When it comes to provisioning the IT on which all businesses rely, running costs and energy efficiency have long played a significant role in the decision-making processes. Recently, however, concerns about global warming and soaring energy costs have moved them to the top of the priority list. This, in turn, is causing CIOs across the board to fundamentally rethink the approach they take, particularly when it comes to the data center where huge benefits are to be had on both counts.

The size of the problem

Estimates vary but according to the International Energy Agency (and others), datacentres and their associated infrastructures account for around 1% of global energy consumption. In EMEA alone that translates to over 90TWh per year, or enough to satisfy the domestic energy needs of a small country. Moreover, this figure carries with it an environmental impact equivalent to running almost 6 million vehicles.

Big numbers whichever way you look at them and which, in turn, mean that any action to reduce energy consumption would not only save businesses money but have a significant impact when it comes to climate change.

Datacentre change

There are lots of ways of tackling this issue with some organizations, for example, abandoning their on-premises datacentres altogether and moving to the cloud. That, however, doesn’t necessarily save on running costs. Indeed, many businesses find it more expensive compared to running an on-premises datacentre, and with none of the budgetary certainty of an on-premises facility. Neither does it address the climate issues. It just makes them somebody else’s problem.

Of course, the cloud has other benefits besides, but the data center looks like it’s here to stay for some time albeit with changes to the operational model to deliver the benefits of cloud computing at a lower cost in terms of energy and emissions. Indeed, that change is already happening with growing numbers moving from traditional 3-tier architectures (servers + storage + networks) towards next-generation models, hyper-converged infrastructures (HCI) which, most analysts, agree is the best and most expedient way of reducing datacentre energy consumption and carbon footprint.

The reason for that assertion is down to the way hyper-converged models work by distributing computing power and storage across low-cost commodity hardware platforms, linking them with software, and using virtualization to provide an easily scaled and managed operational whole. A mature technology with several different HCI platforms available, here are the expected benefits in terms of energy consumption and climate change of switching from 3-tier to HCI models:

• Measurable benefits could be achieved across a range of organizations from small businesses through big enterprises to the large-scale hyperscalers and managed service providers
• In comparison to traditional 3-tier IT platforms, next-generation HCI architectures could potentially reduce energy consumption and carbon footprint by up to 27% per year.
• Across the EMEA region, HCI transformation has the potential to reduce energy consumption by 56.7 TWh and cut emissions by 14.2 million tonnes of CO²e over the period 2022-2025
• By 2025 a full changeover to HCI across UK datacentres could potentially save 8.1 TWh of energy and 1.8 million tonnes of CO²e, roughly the same as taking 400,000 cars off the road
• By 2025 a full changeover to HCI across data centres in the Middle East & Africa could potentially save 4 TWh of energy and roughly 2.4 million tonnes of CO²e.
• Large-scale colocation datacentres offer a much lower PUE (Power Usage Effectiveness) factor than typical on-premises facilities. Switching these to HCI architectures could potentially boost energy saving by 30-40%.
• Next-generation colocation datacentres could provide access to renewable energy through long-term Power Purchase Agreements (PPA) and so contribute to an organization’s climate neutrality goal without having to invest in CO2 certificates

The use of artificial intelligence (AI) in healthcare was not adequate and much less compared with commercial verticals especially in modern societies, such as the United States, China, and Europe. Had there been the modern technologies used efficiently in hospitals, research labs, patient care units, and drug development centers earlier, we could have saved many lives that the COVID-19 pandemic claimed.

Concerns from similar and diverse angles had been raised by global expert communities and they were inviting the attention of various government authorities, state agencies, and private companies. Incorporating all of it, the International Group of Artificial Intelligence (IGOAI), a community of academics, students, CIOs, members of global AI associations, and professionals who engage with AI, successfully hosted a virtual summit on “The Artificial Intelligence in Healthcare” on 29th June 2022 with the theme of “AI simplifies the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.”

