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Unleashing the Power of Artificial Intelligence in Healthcare: Revolutionising Patient Care – Part 2

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This is the second part of a 2-part series where we delve into the realm of Artificial Intelligence (AI) in healthcare. In the first post, we discussed how AI is transforming healthcare and medicine, by providing new ways to diagnose, treat, or prevent diseases, as well as improving the delivery and quality of health services. We also discussed the applications and benefits of AI in the delivery of healthcare services, such as enhancing efficiency, accuracy, and personalisation.

However, whilst the promises of AI in healthcare are remarkable, AI for health also poses significant challenges and risks for health systems, such as ethical, social, and technical issues. So, in this second and final part of the series, join me as we discuss some of the challenges, risks, and issues that often accompany the implementation of AI, and then proffer possible solutions to ensure responsible AI adoption.

Challenges and Ethical Considerations

  1. Ethical dilemmas: AI can raise ethical questions or conflicts that may not have clear or universal answers, such as who is responsible or accountable for the outcomes or consequences of AI systems, how to ensure fairness or equity in the access or distribution of AI benefits, or how to balance the rights or interests of different parties involved in AI systems, such as patients, providers, developers, or regulators.

  2. Data privacy and security concerns: As healthcare systems rely heavily on sensitive patient data, safeguarding privacy and ensuring robust security measures become paramount. Proper data governance, encryption, and stringent access controls are crucial to protect patient information from breaches or misuse.

  3. Bias and fairness in AI algorithms: AI algorithms are only as unbiased as the data they are trained on. It is essential to ensure diverse and inclusive data sets to avoid perpetuating biases. Ongoing monitoring and auditing of AI systems can help detect and rectify any unintended biases in decision-making processes.

  4. Social implications: AI can have social impacts that may affect the values, norms, or behaviours of individuals or groups in society, such as how to preserve human dignity, autonomy, or agency in the face of AI systems, how to maintain human relationships, trust, or empathy in the context of AI systems, or how to address potential social disruptions, divisions, or inequalities that may result from AI systems.

  5. Transparency and interpretability of AI systems:The “black box” nature of AI systems can raise concerns regarding transparency and interpretability. Healthcare professionals and regulators must work together to ensure that AI algorithms are transparent, providing insights into how decisions are made, and fostering trust among stakeholders.

  6. Technical limitations: AI can have technical challenges that may limit its performance, reliability, or safety in real-world settings, such as how to ensure the quality, validity, or security of the data used to train or test AI systems, how to deal with uncertainty, complexity, or ambiguity in the data or environment that may affect AI systems, or how to handle errors, failures, or attacks that may compromise AI systems.

  7. Regulatory gaps: AI can have regulatory challenges that may hinder its development, adoption, or evaluation in health systems, such as how to define, measure, or monitor the standards, criteria, or indicators for assessing the effectiveness, efficiency, or impact of AI systems, how to establish, enforce, or update the rules, guidelines, or frameworks for governing the design, use, or oversight of AI systems or how to foster collaboration, cooperation, or coordination among different stakeholders sectors or jurisdictions involved in AI systems.

Overcoming Challenges and Ensuring Responsible AI Adoption

To ensure responsible AI adoption in healthcare, some measures to overcome and mitigate these challenges and risks may include:

  1. Develop ethical guidelines: Ethical guidelines can help provide a common vision principles or values for guiding the development use or evaluation of AI systems in healthcare. Ethical guidelines can also help address or resolve ethical dilemmas or conflicts that may arise from AI systems in healthcare. For example, the World Health Organization (WHO) issued a report on Ethics and governance of artificial intelligence for health, which provides six guiding principles for AI in health, namely:

    1. Protecting human autonomy.
    2. Promoting human well-being and safety and the public interest.
    3. Ensuring transparency, explainability, and intelligibility.
    4. Fostering responsibility and accountability.
    5. Ensuring inclusiveness and equity.
    6. Promoting AI that is responsive and sustainable.

