It’s a bit content-heavy and not as graphic-y as I’d like but c’est la vie.
I’m quite proud of what I think is a novel innovation in poster design; the addition of the tl;dr column before the findings. In other words, if you only have 30 seconds to look at the poster then that’s the bit you want to focus on. Related to this, I’ve also moved the Background, Methods and Conclusion sections to the bottom and made them smaller so as to emphasise the Findings, which are placed first.
Reference list (download this list as a Word document)
Yang, C. C., & Veltri, P. (2015). Intelligent healthcare informatics in big data era. Artificial Intelligence in Medicine, 65(2), 75–77. https://doi.org/10.1016/j.artmed.2015.08.002
Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using deep convolutional neural network. Neurocomputing, 266, 8–20. https://doi.org/10.1016/j.neucom.2017.05.025
Li, Z., Zhang, X., Müller, H., & Zhang, S. (2018). Large-scale retrieval for medical image analytics: A comprehensive review. Medical Image Analysis, 43, 66–84. https://doi.org/10.1016/j.media.2017.09.007
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90, 200–205. https://doi.org/10.1016/j.procs.2016.07.014
Ramzan, M., Shafique, A., Kashif, M., & Umer, M. (2017). Gait Identification using Neural Network. International Journal of Advanced Computer Science and Applications, 8(9). https://doi.org/10.14569/IJACSA.2017.080909
Kidziński, Ł., Delp, S., & Schwartz, M. (2019). Automatic real-time gait event detection in children using deep neural networks. PLOS ONE, 14(1), e0211466. https://doi.org/10.1371/journal.pone.0211466
Horst, F., Lapuschkin, S., Samek, W., Müller, K.-R., & Schöllhorn, W. I. (2019). Explaining the Unique Nature of Individual Gait Patterns with Deep Learning. Scientific Reports, 9(1), 2391. https://doi.org/10.1038/s41598-019-38748-8
Cai, T., Giannopoulos, A. A., Yu, S., Kelil, T., Ripley, B., Kumamaru, K. K., … Mitsouras, D. (2016). Natural Language Processing Technologies in Radiology Research and Clinical Applications. RadioGraphics, 36(1), 176–191. https://doi.org/10.1148/rg.2016150080
Jackson, R. G., Patel, R., Jayatilleke, N., Kolliakou, A., Ball, M., Gorrell, G., … Stewart, R. (2017). Natural language processing to extract symptoms of severe mental illness from clinical text: The Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open, 7(1), e012012. https://doi.org/10.1136/bmjopen-2016-012012
Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S. F., … Botsis, T. (2017). Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of Biomedical Informatics, 73, 14–29. https://doi.org/10.1016/j.jbi.2017.07.012
Montenegro, J. L. Z., Da Costa, C. A., & Righi, R. da R. (2019). Survey of Conversational Agents in Health. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2019.03.054
Carrell, D. S., Schoen, R. E., Leffler, D. A., Morris, M., Rose, S., Baer, A., … Mehrotra, A. (2017). Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings. Journal of the American Medical Informatics Association, 24(5), 986–991. https://doi.org/10.1093/jamia/ocx039
Oña, E. D., Cano-de la Cuerda, R., Sánchez-Herrera, P., Balaguer, C., & Jardón, A. (2018). A Review of Robotics in Neurorehabilitation: Towards an Automated Process for Upper Limb. Journal of Healthcare Engineering, 2018, 1–19. https://doi.org/10.1155/2018/9758939
Krebs, H. I., & Volpe, B. T. (2015). Robotics: A Rehabilitation Modality. Current Physical Medicine and Rehabilitation Reports, 3(4), 243–247. https://doi.org/10.1007/s40141-015-0101-6
Leng, M., Liu, P., Zhang, P., Hu, M., Zhou, H., Li, G., … Chen, L. (2019). Pet robot intervention for people with dementia: A systematic review and meta-analysis of randomized controlled trials. Psychiatry Research, 271, 516–525. https://doi.org/10.1016/j.psychres.2018.12.032
Jennifer Piatt, P., Shinichi Nagata, M. S., Selma Šabanović, P., Wan-Ling Cheng, M. S., Casey Bennett, P., Hee Rin Lee, M. S., & David Hakken, P. (2017). Companionship with a robot? Therapists’ perspectives on socially assistive robots as therapeutic interventions in community mental health for older adults. American Journal of Recreation Therapy, 15(4), 29–39. https://doi.org/10.5055/ajrt.2016.0117
Troccaz, J., Dagnino, G., & Yang, G.-Z. (2019). Frontiers of Medical Robotics: From Concept to Systems to Clinical Translation. Annual Review of Biomedical Engineering, 21(1). https://doi.org/10.1146/annurev-bioeng-060418-052502
Riek, L. D. (2017). Healthcare Robotics. ArXiv:1704.03931 [Cs]. Retrieved from http://arxiv.org/abs/1704.03931
Kappassov, Z., Corrales, J.-A., & Perdereau, V. (2015). Tactile sensing in dexterous robot hands — Review. Robotics and Autonomous Systems, 74, 195–220. https://doi.org/10.1016/j.robot.2015.07.015
Choi, C., Schwarting, W., DelPreto, J., & Rus, D. (2018). Learning Object Grasping for Soft Robot Hands. IEEE Robotics and Automation Letters, 3(3), 2370–2377. https://doi.org/10.1109/LRA.2018.2810544
Shortliffe, E., & Sepulveda, M. (2018). Clinical Decision Support in the Era of Artificial Intelligence. Journal of the American Medical Association.
Attema, T., Mancini, E., Spini, G., Abspoel, M., de Gier, J., Fehr, S., … Sloot, P. M. A. (n.d.). A new approach to privacy-preserving clinical decision support systems. 15.
