Interdisciplinary Approaches to Complex Challenges: A Source Guide

Frame the question

This guide examines how interdisciplinary approaches in neuroscience, public health, and AI address complex challenges. Source [1] proposes NeuroAI as a framework for AI advancement through embodied cognition, while [4] highlights digital technologies' role in pandemic management. Source [2] critiques NHST's impact on research validity, and [3] explores immunotherapy biomarkers. These sources collectively address methodological, technological, and clinical challenges across disciplines.

What the evidence shows

Source [1] argues that traditional AI benchmarks like game playing are insufficient, advocating for the embodied Turing test to evaluate AI systems through sensorimotor interactions. This challenges researchers to develop models that mimic biological evolution over 500 million years [1]. Source [2] identifies NHST's flaws, including its role in the replication crisis, and recommends pre-registration, power calculations, and alternative inferential methods. Source [3] details TILs' clinical significance in breast cancer, linking immunogenic infiltration to treatment responses. Source [4] demonstrates how digital technologies like AI, big data, and 5, G transformed pandemic response, with China's large-scale integration cited as a success factor.

Follow the source trail

Source [1] and [4] both emphasize AI's role in solving complex problems, but [1] focuses on biological embodiment while [4] highlights digital infrastructure. Source [2] provides methodological context for [3]'s clinical research, as NHST limitations could affect biomarker validation. The embodied Turing test [1] parallels digital healthcare's need for real-world application [4], while [2]'s critique of NHST underscores the importance of rigorous statistical frameworks in [3]'s clinical trials. These sources collectively show how interdisciplinary approaches require both technical innovation and methodological rigor.

Use these sources well

For an essay, structure around the intersection of AI and public health: use [1] to discuss NeuroAI's embodied cognition framework, [4] to analyze digital pandemic solutions, and [2] to critique statistical methodologies. When discussing [3], pair it with [2] to explore how NHST limitations might impact biomarker validation. Always cite sources verbatim when quoting abstracts, and avoid overstating claims—e.g., note that [4] describes China's success but doesn't claim universal applicability. For follow-up, compare [1]'s embodied Turing test with [4]'s digital healthcare applications.

What to search next

How might the embodied Turing test [1] inform digital healthcare's real-world application [4]? What methodological alternatives to NHST [2] could improve biomarker validation in [3]? How do the statistical challenges in [2] compare to the data fragmentation issues in [4]? What ethical considerations arise from large-scale digital healthcare integration [4]?

Verbatim source abstracts

[1] Catalyzing next-generation Artificial Intelligence through NeuroAI — Nature Communications, 2023-03-22, doi:10.1038/s41467-023-37180-x

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI. [1]

[2] When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment — Frontiers in Human Neuroscience, 2017-08-02, doi:10.3389/fnhum.2017.00390

Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. [2]

[3] The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group — npj Breast Cancer, 2021-12-01, doi:10.1038/s41523-021-00346-1

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC. [3]

[4] Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare — International Journal of Environmental Research and Public Health, 2021-06-04, doi:10.3390/ijerph18116053

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19. [4]

Source dossiers

Reference cards for every cited source, using only verified record metadata.

[1] Catalyzing next-generation Artificial Intelligence through NeuroAI

• Authors: Anthony M. Zador, G. Sean Escola, Blake A. Richards, Bence P. Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri B. Chklovskii, Anne K. Churchland, Claudia Clopath, James J. DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Y. Tsao
• Venue: Nature Communications
• Published: 2023-03-22
• DOI: 10.1038/s41467-023-37180-x
• Citation count: 297
• Institutions: Cold Spring Harbor Laboratory; Columbia University; Montreal Neurological Institute and Hospital; Ontario Brain Institute; Mila - Quebec Artificial Intelligence Institute; McGill University; Harvard University; Stanford University
• Topics: Reinforcement Learning in Robotics, Neural dynamics and brain function, Advanced Memory and Neural Computing, Computer science, Artificial intelligence, Data science
• License/access: cc-by (open access)
• Record: https://doi.org/10.1038/s41467-023-37180-x
• Abstract (verbatim): "Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI." [1]

[2] When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment

• Authors: Dénes Szűcs, John P. A. Ioannidis
• Venue: Frontiers in Human Neuroscience
• Published: 2017-08-02
• DOI: 10.3389/fnhum.2017.00390
• Citation count: 296
• Institutions: University of Cambridge; Stanford Health Care; Innovative Research (United States); Stanford University
• Topics: Meta-analysis and systematic reviews, Statistical Methods in Clinical Trials, Mental Health Research Topics, Null hypothesis, Statistical hypothesis testing, Spurious relationship, Statistical power, Psychology, Replication (statistics), Alternative hypothesis
• License/access: cc-by (open access)
• Record: https://doi.org/10.3389/fnhum.2017.00390
• Abstract (verbatim): "Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out." [2]

