Case Study - Final

       Google, in full Google LLC formerly Google Inc. (1998–2017), American search engine company, founded in 1998 by Sergey Brin and Larry Page, that is a subsidiary of the holding company Alphabet Inc. More than 70 percent of worldwide online search requests are handled by Google, placing it at the heart of most Internet users’ experience. Google has always seen itself as more than a search engine and advertising company. Now it’s turning its focus to healthcare, betting that its AI prowess can create a powerful new paradigm for the detection, diagnosis, and treatment of disease. In short, Google seems to be going after the healthcare space from every possible angle. Google is betting that the future of healthcare is going to be structured data and AI. The company is applying AI to disease detection, new data infrastructure, and potentially insurance. "Tomorrow, if AI can shape healthcare, it has to work through the regulations of healthcare… In fact, I see that as one of the biggest areas is where the benefits will play out for the next 10–20 years." said Sundar Pichai, CEO of Google. In this study we explore Google’s many healthcare initiatives and areas of potential future expansion. Furthermore, we will discuss the joining of an AI company called "DeepMind" to Google health (Wikipedia contributors, 2021). In short, DeepMind is dedicated to artificial intelligence research. One of its main initiatives is finding ways AI can be applied to healthcare. DeepMind was acquired by Google for $500M+ and is run by Demis Hassabis (Wikipedia contributors, 2021). The company is based in London and works closely with National Health Service institutions.

    DeepMind, a UK-based company, founded in September 2010, is teaming up with Google Health to tap into expertise in areas like artificial intelligence, app development, data security, and cloud storage. For the last three years, DeepMind has built a team to tackle complex problems in healthcare. Alongside teams at Google, DeepMind will work to build products that support care teams and improve patient outcomes. "During my time working in the UK National Health Service (NHS) as a surgeon and researcher, I saw first-hand how technology could help, or hinder, the important work of nurses and doctors," said Dominic King, UK Site Lead at Google Health. DeepMind and Google Health will aim to develop tools that could potentially help prevent sepsis and acute kidney injury. By joining forces with Google Health, DeepMind will add to its many projects aimed at improving patient care. The company recently developed a clinical decision support tool that can accurately identify more than 50 eye diseases. With this new collaboration, both DeepMind and Google Health will continue to improve patient care. "I know DeepMind is proud of our healthcare work to date. With the expertise and reach of Google behind us, we'll now be able to develop tools and technology capable of helping millions of patients around the world," said Dominic King. 

    Breast cancer kills roughly 11,500 people in the UK every year (Cancer Research UK, 2020). Google Health and DeepMind have created an AI tool capable of spotting breast cancer with as much accuracy as a human radiologist. DeepMind has suggested that the AI tool could be used as an automated "second reader" to assist radiologists. In the UK it is already standard procedure for two radiologists to view a scan. DeepMind claims to have run a simulation which indicated using the AI could alleviate the work of second readers by 88% (McKinney, Sieniek and Godbole, 2020). This is far from DeepMind's first foray into healthcare. The company has previously developed AI models for identifying eye diseases and detecting neck cancer. We will discuss these models in-depth later on.

    Using AI to tackle disease, from monitoring, to detection, to lifestyle management. Google’s strategy involves an end-to-end approach to healthcare, including:

• Data generation — This includes digitising and ingesting data produced by wearables, imaging, and MRIs among other methods. This data stream is critical to AI-driven anomaly detection.
• Disease detection — Using AI to detect anomalies in a given dataset that might signal the presence of some disease.
• Disease/lifestyle management — These tools help people who have been diagnosed with a disease or are at risk of developing one go about their day-to-day lives and/or make positive lifestyle modifications

DeepMind is involved in several parts of disease detection and Google itself holds several patents under the parent company.