  2. Establish governance frameworks: Governance frameworks can help provide a clear and consistent structure, process, or mechanism for regulating, monitoring, or evaluating AI systems in healthcare. Governance frameworks can also help ensure the compliance, quality, or safety of AI systems in healthcare. For example, the UK National Health Service (NHS) has developed an AI in Health and Care Award programme, which provides a pathway for accelerating the testing and adoption of AI systems in healthcare, as well as a set of standards and requirements for assessing the feasibility, clinical safety, efficacy, and cost-effectiveness of AI systems in healthcare.

  3. Establish robust data governance and protection measures: Healthcare organizations must establish robust data governance frameworks, ensuring compliance with privacy regulations and implementing stringent security protocols to protect patient data.

  4. Ensure diverse and inclusive data sets for AI training: By actively seeking diverse and representative data sets, we can mitigate biases and ensure that AI algorithms deliver fair and equitable outcomes for all patients, irrespective of their background or demographics.

  5. Establish regulatory frameworks and guidelines for AI deployment: Regulators play a crucial role in establishing guidelines and standards for AI deployment in healthcare. By working hand in hand with healthcare professionals and AI experts, regulators can ensure responsible AI adoption, promoting patient safety, and ethical practices.

  6. Ensure human oversight: Human oversight can help provide a check and balance for AI systems in healthcare, by ensuring that human values, judgments, or inputs are incorporated or respected in AI systems in healthcare. Human oversight can also help prevent or correct errors, failures, or harms that may result from AI systems in healthcare. For example, the European Commission has proposed a risk-based approach for regulating AI systems in various sectors, including healthcare, which requires high-risk AI systems to have adequate human oversight measures, such as human-in-the-loop (where humans can intervene or override AI decisions), human-on-the-loop (where humans can monitor or supervise AI decisions), or human-in-command (where humans can set or limit AI goals or parameters).

  7. Foster collaboration: Collaboration can help facilitate the sharing of knowledge, resources, or best practices among different stakeholders, sectors, or jurisdictions involved in AI systems in healthcare. Collaboration can also help promote innovation, diversity, or inclusion in AI systems in healthcare. For example, the Partnership on AI is a multi-stakeholder initiative that brings together academics, researchers, civil society organizations, companies, and governments to explore and address the opportunities and challenges of AI in various domains, including healthcare. The Partnership on AI aims to advance the understanding and application of AI in ways that are ethical, fair, transparent, accountable, and beneficial to humanity.

  8. Promote education: Education can help raise awareness and understanding of AI among various audiences, such as patients, providers, researchers, policymakers, or the public. Education can also help develop the skills Competencies or capacities needed to design use or evaluate AI systems in healthcare. For example, the Stanford Center for Health Education has launched an online course on Artificial Intelligence in Healthcare which introduces the concept, technologies, and applications of AI in healthcare, as well as the ethical social and regulatory issues related to AI in healthcare.

The Future of AI in Healthcare

As we gaze into the future, the potential of AI in healthcare seems boundless; I dare say that we’re yet to scratch the surface of the immense potential of AI in healthcare. Emerging trends and technologies such as natural language processing, robotic surgery, and AI-driven drug discovery hold promise for revolutionizing healthcare practices. However, we must remain mindful of ethical and societal considerations, actively shaping the future of AI in healthcare through collaboration, innovation, and responsible implementation.

Eventually, doctors will adopt AI and algorithms as their work partners. This levelling of the medical knowledge landscape will ultimately lead to a new premium: to find and train doctors who have the highest level of emotional intelligence.

Conclusion

AI is transforming healthcare and medicine in unprecedented ways, by providing new tools methods or solutions for improving health outcomes quality and access to care. AI has many benefits for various stakeholders in health systems, such as patients, providers, researchers, policymakers, and society at large.

However, AI also poses significant challenges and risks for health systems. Therefore, it is essential to adopt a balanced and responsible approach to developing, using, and evaluating AI systems in healthcare, by following ethical guidelines, establishing governance frameworks, ensuring more comprehensive and inclusive data sets, ensuring human oversight, fostering collaboration, and promoting education.