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., … Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(1). https://doi.org/10.1186/s13336-015-0019-3
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11), 1544. https://doi.org/10.1001/jamainternmed.2018.3763
Kliegr, T., Bahník, Š., & Fürnkranz, J. (2018). A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. ArXiv:1804.02969 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1804.02969
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. https://doi.org/10.1371/journal.pone.0174944
Suresh, H., Hunt, N., Johnson, A., Celi, L. A., Szolovits, P., & Ghassemi, M. (2017). Clinical Intervention Prediction and Understanding using Deep Networks. ArXiv:1705.08498 [Cs]. Retrieved from http://arxiv.org/abs/1705.08498
Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689
Verghese, A., Shah, N. H., & Harrington, R. A. (2018). What This Computer Needs Is a Physician: Humanism and Artificial Intelligence. JAMA, 319(1), 19. https://doi.org/10.1001/jama.2017.19198
Any high-quality speech-to-text engines require thousands of hours of voice data to train them, but publicly available voice data is very limited and the cost of commercial datasets is exorbitant. This prompted the question, how might we collect large quantities of voice data for Open Source machine learning?
One of the big problems with the development of AI is that few organisations have the large, inclusive, diverse datasets that are necessary to reduce the inherent bias in algorithmic training. Mozilla’s Common Voice project is an attempt to create a large, multilanguage dataset of human voices with which to train natural language AI.
This is why we built Common Voice. To tell the story of voice data and how it relates to the need for diversity and inclusivity in speech technology. To better enable this storytelling, we created a robot that users on our website would “teach” to understand human speech by speaking to it through reading sentences.
I think that voice and audio is probably going to be the next compter-user interface so this is an important project to support if we want to make sure that Google, Facebook, Baidu and Tencent don’t have a monopoly on natural language processing. I see this project existing on the same continuum as OpenAI, which aims to ensure that “…AGI’s benefits are as widely and evenly distributed as possible.” Whatever you think about the possibility of AGI arriving anytime soon, I think it’s a good thing that people are working to ensure that the benefits of AI aren’t mediated by a few gatekeepers whose primary function is to increase shareholder value.
Most of the data used by large companies isn’t available to the majority of people. We think that stifles innovation. So we’ve launched Common Voice, a project to help make voice recognition open and accessible to everyone. Now you can donate your voice to help us build an open-source voice database that anyone can use to make innovative apps for devices and the web. Read a sentence to help machines learn how real people speak. Check the work of other contributors to improve the quality. It’s that simple!
The datasets are openly licensed and available for anyone to download and use, alongside other open language datasets that Mozilla links to on the page. This is an important project that everyone should consider contributing to. The interface is intuitive and makes it very easy to either submit your own voice or to validate the recordings that other people have made. Why not give it a go?
For all the promise that digital records hold for making the system more efficient—and the very real benefit these records have already brought in areas like preventing medication errors—EMRs aren’t working on the whole. They’re time consuming, prioritize billing codes over patient care, and too often force physicians to focus on digital recordkeeping rather than the patient in front of them.
I’ve read some physicians can spend up to 60% of their day capturing patient information in the EHR. And this isn’t because there’s a lot of information. It’s often down to confusing user interfaces, misguided approaches to security (e.g. having to enter multiple different passwords and a lack of off-site access), and poor design that results in physicians capturing more information than necessary.
There’s interest in using natural language processing to analyse recorded conversation between clinicians and colleagues/patients and while the technology is still unsuitable for mainstream use, it seems likely that it will continue improving until it is.
“We essentially gathered hateful tweets and used language processing to find the other terms that were associated with such messages… We learned these terms and used them as the bridge to new terms—as long as we have those words, we have a link to anything they can come up with.” This defeats attempts to conceal racist slurs using codes by targeting the language that makes up the cultural matrix from which the hate emerges, instead of just seeking out keywords. Even if the specific slurs used by racists change in order to escape automated comment moderation, the other terms they use to identify themselves and their communities likely won’t.
There are a few things I thought are worth noting:
The developers of this algorithm used Tweets to identify the hateful language, which says something about the general quality of discourse on Twitter.
The algorithm isn’t simply substituting one set of keywords with another; it identifies the context of the sentence in order to determine if the sentiment is hateful. The specific words almost don’t matter. This is a significant step in natural language processing.
The post appeared in 2017 so it’s a year old and I haven’t looked to see what (if any) progress has been made since then.
…even if we stopped at today’s AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including:
1) Mass labor displacement, unemployment, and inequality; 2)The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order; 3)Imagery intelligence and other mechanisms for revealing most of the ballistic missile-carrying submarines that countries rely on to be able to respond to nuclear attack; 4)Ubiquitous sensors and algorithms that can identify individuals through face recognition, leading to universal surveillance; 5)Autonomous weapons with an independent chain of command, making it easier for authoritarian regimes to violently suppress their citizens.
This is one of those things that isn’t intuitive but at the same time is obviously true. Even if all we do going forward is improve what we already have (e.g. cheaper, faster, more powerful computation, sensors, etc.) we could brute force our way to a vastly different society. It’s easy to make fun of all the ways that self-driving cars, natual language processing, and recommendation systems aren’t as good as humans. But think about the fact that we have self-driving cars, NLP and recommendation systems. These things may not be perfect today but they didn’t exist 10 years ago. In a decade we’ve gone from, “This is impossible”, to “This isn’t perfect”. Unless technological development comes to a complete standstill (note: this would require some kind of apocalyptic event), machine learning by itself will transform society using nothing more advanced than larger data sets and more powerful computation.