instrumentation Engineers},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{banerjee2023shortcuts,

title = {“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation.},

author = {Imon Banerjee and Kamanasish Bhattacharjee and John L Burns and Hari Trivedi and Saptarshi Purkayastha and Laleh Seyyed-Kalantari and Bhavik N Patel and Rakesh Shiradkar and Judy Gichoya},

year  = {2023},

date = {2023-01-01},

journal = {Journal of the American College of Radiology},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{venkatayogi2023seeing,

title = {From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings},

author = {Nethra Venkatayogi and Maanas Gupta and Alaukik Gupta and Shreya Nallaparaju and Nithya Cheemalamarri and Krithika Gilari and Shireen Pathak and Krithik Vishwanath and Carel Soney and Tanisha Bhattacharya and others},

year  = {2023},

date = {2023-01-01},

journal = {Applied Sciences},

volume = {13},

number = {14},

pages = {8427},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{guo2023medshift,

title = {MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation},

author = {Xiaoyuan Guo and Judy Wawira Gichoya and Hari Trivedi and Saptarshi Purkayastha and Imon Banerjee},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{li2023ethics,

title = {Ethics of large language models in medicine and medical research},

author = {Hanzhou Li and John T Moon and Saptarshi Purkayastha and Leo Anthony Celi and Hari Trivedi and Judy W Gichoya},

year  = {2023},

date = {2023-01-01},

journal = {The Lancet Digital Health},

volume = {5},

number = {6},

pages = {e333\textendashe335},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tummala2022development,

title = {Development and evaluation of a natural language conversational bot for identifying appropriate clinician referral from patient narratives},

author = {Sriharsha Tummala and Saptarshi Purkayastha and Josette Jones},

year  = {2022},

date = {2022-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gichoya2022ai,

title = {AI recognition of patient race in medical imaging: a modelling study},

author = {Judy Wawira Gichoya and Imon Banerjee and Ananth Reddy Bhimireddy and John L Burns and Leo Anthony Celi and Li-Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih-Cheng Huang and others},

year  = {2022},

date = {2022-01-01},

journal = {The Lancet Digital Health},

volume = {4},

number = {6},

pages = {e406\textendashe414},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ochoa2022ntd,

title = {NTD Health: An electronic medical record system for neglected tropical diseases},

author = {Rodrigo Ochoa and Alessa \'{A}lvarez and Jordan Freitas and Saptarshi Purkayastha and Iv\'{a}n D V\'{e}lez},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Biom\'{e}dica},

volume = {42},

number = {4},

pages = {602\textendash610},

publisher = {Instituto Nacional de Salud},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{guo2022multi,

title = {Multi-label Medical Image Retrieval via Learning Multi-class Similarity},

author = {Xiaoyuan Guo and Jiali Duan and Judy Gichoya and Hari Trivedi and Saptarshi Purkayastha and Ashish Sharma and Imon Banerjee},

year  = {2022},

date = {2022-01-01},

publisher = {AIIM-D-22-00928},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ezenwa2022using,

title = {Using mobile virtual reality simulation to prepare for in-person helping babies breathe training: Secondary analysis of a randomized controlled trial (the eHBB/mHBS trial)},

author = {Beatrice Nkolika Ezenwa and Rachel Umoren and Iretiola Bamikeolu Fajolu and Daniel S Hippe and Sherri Bucher and Saptarshi Purkayastha and Felicitas Okwako and Fabian Esamai and John B Feltner and Olubukola Olawuyi and others},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {JMIR Medical Education},

volume = {8},

number = {3},

pages = {e37297},

publisher = {JMIR Publications Inc., Toronto, Canada},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{guo2021cvad,

title = {CVAD/An unsupervised image anomaly detector},

author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},

doi = {10.1016/j.simpa.2021.100195},

year  = {2021},

date = {2021-12-22},

urldate = {2021-01-01},

journal = {Software Impacts},

pages = {100195},

publisher = {Elsevier},

abstract = {Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector , which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mahajan2021plhi,

title = {PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor},

author = {Yohan Mahajan and Ananth Bhimireddy and Areeba Abid and Judy W Gichoya and Saptarshi Purkayastha},

doi = {10.1016/j.dib.2021.107287},

year  = {2021},

date = {2021-10-01},

urldate = {2021-01-01},

journal = {Data in Brief},

volume = {38},

pages = {107287},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{umoren2021ehbbb,

title = {eHBB: a randomised controlled trial of virtual reality or video for neonatal resuscitation refresher training in healthcare workers in resource-scarce settings},

author = {Rachel Umoren and Sherri Bucher and Daniel S Hippe and Beatrice Nkolika Ezenwa and Iretiola Bamikeolu Fajolu and Felicitas M Okwako and John Feltner and Mary Nafula and Annet Musale and Olubukola A Olawuyi and others},

doi = {10.1136/bmjopen-2020-048506},

year  = {2021},

date = {2021-08-25},

urldate = {2021-01-01},

journal = {BMJ open},

volume = {11},

number = {8},

pages = {e048506},

publisher = {British Medical Journal Publishing Group},

abstract = {Objective To assess the impact of mobile virtual reality (VR) simulations using electronic Helping Babies Breathe (eHBB) or video for the maintenance of neonatal resuscitation skills in healthcare workers in resource-scarce settings.



Design Randomised controlled trial with 6-month follow-up (2018\textendash2020).



Setting Secondary and tertiary healthcare facilities.



Participants 274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.



Intervention(s) eHBB VR simulation or neonatal resuscitation video.



