healthcare
Project Phoenix
An AI Platform that Predicts & Prevents Pandemics Worldwide. Please take a look at our proposed SAAS Application Prototype, showcased below.
About The Project
The Problem
The COVID-19 pandemic has been an unprecedented challenge. The worst public health emergency for a century has had a profound impact on the NHS. Staff have treated more than half a million COVID-19 patients over the last 18 months in hospital alone. The pandemic has illuminated chronic problems in our health and social care system and made many of them worse. For instance, when COVID-19 broke out, there were thousands of hospital beds filled with people who could have been better cared for elsewhere. The number of NHS patients waiting for tests, surgery and routine treatment in England is at a record high of 5.5 million and could potentially reach 13 million over the next few years. Health services in other parts of the UK have faced similar challenges. [Building Back Better: Our Plan for Health and Social Care. Presented to Parliament by the Prime Minister by Command of Her Majesty, 2021] |
Focus on innovation: The UK government and Healthcare institutions are actively exploring AI applications in Healthcare. This creates a market for AI-powered tools and services. Serious illnesses, viruses, bacteria, and outbreaks like pandemics and epidemics can strike anyone, regardless of age, race, or physical health. New and old infectious threats are constantly emerging, putting a strain on regions with limited healthcare resources. Vaccine-preventable diseases like meningococcal disease, yellow fever, and cholera can be particularly devastating in these areas where timely detection and response are difficult. SynthoSense wants to make an important contribution to the health and well-being of the nation, driving more efficient and effective health for all across the UK. To combat these challenges, international efforts are underway, including To Eliminate Yellow Fever Epidemics strategy (2017-2026). Ending Cholera: A Global Roadmap to 2030. The Pandemic Influenza Preparedness (PIP) Framework. The Global Strategy for Influenza (2018-2030). It’s important to note that this list focuses on infectious diseases, and there are other global health concerns like the mental health crisis in the UK and the ongoing fight against malaria in Africa. https://www.who.int/activities/preparing-and-preventing-epidemics-and-pandemics/ |
To solve this problem, Artificial Intelligence offers various techniques in applying AI to healthcare, which can considerably change the effect of any pandemic in the UK and/or globally. AI can change our future in healthcare.
By partnering and working alongside The WHO, The Alan Turing Institute, Imperial College and other AI companies in HealthCare we can build a first-of-its-kind HealthCare Neural Network which would put the United Kingdom at the forefront of HealthCare Technology.
The stages of applying AI in HealthCare to predict and prevent pandemics:
Early Detection and Surveillance of Data: AI can analyse an immense set of data from medical records in a very short time, social media and news reports and identify patterns and predict disease outbreaks much faster than traditional methods. By analysing these diverse sources, AI can identify patterns and trends that might be missed by traditional methods.
The Project
Solving The Problem
Here’s how:
Real-time analysis: AI can continuously monitor these data streams, allowing for much faster detection of potential outbreaks compared to waiting for official reports.
Pattern recognition: AI algorithms can identify subtle patterns in the data, such as increases in specific keywords on social media or correlations between geography and diagnoses in medical records.
Predictive modelling: AI can be used to build models that predict the potential spread of an outbreak based on historical data and current trends. These capabilities allow AI to provide early warnings of potential outbreaks, giving public health officials a head start in containing them.
Diagnosis and Treatment: AI can analyse medical images like X-rays and CT scans to improve accuracy and speed up diagnoses. AI can analyse patient data to recommend personalized treatment and predict potential drug interactions.
Resource Allocation and Response: AI in healthcare can model the spread of an epidemic and make predictions of areas most likely to be affected. This can help to allocate resources such as Healthcare Personnel and medical supplies such as vaccines.
Drug Discovery and Development: AI can analyse vast datasets of genetic information and chemical compounds to accelerate the development of new drugs and vaccines.
- Define Your Purpose and Target Market
- Gather and Prepare Your Data
- Choose the Right AI Model and Architecture
- Train and Test Your AI Model
- Deployment and Monitoring
- Security and Governance
Solutions
How would the solution work?
The Solution consists of 2 very complex technologies which need to be provisioned, deployed and commercialized to predict pandemics. The first solution which needs to be built is the Healthcare Artificial Intelligence Neural Network (Project Nexus) which feeds HealthCare data to a centralized point for analysis. Building an AI neural network can be approached in two ways: from scratch or using high-level tools. Building from scratch provides full control and requires expertise in math, programming, and machine learning. The process involves defining the problem, selecting an architecture (CNNs for images, RNNs for text), setting up a programming environment (Python with TensorFlow, PyTorch, or Keras), designing the network (layers, neurons, connections), preparing data (collection and pre-processing), training the model (adjusting parameters), and evaluating and refining based on test results. High-level tools simplify the process. Cloud-based platforms like Google Cloud AI Platform or Amazon SageMaker offer managed environments and pre-built components. AutoML tools like AutoKeras or TensorFlow AutoML automate design and training, ideal for beginners or rapid prototyping. Both methods aim to create neural networks capable of tasks like image recognition or language translation, with the choice depending on the user’s expertise and project needs. In our case, we need to develop DNNs (Deep Neural Networks) for predictive modelling. DNNs with multiple hidden layers can learn complex relationships within data. They can be used to analyse various factors and predict an individual’s risk of developing certain diseases. Recurrent Neural Networks (RNNs) or transformers can also be used. RNNs and transformers can process textual data like medical records. They can be used to extract information, identify relevant diagnoses, or even generate reports. Our HealthCare Neural Network will singularly establish a new standard. The second solution which needs to be built is SynthoSense’s HealthCare AI Platform Models. To make SynthoSense unique and a forerunner for AI in Healthcare we want to develop and implement an AI Architecture correctly from day 1, fully automated with CI/CD pipelines. The end-to-end process shown in Figure 2 depicts the way we will build Industry Standard AI Models which will be productionised and fully automated. Overall, AI is a powerful tool that can significantly improve our preparedness and response to healthcare epidemics. However, ethical considerations and responsible development are necessary to maximize its benefits. |
Opportunity
The opportunity – Our Healthcare systems are outdated by Decades, and we need to deploy AI into our Healthcare Systems or else the NHS will not be able to cope. Pandemics do great harm to our people and economy. With Technology, we can develop a Solution to Predict and Prevent Pandemics and make the World a safer place for everyone.
What’s next – SynthoSense is seeking feedback on this Programme Thesis, before launching a Programme funding opportunity. You are welcome to request more information on this opportunity and share feedback on the Programme Thesis.
