Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to the World Health Organization (WHO).

WHO’s report, Ethics and governance of artificial intelligence for health, is the result of two years of consultations held by a panel of international experts appointed by WHO.

“Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This important new report provides a valuable guide for countries on how to maximize the benefits of AI, while minimizing its risks and avoiding its pitfalls.”

Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.

AI could empower patients to take greater control of their own health care and better understand their evolving needs. It could also enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services.

However, WHO’s new report, published on June 28, cautions against overestimating the benefits of AI for health, especially when this occurs at the expense of core investments and strategies required to achieve universal health coverage.

It also points out that opportunities are linked to challenges and risks, including unethical collection and use of health data; biases encoded in algorithms, and risks of AI to patient safety, cybersecurity, and the environment.

For example, while private and public sector investment in the development and deployment of AI is critical, the unregulated use of AI could subordinate the rights and interests of patients and communities to the powerful commercial interests of technology companies or the interests of governments in surveillance and social control.

The report also emphasizes that systems trained primarily on data collected from individuals in high-income countries may not perform well for individuals in low- and middle-income settings.

AI systems should therefore be carefully designed to reflect the diversity of socio-economic and health-care settings. They should be accompanied by training in digital skills, community engagement and awareness-raising, especially for millions of healthcare workers who will require digital literacy or retraining if their roles and functions are automated, and who must contend with machines that could challenge the decision-making and autonomy of providers and patients.

Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technology’s design, development, and deployment. 

To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance:

Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.

Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.

Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.

Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.

Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.

Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.

“These principles will guide future WHO work to support efforts to ensure that the full potential of AI for healthcare and public health will be used for the benefits of all.”

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Growing use of AI for health presents governments, providers, and communities with opportunities and challenges

Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to new WHO guidance published today.

The report, Ethics and governance of artificial intelligence for health, is the result of 2 years of consultations held by a panel of international experts appointed by WHO.

“Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This important new report provides a valuable guide for countries on how to maximize the benefits of AI, while minimizing its risks and avoiding its pitfalls.”

Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.

AI could also empower patients to take greater control of their own health care and better understand their evolving needs. It could also enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services.

However, WHO’s new report cautions against overestimating the benefits of AI for health, especially when this occurs at the expense of core investments and strategies required to achieve universal health coverage.

It also points out that opportunities are linked to challenges and risks, including unethical collection and use of health data; biases encoded in algorithms, and risks of AI to patient safety, cybersecurity, and the environment.      

For example, while private and public sector investment in the development and deployment of AI is critical, the unregulated use of AI could subordinate the rights and interests of patients and communities to the powerful commercial interests of technology companies or the interests of governments in surveillance and social control.

The report also emphasizes that systems trained primarily on data collected from individuals in high-income countries may not perform well for individuals in low- and middle-income settings.

AI systems should therefore be carefully designed to reflect the diversity of socio-economic and health-care settings. They should be accompanied by training in digital skills, community engagement and awareness-raising, especially for millions of healthcare workers who will require digital literacy or retraining if their roles and functions are automated, and who must contend with machines that could challenge the decision-making and autonomy of providers and patients.

Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technology’s design, development, and deployment. 

Six principles to ensure AI works for the public interest in all countries

To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance:

Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.

Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.

Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.

Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.

Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.

Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.         

These principles will guide future WHO work to support efforts to ensure that the full potential of AI for healthcare and public health will be used for the benefits of all.

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According to a WHO prediction, by 2020, roughly 20% of India will suffer from some mental illness and 450 million people currently suffer from a mental illness, worldwide.

These numbers are a wake-up call that psychology as an issue and psychologists as a profession must be taken seriously. Such helping professions are often considered as human channels. Unlike manual workers whose job responsibilities are being taken over by machines and AI bots, psychiatrists and counselors see no threat to their professions with the advancements of machine learning and artificial intelligence.

According to an influential survey of the future of employment by Carl Benedikt Frey and Micheal Osborne who are Oxford economists, the probability that psychology could be automated in the future is only 0.43%. Behavioral scientists who study organizational behavior also believe that the idea of automating psychology has now become out of date. There is rather less scope for automation in psychology. It’s a humane profession that requires a human touch. For a psychologist, empathy and intuitive skills are a necessity that cannot be replicated by a machine. But there is some debate about this.

A General Break Down

Typically, a psychologist’s job has four primary functions – assessment, formulation, intervention, and evaluation. Is it possible to automate these tasks?

Assessing a client’s strengths and troubles is done by computer-aided psychological tests, which are then interpreted and documented on systems. Psychology tools like decision trees or worry decision trees are sometimes used by practitioners to diagnose conditions. Interventions for those issues are formulated and calculated for problem-solving with exercises that help the client via therapy. Evaluation is a summary of the initial assessment. This is how a routine psychiatric appointment is.

What’s Happening Now?