Dr. Jassim Haji, Chairman, IGOAI

The following article refers to the key areas of AI that were analyzed during the summit and they would ideally catalyze further discussions on the subject.

Supports Medical Imaging Analysis

AI is used as a tool for case triage. It supports a clinician reviewing images and scans. This enables radiologists or cardiologists to identify essential insights for prioritizing critical cases, avoid potential errors in reading electronic health records (EHRs), and establish more precise diagnoses.

Builds Complex And Consolidated Platforms For Drug Discovery

AI algorithms can identify new drug applications, tracing their toxic potential as well as their mechanisms of action. This technology led to the foundation of a drug discovery platform that enables the company to repurpose existing drugs and bioactive compounds.

Forecast Kidney Disease

Acute kidney injury can be difficult to detect by clinicians but can cause patients to deteriorate very fast and become life-threatening. With an estimated 11% of deaths in hospitals following a failure to identify and treat patients, the early prediction and treatment of these cases can have a huge impact to reduce life-long treatment and the cost of kidney dialysis.

Provides Valuable Assistance to Emergency Medical Staff

During a sudden heart attack, the time between the 911 or 999 call to the ambulance arrival is crucial for recovery. For an increased chance of survival, emergency dispatchers must be able to recognize the symptoms of a cardiac arrest to take appropriate measures. AI can analyze both verbal and nonverbal clues to establish a diagnosis from a distance.

Supports Health Equity

The AI and ML industry has the responsibility to design healthcare systems and tools that ensure fairness and equality are met, both in data science and in clinical studies, to deliver the best possible health outcomes. With more use of ML algorithms in various areas of medicine, the risk of health inequities can occur.

Natural Language Processing

Making sense of human language has been a goal of AI researchers since the 1950s. This field, NLP, includes applications such as speech recognition, text analysis, translation, and other goals related to language.

There are two basic approaches to it: statistical and semantic NLP. Statistical NLP is based on machine learning and has contributed to a recent increase in the accuracy of recognition. It requires a large “corpus” or body of language from which to learn.

Rule-Based Expert Systems

Expert systems based on collections of “if-then” rules were the dominant technology for AI in the 1980s and were widely used commercially in that and later periods. In healthcare, they were widely employed for “clinical decision support” purposes over the last couple of decades and are still in wide use today. Many electronic health records (EHR) providers furnish a set of rules with their systems today.

Physical Robots

Physical robots are well known by this point, given that more than 200,000 industrial robots are installed each year around the world. They perform pre-defined tasks, such as lifting, repositioning, welding, or assembling objects in places like factories and warehouses and delivering supplies in hospitals. Recently, robots have become more collaborative with humans and are more easily trained by moving them through the desired task. They are also becoming more intelligent, as other AI capabilities are being embedded in their “brains.”

Robotic Process Automation

This technology performs structured digital tasks for administrative purposes, i.e., those involving information systems, as if they were a human user following a script or rules. Compared to other forms of AI they are inexpensive, easy to program, and transparent in their actions. Robotic process automation (RPA) doesn’t involve robots – only computer programs on servers. It relies on a combination of workflow, business rules, and “presentation layer” integration with information systems to act like a semi-intelligent user of the systems. In healthcare, they are used for repetitive tasks like prior authorization, updating patient records, or billing.

Patient Engagement And Adherence Applications

Patient engagement and adherence have long been seen as the ‘last mile”’ problem of healthcare – the final barrier between ineffective and good health outcomes. The more patients proactively participate in their well-being and care, the better the outcomes – utilization, financial outcomes, and member experience. These factors are increasingly being addressed by big data and AI.

Administrative Applications

There are also a great many administrative applications in healthcare. The use of AI is somewhat less potentially revolutionary in this domain as compared to patient care, but it can provide substantial efficiencies.

International Group of Artificial Intelligence (IGOAI) was established in 2021 to create global awareness about artificial intelligence (AI). It disseminates learning about AI through global summits and conferences by articulating potential algorithm-based biases, security gaps, and other key areas that need feedback and scrutiny.