By doing so, we can harness the potential of AI for health to revolutionize healthcare and medicine in the future, while minimizing its pitfalls and maximizing its benefits for humanity.

Au revoir; take care, until we meet again.

If you are interested in learning more about AI in healthcare, you can check out these resources:

  • Artificial Intelligence: Healthcare’s New Nervous System: This report by Accenture provides an overview of the current state and future trends of AI in healthcare, as well as some recommendations for health organizations to leverage AI for growth and value.

  • Artificial Intelligence for Health: This website by the International Telecommunication Union (ITU) provides information on the global focus group on artificial intelligence for health (FG-AI4H), which aims to establish a standardized assessment framework for evaluating and benchmarking AI solutions for health.

  • AI4Healthcare: This website by the AI4Healthcare initiative provides a platform for connecting and collaborating with AI experts, healthcare professionals, and patients to co-create and co-evaluate AI solutions for health.

ai revolutionizing healthcare

Unleashing the Power of Artificial Intelligence in Healthcare: Revolutionising Patient Care – Part 1

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In a previous post, we discussed possible cybersecurity trends and predictions for 2023. Today, I thought we’d embark on a journey into the realm of Artificial Intelligence (AI) in healthcare. So, join me, if you may, as we explore the applications, benefits, challenges, and ethical considerations surrounding this groundbreaking yet evolving technology, and what are the future directions of AI-augmented healthcare systems. We’ll uncover the potential of AI to revolutionize the delivery of healthcare services, transcending boundaries and ushering in a new era of improved patient outcomes and innovation.

AI is not a new concept, but it has gained a lot of momentum and attention in recent years. AI is the ability of machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. It’s probably been contemplated in some quarters, that AI can also enhance or possibly surpass human capabilities in some domains, such as vision, speech, and natural language.

Understanding Artificial Intelligence in Healthcare

AI in healthcare is the use of machine-learning algorithms and software, or artificial intelligence, to mimic human cognition in the analysis, presentation, and comprehension of complex medical and healthcare data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease.

AI in healthcare can be classified into three main types:

Machine learning (ML): This is the process of training machines to learn from data and make predictions or decisions based on patterns or rules. ML can be further divided into supervised learning (where the machine learns from labelled data), unsupervised learning (where the machine learns from unlabelled data), and reinforced learning (where the machine learns from feedback or rewards).

Natural language processing (NLP): This is the process of enabling machines to understand and generate natural language, such as text or speech. NLP can be used for tasks such as information extraction, sentiment analysis, text summarization, question answering, and conversational agents.

Computer vision (CV): This is the process of enabling machines to perceive and interpret visual information, such as images or videos. CV can be used for tasks such as face recognition, object detection, scene understanding, medical imaging analysis, and gesture recognition.

AI in healthcare can also involve other technologies, such as robotics (the use of machines to perform physical tasks), the Internet of Things (the interconnection of devices and sensors that collect and exchange data), blockchain (the use of distributed ledgers to store and verify transactions), and cloud computing (the use of remote servers to store and process data).

Applications of AI in Healthcare

AI has many applications in various fields and industries, but one of the most promising and impactful areas is healthcare and medicine. AI can help improve the delivery and quality of health services, advance medical research and innovation, as well as empower patients and communities.

Let us delve into the remarkable applications of AI in the healthcare domain, where its potential knows no bounds.

  1. Diagnosis and medical imaging: AI can help improve the speed and accuracy of diagnosis and screening for diseases by analysing various types of data, such as symptoms, medical history, lab tests, genetic information, and imaging scans. For instance, AI can help detect skin cancer by comparing images of skin lesions with a database of known cases or diagnose diabetic retinopathy by analysing retinal photographs for signs of damage.

    By augmenting human expertise, AI can enhance diagnostic accuracy, leading to earlier detection of diseases such as cancer. Case studies have shown remarkable success in AI-assisted radiology, revolutionizing the way we identify early-stage cancers and potentially saving countless lives.