To counter the argument that AI cannot mimic a psychologist’s responsibilities, many AI-driven mental health apps are available in the market like Cogniant and Woebot. These apps work on CBT procedures (cognitive behavioral therapy) which is a guidebook for psychological conditions and their interventions. These apps use AI chatbots to talk to the users about managing their mental health. Research on similar apps is showing great results. This is resulting in the flooding of AI into the stream of psychology.

As technology is advancing and artificial intelligence is becoming more-human-like at a rapid rate. The development of deep learning algorithms and the inception of predictive analytic systems is making the course of AI easier to enter this domain. Thanks to big data, AI systems are getting all the data they need to interact and intervene.

COVID-19 And Its Effect

The demand for mental health surged during the onset of the pandemic. In such times when going to a psychologist’s clinic is risky and unadvised, people turned to mental health apps to do the talking. This provides a substantial ground that clients are ready to engage in technological forms of therapy. But just because they can, does that mean they should? This is a debate that can go on for a while now. AI is certainly getting more-human-like as we think about this but a combination of AI-powered diagnosis and human touch will benefit the client to better their mental health.

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Contribution of AI to a Coronavirus vaccine

Biomedical research of vaccines against COVID-19 was already being tested in humans in March. Three months after the initial outbreak was identified in China, many of those owed their rapid start to the power of Artificial intelligence (AI).

The feat is a promising and remarkable step in more than 200 years of immunization history. The experience may revolutionize the way vaccines are developed, potentially saving countless lives in future epidemics.

According to the World Health Organization (WHO), 34 vaccine candidates were being tested in humans as of early September. Another 145 candidates were picked up to test them in animals or in the lab, says WHO keeping a worldwide running list. Considering no one had heard of the novel coronavirus less than a year ago, these numbers are surprising. Novel coronavirus now recognized as SARS-CoV-2 that causes respiratory disease COVID-19. It typically takes several years or even decades to create a vaccine. The mumps vaccine’s highest speed record went from a collected sample to a marketed product within almost four years.

Research is speeding up with time. Our society and economy likely will not return to normal until a highly effective vaccine has been administered to a substantial amount of the planet’s population. The search for a vaccine has now expanded, collaborating with thousands of researchers at hundreds of laboratories worldwide and spending billions of dollars.

Human lives and the global vaccine market are at stake, risking approximately US $35billion even before COVID-19, and governments, philanthropies, and apothecary companies have been spending accordingly. In July, the U.S. government agreed to pay nearly $2 billion to pharmaceutical giants German’s BioNTech and Pfizer for 100 million doses of a vaccine when and if it comes to the market. Other major vaccine initiatives across the world are also getting funded in the 10 figures.

Machine learning algorithms and computational analyses have played a pivotal role in the vaccine venture. These tools help researchers understand the structure of the virus and speculate which of its components will provoke an immune response, an essential step in designing vaccines. These can also help scientists choose the potential vaccines’ elements, track the virus’s genetic mutations, and make sense of experimental data.

Suchi Saria, a professor at the John Hopkins Whiting School of Engineering and director of the university’s machine learning and health care lab, says, “AI is a powerful catalyst.” She explains, “AI enables scientists “to draw insights by combining data from multiple experimental and real-world sources.” She adds, such datasets are mostly messy and challenging as the researchers historically haven’t attempted this kind of analysis.

AI has contributed to the quest for a COVID vaccine than it has ever before. It is a tiny part of a broader suite of computational tools that are revolutionizing vaccine R&D. A few people have already started thinking about the next pandemic, and on the other hand, scientists have also started to figure out how these tools will do a bit more the next time.

In the early months of the pandemic, Russ Altman and Binbin Chen led a team of computer scientists at the Standford Institute for Human-Centred Artificial Intelligence (HAI) used machine learning to see if vaccine candidates, tested in animals can provoke an excellent immune response. Using the neural-network algorithms NetMHCpan-4.0 and MARIA and DiscoTope, the scientists came up with a list of targets or epitopes on the coronavirus that is expected to provoke an immune response. These epitopes are components of the virus, which B cells and T cells will likely identify.

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Data analytics has rarely been more newsworthy. Throughout the Covid-19 coronavirus pandemic, governments and bodies such as the World Health Organization (WHO) have produced a stream of statistics and mathematical models.

Businesses have run models to test post-lockdown scenarios, planners have looked at traffic flows and public transport journeys, and firms use artificial intelligence (AI) to reduce the workload for hard-pressed customer services teams and to handle record demand for e-commerce.

All that places more demand on storage.

Even before Covid-19, industry analysts at Gartner pointed out that expansion of digital business would “result in the unprecedented growth of unstructured data within the enterprise in the next few years”.

Advanced analytics needs powerful computing to turn data into insights. Machine learning (ML) and AI takes this to another level because such systems need rich datasets for training and rapid access to new data for operations. These can run to multiple petabytes.

Sure, all data-rich applications put pressure on storage systems, but the demands can differ.