Main outcome(s) Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.



Results Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (−15% VR, p=0.10; −21% video, p\<0.01, \textendash27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (−21% at 3 months, p\<0.001; −14% at 6 months, p=0.066) and control groups (−7% at 3 months, p=0.43; −14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.



Conclusion eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kathiravelu2021dicom,

title = {A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images},

author = {Pradeeban Kathiravelu and Puneet Sharma and Ashish Sharma and Imon Banerjee and Hari Trivedi and Saptarshi Purkayastha and Priyanshu Sinha and Alexandre Cadrin-Chenevert and Nabile Safdar and Judy Wawira Gichoya},

doi = {10.1007/s10278-021-00491-w},

year  = {2021},

date = {2021-08-01},

urldate = {2021-01-01},

journal = {Journal of Digital Imaging},

volume = {34},

number = {4},

pages = {1005--1013},

publisher = {Springer International Publishing},

abstract = {Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{guo2021margin,

title = {Margin-Aware Intra-Class Novelty Identification for Medical Images},

author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},

url = {https://arxiv.org/abs/2108.00117},

year  = {2021},

date = {2021-07-31},

urldate = {2021-07-31},

journal = {arXiv preprint arXiv:2108.00117},

abstract = {Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For example, a machine learning model trained with normal chest X-ray and common lung abnormalities, is expected to discover and flag idiopathic pulmonary fibrosis which a rare lung disease and unseen by the model during training. The nuances from intra-class variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. To tackle the challenges, we propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) which without any out-of-distribution training data, performs novelty identification by combining both autoencoder-based and classifier-based method. With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs. To enhance the separation, a distance objective is optimized to enforce a margin between the two classes. Extensive experimental results on both natural image datasets and medical image datasets are presented and our method out-performs state-of-the-art approaches. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{banerjee2021reading,

title = {Reading Race: AI Recognises Patient's Racial Identity In Medical Images},

author = {Imon Banerjee and Ananth Reddy Bhimireddy and John L Burns and Leo Anthony Celi and Li-Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih-Cheng Huang and Po-Chih Kuo and others},

url = {https://arxiv.org/abs/2107.10356},

year  = {2021},

date = {2021-07-21},

urldate = {2021-07-21},

journal = {arXiv preprint arXiv:2107.10356},

abstract = {Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images.

Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.

Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study.

Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tariq2021patient,

title = {Patient-specific COVID-19 resource utilization prediction using fusion AI model},

author = {Amara Tariq and Leo Anthony Celi and Janice M Newsome and Saptarshi Purkayastha and Neal Kumar Bhatia and Hari Trivedi and Judy Wawira Gichoya and Imon Banerjee},

doi = {10.1038/s41746-021-00461-0},

year  = {2021},

date = {2021-06-03},

urldate = {2021-01-01},

journal = {NPJ digital medicine},

volume = {4},

number = {1},

pages = {1--9},

publisher = {Nature Publishing Group},

abstract = {The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1\textendash86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{purkayastha2021failuresb,

title = {Failures hiding in success for artificial intelligence in radiology},

author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},

doi = {10.1016/j.jacr.2020.11.008},

year  = {2021},

date = {2021-03-01},

urldate = {2021-01-01},

journal = {Journal of the American College of Radiology},

volume = {18},

number = {3},

pages = {517--519},

publisher = {Elsevier},

abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans [ 1

]. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{purkayastha2021failures,

title = {Failures hiding in success for artificial intelligence in radiology},

author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},

doi = {10.1016/j.jacr.2020.11.008},

year  = {2021},

date = {2021-03-01},

journal = {Journal of the American College of Radiology},

volume = {18},

number = {3},

pages = {517-519},

abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, the successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently. },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tariq2020current,

title = {Current clinical applications of artificial intelligence in radiology and their best supporting evidence},

author = {Amara Tariq and Saptarshi Purkayastha and Geetha Priya Padmanaban and Elizabeth Krupinski and Hari Trivedi and Imon Banerjee and Judy Wawira Gichoya},

doi = {10.1016/j.jacr.2020.08.018},

year  = {2020},

date = {2020-11-02},

urldate = {2020-11-02},

journal = {Journal of the American College of Radiology},

volume = {17},

number = {11},

pages = {1371-1381},

publisher = {Elsevier},

abstract = {Purpose

Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.



Methods

A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.



Results

There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.



Conclusions

Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bucher2020digital,

title = {Digital health innovations, tools, and resources to support Helping Babies Survive programs},

author = {Sherri L. Bucher and Peter Cardellichio and Naomi Muinga and Jackie K. Patterson and Anu Thukral and Ashok K. Deorari and Santorino Data and Rachel Umoren and Saptarshi Purkayastha},

url = {https://pediatrics.aappublications.org/content/146/Supplement_2/S165},

doi = {10.1542/peds.2020-016915I},

issn = {0031-4005},

year  = {2020},

date = {2020-10-01},

journal = {Pediatrics},

volume = {146},

number = {Supplement 2},

pages = {S165-S182},

abstract = {The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mathur2020artificial,

title = {Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care},

author = {Varoon Mathur and Saptarshi Purkayashtha and Judy Wawira Gichoya},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2005.12378},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{banerjee2020there,

title = {Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications},

author = {Imon Banerjee and Priyanshu Sinha and Saptarshi Purkayastha and Nazanin Mashhaditafreshi and Amara Tariq and Jiwoong Jeong and Hari Trivedi and Judy W Gichoya},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2006.13262},

keywords = {},

pubstate = {published},

tppubtype = {article}

}