STEM Foundation

Reimagining Healthcare with AI

Targeted therapies, personalised medicine, and other customised treatments that aim to match an individual’s genetic profile, is the newly emerging world of health that challenges the common health notion of ‘the balance of risk’ when it comes to recommending or administering a drug or treatment.  Understanding why 1 in 100 people might suffer adverse reaction to a medicine can help us create a stronger patient experience tailored to individual need, thus shifting offerings from blanket therapies to that of personalised medicines. Having insights to make better decisions when it comes to reforming and improving the overall value chain of health systems at pace, is a fundamental component.

Today, the global spend on health is estimated to be around 10% of global GDP, or US$ 8.3 trillion for the next 3-5 years, according to WHO, the World Health Organisation[1]. Public health systems continue to experience insurmountable pressures, as a consequence of the COVID-19 pandemic. Hence, accelerating change within public and private health providers, and across their ecosystems, forcing them to innovate, adapt and respond to the many dynamically shifting needs and demands, within a shorter period of time, is called for.

Healthcare is unquestionably, a sector that is in need of innovation. Health plans, health providers, life and bio science businesses, as well as, governments, are all facing increased costs, often, yielding inconsistent outcomes.

Trends’ forecasters have highlighted many technologies that are likely to transform the healthcare landscape as we know it. These include, amongst others, such technologies as: Artificial Intelligence (AI), Internet of Medical Things, Telemedicine, Big Data and Analytics, Cloud Computing, Immersive Technologies, Genomics, Blockchain, 3D-printed Devices and Mobile Health. Digital technologies form the backbone of transforming health practices and optimising their systems and responses, thereby, improving patient outcomes and lowering overall costs.

The shift in users’ behaviours to be more accepting, or even favouring digital health solutions, is also a significant driver that is precipitating the creation and reinvention of healthcare offerings.

Whilst the digital economy is impacting every sector, and it represents 22.5% of global GDP, the ability of digital to unlock value, particularly, in the health and pharma related industries, remain to be far from being fully understood, or exploited[2]. Identifying digital health opportunities across the entire patient/user pathway, from primary prevention and screening, through diagnosis to treatment and monitoring, require a clear assessment and distillation of where the ‘pain points’ and potential ‘gains’ are, within the pathway, and, how digital health solutions might address them.

The confluence of AI with other advances in digital and biological sciences is undoubtedly presenting an exciting wave of innovations. For example, the speed with which scientists have been able to sequence the COVID-19 virus’ genomic structure and its various mutations, in weeks rather than months, is a testament to the new converging advances in computing, large data analytics, artificial intelligence (AI) including machine learning (ML), and biological engineering. The decreasing cost of DNA sequencing to be less than US $1,000, which is likely to drop even further to be less than US $100 within this decade, is increasing our ability to “understand and engineer biology” better resulting in the emergence of new techniques to edit genes and reprogram cells. There are four transformative biological areas that are experiencing innovation growth[3]. These are: 

  1. Biomoleculesthe mapping, measuring, and engineering of molecules to create processes, devices and products that benefit people;
  2. Biosystemsthe engineering of cells, tissues, and organs;
  3. Bio-machinesthe interface between biology and machines; and,
  4. Bio-computingthe use of cells, or molecules such as DNA, for computation.

All these Bio Innovative areas, show various rates of progress, from their initial innovation triggers to that of developing proofs of concepts, and ultimately, deploying them in their relevant fields. Such new biological capabilities have the potential to bring about sweeping change to our societies, particularly, in such areas as oncology, where the time between diagnosis and treatment, is often, key to a patient’s outcome.

Of course, such biological advancement would not have been possible without the significant cost-effective computational processing power, and particularly, the power of AI’s predictive capability. If there is one activity at which ML really excels at, it is that of identifying patterns, and extracting insights about complex systems, given lots of data. Healthcare, therefore, represents an ideal candidate for the AI challenge and opportunity.

The progress in the disciplines of AI and data science, together with, a perfect combination of increased computer processing power and speed, and having access to larger data collections and data libraries, coupled with an expanding pool of AI talent, has enabled more experimentation of AI within life sciences and healthcare. Noticeably, the advancement in Deep Learning has had an impact on the way we look at AI tools today, and is the reason for much of the recent excitement surrounding AI applications. Deep Learning allows finding correlations, that were too complex to render, using previous ML algorithms. Largely, this is due to the expansive developments in Artificial Neural Networks.  For example, AI’s Deep Learning offered by companies like IBM Watson and Google’s Deep Mind are currently being used for many of the healthcare-related applications. IBM Watson is being used to investigate diabetes management, advanced cancer care modelling, and drug discovery, but has yet to show clinical value to the patients. Deep Mind is also being considered for applications such as mobile medical assistant, diagnostics based on medical imaging, and prediction of patient condition. Also, Apple’s well-known partnership with Stanford Medicine, could lead to a new paradigm shift from that of a ‘provider-centric’ to a ‘patient-centric’ healthcare operating model. And, the latest application from GE Research demonstrated the use of a sensor, which is smaller than a fingertip that could find viruses and pathogens including that of COVID-19 Coronavirus!

AI is proving to be a key enabler for accelerating healthcare innovations both in clinical and non-clinical domains.

Now, AI applications are being used in such areas as Cognitive Reasoning Technologies, which include computer vision, natural language processing and speech recognition.