    Google's DeepMind artificial intelligence research company is developing a clinical decision support (CDS) tool for identifying eye diseases. Researchers from DeepMind are progressing towards a clinical decision support product that can accurately identify more than fifty eye diseases and provide treatment recommendations for patients. The company is working with the Moorfields Eye Hospital in the UK to improve on this prior eye disease research and assist eye doctors in assessing the risk of a patient’s eye problem and directing them to medical care based on the urgency of the condition. "Currently, eye care professionals use optical coherence tomography (OCT) scans to help diagnose eye conditions. These 3D images provide a detailed map of the back of the eye, but they are hard to read and need expert analysis to interpret," explained DeepMind. Using deep learning, the research team is able to automatically detect relevant eye features in seconds, and can flag patients at high risk of serious complications to bring their cases to the front of the diagnostic queue. Initial results, published in Nature Medicine, show that the deep learning tool developed by DeepMind and Moorfields Eye Hospital can identify, diagnose, and recommend care for common clinical eye conditions as accurately as human clinicians (De Fauw, Ledsam and Romera-Paredes, 2018). The algorithm, which is yet to be commercialised, aims to reduce the time of diagnosis for conditions that threaten permanent loss of sight, allowing providers to treat patients or refer them to the correct specialist more quickly. While speed is important, delivering trustworthy and transparent results is an equally critical goal (DeepMind, 2018). 

    In addition, The team has focused specifically on avoiding the creation of a “black box” system that does not allow users and patients to understand the decision-making pathways behind a recommendation (DeepMind, 2018). By using a dual layered neural network, DeepMind and Moorfields researchers hope to provide adequate explanation – and trustworthy confidence intervals – to supplement the clinical decision support delivery process (DeepMind, 2018). “The first neural network, known as the segmentation network, analyses the OCT scan to provide a map of the different types of eye tissue and the features of disease it sees, such as hemorrhages, lesions, irregular fluid or other symptoms of eye disease,” the team said. The tool has not yet been put through its paces with clinical trials, and has not received regulatory approval that would allow it to be used in the clinical setting just yet. However, the researchers are confident that the preliminary results are a positive step towards developing a product that could alter the way eye care providers diagnose and treat their patients.

Source: DeepMind / Moorfields

    As Google enters healthcare, it’s leaning heavily on its expertise in AI. Health data is getting digitised  and structured, from a new electronic record standard to imaging to DNA sequencing. Google is both helping speed up this process by creating new means of ingesting health data and betting that it can use AI to make sense of the data quickly and potentially more accurately than current methods. Among the big 5 tech giants (Facebook, Apple, Microsoft, Google, Amazon), Google emphasis sizes its progress on machine learning much more than the rest. In my point of view, I strongly believe that Google has a big potential to develop a lot of fields in the future, especially healthcare. Unfortunately, there are limitations to this study. For instance, I was not able to contact the mentioned companies to gain more information to backup this study. In addition, the reviewed methods has not been put through clinical trials yet. So, there is no enough studies and secondary data about it. In the future, we can explore more of Google's partner companies same as DeepMind. Moreover, we can explore more methods in detail, perhaps we can explore AI and healthcare companies other than Google, and put these companies in contrast and analyse the results.

    To sum up, this study illustrates that AI in healthcare is being developed by a lot of companies as well as it is rich of benefits in this field because it saves money, effort, and time. Google health and DeepMind is a perfect example for developing the sense to manage many critical health related conditions. In my point of view, AI and ML must be utilised more in these fields to reach a level of high-tech support and services. Lastly, after doing this study, it is clear that the positive effects of utilising AI and ML in healthcare services overweight the negative effects.

References:

• Wikipedia contributors, (2021). DeepMind. [online] Available at: <https://en.wikipedia.org/wiki/DeepMind> [Accessed 3 December 2021].
• De Fauw, J., Ledsam, J.R., Romera-Paredes, B., (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24, 1342–1350. <https://doi.org/10.1038/s41591-018-0107-6>
• DeepMind, (2018). <https://deepmind.com/blog/article/moorfields-major-milestone> Accessed [December] [2021]
• McKinney, S.M., Sieniek, M., Godbole, V., (2020). International evaluation of an AI system for breast cancer screening. Nature 577, 89–94. <https://doi.org/10.1038/s41586-019-1799-6>
• Cancer Research UK, (2020). <https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer#heading-Two>, Accessed [December] [2021]