“Data-intensive applications have multiple storage architectures. It is all about the specific KPI [key performance indicators] of the specific workload,” says Julia Palmer, research vice-president at Gartner. “Some of those workloads require lower latency and some of them require higher throughput.”

AI, ML and big data: Storage demands

All big data and AI projects need to mix performance, capacity and economy. But that mix will vary, depending on the application and where it is in its lifecycle.

Projects based on unstructured data, especially images and video, involve large single files.

Also, AI applications that include surveillance and facial recognition, geological, scientific and medical research use large files and so need petabyte scale storage.

Applications based on business systems data, such as sales or enterprise resource planning (ERP), might only need a few hundred megabytes to be effective.

Sensor-based applications that include maintenance, repair and overhaul technologies in transport and power generation could run to the low hundreds of gigabytes.

Meanwhile, applications based on compute-intensive machine learning and dense neural networks need high throughput and low latency, says Gartner’s Palmer. But they also need access to scalable, low-cost storage for potentially large volumes of data.

AI and ML applications have distinct cycles of storage demand too. The learning or training phase is most data intensive, with more data making for a better model. And storage needs to keep up with the compute engines that run the algorithm. Model training needs high throughput and low latency.

IOPS is not the only measure

Once the system is trained, requirements can be modest because the model only needs to examine relevant data.

Here, latency becomes more important than throughput. But this presents a challenge for IT departments because conventional storage solutions usually struggle to perform well for both sequential and random input/output (I/O).

For data analytics, typical batch-based workflows need to maximise the use of computing resources to speed up processing.

As a result, big data and analytics projects work well with distributed data, notes Ronan McCurtin, vice-president for northern Europe at Acronis.

“It is better to have distributed storage for data analytics and, for example, apply Hadoop or Spark technologies for big data analysis. In this case, the analyst can solve issues with memory limitations and run tasks on many machines. AI/ML training/inference requires fast SSD storage.”

But solid-state technology is typically too expensive for large volumes of data and long-term retention, while the need to replicate volumes for distributed processing adds further cost.

As Stephen Gilderdale, a senior director at Dell Technologies points out, organisations have moved on from a primary focus on enterprise resource planning (ERP) and customer relationship management (CRM) to heavier use of unstructured data.

And analytics has moved on too. It is no longer simply a study of historical data, “looking backwards to move forwards”. Instead, predictive and real-time analytics including sensor data is growing in importance.

Here, data volumes are lower, but the system will need to process the data very quickly to deliver insights back to the business. System designers need to ensure the network is not the bottleneck. This is prompting architects to look at edge processing, often combined with centralised cloud storage and compute.

AI/ML storage approaches, and limitations

To meet the requirements imposed by AI/ML, IT managers need to pick and mix from the following types of storage:

• High performance – NVMe and flash.
• High capacity – performance spinning disk, perhaps combined with flash/advanced caching.
• Offline and cold storage – capacity-optimised disk, cloud storage, tape.

Analytics and AI/ML are natural candidates for tiered storage, as these allow system designers to put the most expensive, best-performing resources as close as possible to compute resources, but still use large-capacity storage for archive data.

Architectures will also depend on the type of data a system handles. Gartner, for example, suggests that AI/ML using unstructured data could use NVMe-over-fibre, persistent memory and distributed file systems, and that will likely be on-premise, or using a hybrid cloud architecture.

Data analytics projects, meanwhile, are more likely to use converged file and object storage and hybrid models. That’s so they can scale but also to take advantage of the economies of long-term cloud storage. Analytics projects might process a few hours’ or several years’ worth of data, depending on the business questions, so being able to reload older data quickly and economically has its own value.

Real-time analytics needs data sources, compute and storage to be closely coupled. This is prompting organisations to use the cloud-based hyperscalers – primarily Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform – for tiers of compute and storage performance, as well as multiple physical locations.

There is no universal technology solution, however, and a degree of compromise is inevitable. “AI workloads are diverse and some are fundamentally different from any other workload the organisation may have run in the past,” says Palmer.

Analytics and AI: Build or buy?

Larger AI and business intelligence (BI) projects will need significant investment in storage, compute and networking. That has prompted some businesses to look to the cloud, and others to buy in analytics “as a service”.

But for most, venturing into data-rich applications will be a blend of existing and new capabilities.

“Buying technology is easy, but AI, ML and analytics rarely arrive or operate in perfect, pristine environments,” cautions Nick Jewell, director of product evangelism and enablement at data analytics firm Alteryx. “The reality is that most systems of insight are built on architectures that have existing dependencies or a legacy of some kind.”

CIOs also need to decide if AI and advanced analytics are a project, or a long-term strategic choice for the business.

Discrete projects, especially where data is already in the cloud, might make good use of a cloud or outsourced solution. But if the business wants to drive long-term value from analytics, and later AI, it needs to connect its existing data to the analytics platforms. For that, the storage architecture will need to measure up.

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