Additionally, in medical imaging technology, a number of firms are now working toward making the role of Radiologists more effective by using AI-based computer vision algorithms to identify areas of mammograms that are consistent with breast cancer. The system automatically analyses mammogram images and outlines suspicious areas that indicate potential abnormalities. Google’s recent launch of ‘Derm Assist’, an AI-powered platform that enables users to self-diagnose hundreds of skin conditions, is another demonstration of a true inflection point in this field. Other players such as Apple, Amazon and Microsoft are also pushing into this potentially lucrative space, offering healthcare solutions for patients, physicians and pharmaceutical and other health related businesses. 

Over the next two decades we will see an expansion of direct brain-to-device communication for paralyzed patients who are unable to communicate. In a recent lab demonstration, a paralyzed man was able to translate his thoughts of writing into text at a rate of 90 characters per second. By connecting these implants into his premotor cortex, he was able to  envision that he was writing by hand, and the corresponding text was generated[1]! Beyond 2040, we are likely to see a direct link between a brain and computational chip–  a true neuromorphic computing capability.

Healthcare, life, and biological sciences are entering an exhilarating new phase of growth as a result of digital advancements, particularly in AI, forging a transition from therapeutic to therapeutech! The Lab of the Future will enable Scientific Research to become more collaborative and predictive, joined by shared knowledge and digitalized processes, able to discover and respond to challenges, more rapidly. Dynamic learning and compliance can be achieved to enable faster commercialisation from- lab-to-market. And, of course, the use of AI, AR/VR, IoT-Analytics, and other forms of emerging digital automation technologies, will generate a new ‘beyond the pill’ patient journey and experience that is more dedicated to the specific needs, netting better results for all.

Parsing existing traditional business and operating models used by health systems, providers and stakeholders, to become more digitally activated and offer channel-agnostic patient/user experiences through integrating and using data from multiple systems and channels in a not too dissimilar way to that of using a WhatsApp or Facebook Messenger, is now a necessity, and no longer an option. Businesses organising for speed in this new era, to claim a first-mover digital advantage within the health landscape, is stimulating a race for new innovative value propositions.

But beyond exploring and reimagining health-related value propositions and creating optimised solutions, there remains to be a number of challenges an organisation could face and should be mindful of. These include, amongst others the following:

  • Data source of trust and truth: Sources of structured and unstructured data including those from healthcare networks (hospitals, clinics, and laboratories), technology networks (sensors, monitoring, and IoT devices), and social networks will need to be enabled and conditioned for use by AI models and big data analytics applications. Deep Learning algorithms tend to be data-greedy. Big data in biotech is not always well-prepared for modelling, or is inaccessible due to privacy reasons. Off the shelf pretrained data models with large parameters are now accelerating the take up of AI and its diffusion. Google’s Bidirectional Encoder Representations from Transformers BERT has 340 million parameters. Microsoft launched its Turing-NLG model in February 2020 with 17 billion parameters. OpenAI launched GPT-3 in June 20 with 175 billion parameters.
  • Culture of a digitally enabled organisation: a change in the culture of how digital health is viewed across all involved, including the public, policymakers, providers, and those from the health and care professions, need to elevate the value of data accuracy, consistency and currency. Communicating the benefits of shared data and data governance is crucial.
  • Decentralisation of healthcare models: with remote patient monitoring on the increase (e.g. analysis of a patient’s health metrics including vital signs, heart rate, blood glucose, temperature, medication adherence and physical movement), unnecessary visits to healthcare providers will be reduced, negating the need for centralised healthcare models. Such decentralisation has to be public health-driven to enable better orchestration of services.
  • The demand for AI talent: Around 28% of the AI/ML initiatives have failed[2]. Lack of staff with necessary expertise, lack of production-ready data, and lack of integrated development environments, are reported as primary reasons for failure. A growing wave of specialised university courses, geared toward data science and AI applications is projected to address this issue in the coming years.
  • Ethical considerations: Building trust, transparency, and value to the public as well as profits for commercial organisations, is the starting point. In AI related applications, the notion of ‘AI Explainability’ – where results of the solution can be understood or replicated by humans, thus removing the concept of the black box – will need to be resolved to enable trust to be manifested.  

Technology is becoming a regulated industry. The EC will propose a horizontal regulatory guidance in 2021 to safeguard fundamental EU values, rights and user safety by obliging high-risk AI systems to meet mandatory requirements. Earlier this year, the UK published its AI Council Roadmap to guide self-regulations. And, of course, the element of power consumption related to the processing of AI’s large neural networks will need to be responsibly innovated to mitigate any unintended harm to the environment and society.  

AI national strategies continue to grow with real money attached to them[3]. Examples include the USA $47.5 billion, China $7.2 Billion plus regional investments, the UK $2.6 billion, and France €1.5 billion.

In medicine and healthcare, AI algorithms and related digital technologies are already reshaping the bio/life sciences and healthcare roadmaps. Customised offerings that match an individual’s genes, their environment and lifestyle needs, and that are founded on defined clinical baselines and determined efficacy, will become the new normal, in the not so distant future. A completely undiscovered world of new possibilities awaits us, where health capabilities are powered by a fusion of digital technologies seamlessly interlacing with bio and life sciences will help us create the embodiments of our imaginations. 

Prof Sa’ad Sam Medhat

CEO, IKE Institute.

[2] AI Strategies View, June 2020. IDC.

[3] CSET analysis of Crunchbase and Refinitiv data. Target countries are ordered by disclosed investment value in 2019.

[2] Accenture Research and Oxford Economics, Nov 2020.

[3] MGI Report: The Bio Revolution: Innovations transforming economies, societies, and our lives, May 2020

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