[3] The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

• Authors: Khalid El Bairi, Harry R. Haynes, Elizabeth F. Blackley, Susan Fineberg, Jeffrey Shear, Sophia Turner, Juliana Ribeiro de Freitas, Daniel Sur, Luis Claudio Amendola, Masoumeh Gharib, Amine Kallala, Indu Arun, Farid Azmoudeh Ardalan, Luciana Botinelly Mendonça Fujimoto, Luz F. Sua, Shiwei Liu, Huang‐Chun Lien, Pawan Kirtani, Marcelo Balancin, H. El Attar, Prerna Guleria, Wenxian Yang, Emad Shash, I‐Chun Chen, Verónica Bautista, José Fernando do Prado Moura, Bernardo L. Rapoport, Carlos Castaneda, Eunice Spengler, Gabriela Acosta-Haab, Isabel Frahm, Joselyn Sanchez, Miluska Castillo, Najat Bouchmaa, Reena R. Md Zin, Ruohong Shui, Timothy Onyuma, Wentao Yang, Zaheed Husain, Karen Willard‐Gallo, An Coosemans, Edith Perez, Elena Provenzano, Paula Gonzalez Ericsson, Eduardo Richardet, Ravi Mehrotra, Sandra Sarancone, Anna Ehinger, David L. Rimm, John M.S. Bartlett, Giuseppe Viale, Carsten Denkert, Akira I. Hida, Christos Sotiriou, Sibylle Loibl, Stephen M. Hewitt, Sunil Badve, W. Fraser Symmans, Rim S. Kim, Giancarlo Pruneri, Shom Goel, Prudence A. Francis, Gloria Inurrigarro, Rin Yamaguchi, Hernán Garcı́a Rivello, Hugo M. Horlings, Saïd Afqir, Roberto Salgado, Sylvia Adams, Marleen Kok, Maria Vittoria Dieci, Stefan Michiels, Sandra Demaria, Sherene Loi, The International Immuno-Oncology Biomarker Working Group, Khalid El Bairi, Harry R. Haynes, Elizabeth F. Blackley, Susan Fineberg, Jeffrey Shear, Sophia Turner, Juliana Ribeiro de Freitas, Daniel Sur, Luis Claudio Amendola, Masoumeh Gharib, Amine Kallala, Indu Arun, Farid Azmoudeh-Ardalan, Luciana Botinelly Mendonça Fujimoto, Luz F. Sua, Shiwei Liu, Huang‐Chun Lien, Pawan Kirtani, Marcelo Balancin, Hicham El Attar, Prerna Guleria, Wenxian Yang, Emad Shash, I‐Chun Chen, Veronica Bautista
• Venue: npj Breast Cancer
• Published: 2021-12-01
• DOI: 10.1038/s41523-021-00346-1
• Citation count: 284
• Institutions: Mohamed I University; Great Western Hospital; University of Bristol; Peter MacCallum Cancer Centre; Albert Einstein College of Medicine; Montefiore Medical Center; Sidley Austin; Universidade Federal da Bahia
• Topics: Cancer Immunotherapy and Biomarkers, CAR-T cell therapy research, Cancer Genomics and Diagnostics, Oncology, Immunotherapy, Breast cancer, Medicine, Tumor-infiltrating lymphocytes, Biomarker, Internal medicine
• License/access: cc-by (open access)
• Record: https://doi.org/10.1038/s41523-021-00346-1
• Abstract (verbatim): "The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC." [3]

[4] Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

• Authors: Qiang Wang, Min Su, Min Zhang, Rongrong Li
• Venue: International Journal of Environmental Research and Public Health
• Published: 2021-06-04
• DOI: 10.3390/ijerph18116053
• Citation count: 275
• Institutions: China University of Petroleum, East China
• Topics: COVID-19 diagnosis using AI, COVID-19 Digital Contact Tracing, COVID-19 and healthcare impacts, Pandemic, Digital health, Health care, Big data, Cloud computing, Coronavirus disease 2019 (COVID-19), Emerging technologies
• License/access: cc-by (open access)
• Record: https://doi.org/10.3390/ijerph18116053
• Abstract (verbatim): "Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19." [4]