  2. Predictive analytics and personalised treatment: AI can assist with clinical care by providing recommendations or guidance for optimal treatment plans, dosages, or procedures based on patient characteristics, preferences, and outcomes. For example, AI can help optimize radiation therapy for cancer patients by generating personalized plans that minimize damage to healthy tissues or assist surgeons with robotic tools that enhance precision and control.

  3. Research: AI can help advance medical research and innovation by discovering new insights, hypotheses, or solutions from large and complex datasets, such as clinical trials, genomics, or population health. For example, AI can help design new drugs by screening millions of molecules for potential efficacy and safety or find new links between genetic codes and diseases by analysing patterns across genomes.

  4. Public health: AI can help improve public health interventions, such as disease surveillance, outbreak response, and health systems management by monitoring, predicting, and responding to health events or trends at local or global scales. For example, AI can help track the spread of infectious diseases by analysing data from social media, news sources, or mobile devices, or optimize the allocation of health resources by forecasting demand and supply based on historical data and real-time information.

  5. Administration and virtual assistants: AI can help improve the efficiency and performance of administrative and operational workflows, processes, and financial operations by automating or streamlining tasks that are repetitive, time-consuming, or susceptible to errors. For example, AI can help automate the processing of insurance claims or billing by extracting and validating information from documents or images as well as reduce fraud and waste by detecting anomalies or inconsistencies in health data or transactions.

Benefits of AI in Healthcare

AI for health has the potential to bring positive impacts to various stakeholders, such as patients, providers, researchers, policymakers, and society at large. Here are some benefits of AI in healthcare:

  1. Improve accuracy, speed, and scalability: AI can help improve the quality and reliability of health services by reducing errors, delays, and variability that may arise from human factors, such as fatigue, bias, or inconsistency. AI can also help scale up health services by reaching more people or places that may have limited access to human expertise or resources.

  2. Reduce costs and increase efficiency: AI can help reduce the costs and inefficiencies of health systems by optimizing the use of assets and resources, such as equipment, staff, or facilities. AI can also help prevent or minimize errors or adverse events that may result from human mistakes or malpractice, such as misdiagnosis, medication errors, or surgical complications.

  3. Enable personalised and preventive care: AI can help tailor health services to the individual needs and preferences of each patient by considering their respective characteristics, such as genetics, lifestyle, or environment. AI can also help prevent or delay the onset or progression of diseases by identifying risk factors or early signs of illness and providing timely interventions or recommendations.

  4. Empower patients and communities: AI can help empower patients to take greater control of their respective health care and better understand their evolving needs by providing them with access to information, education, or support. AI can also help engage and mobilize communities to participate in health promotion or protection activities by providing them with feedback, incentives, or social networks.

The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honoured connection and trust.

Conclusion:

Without any doubt, the fusion of AI and healthcare offers unparalleled potential for improved patient outcomes, operational efficiency, and personalized care. However, whilst the promises of AI in healthcare are immense, AI for health also poses significant challenges and risks for health systems, such as ethical, social, and technical issues.

In the next article in the series, we’ll delve into some of these challenges and proffer possible solutions to these challenges to ensure responsible AI adoption in healthcare.

Arrivederci; take care, see you around soon.

In the meantime, if you’re interested in learning more about AI in healthcare, you can check out these resources:

  • IBM: This website provides an overview of AI in healthcare, its benefits, use cases, solutions, and resources.
  • Wikipedia: This article provides a comprehensive introduction to AI in healthcare, its history, applications, challenges, ethics, and future.
top 10 cybersecurity risks trends for 2020

Top Cyber Security Risk Trends & Predictions for 2020

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Many security experts would probably agree that 2019 was a very interesting and challenging year. According to a Forbes report, 2019 saw more than 3,800 publicly disclosed data security breaches in the United States within the first 6 months.

The year 2019 witnessed some very large, disturbing, and public-profile information security breaches.

According to the Ninth Annual Cost of Cybercrime Study Report published by Accenture in 2019, the average number of security breaches grew by 11 percent since 2018.

Some of the security breaches recorded in 2019 include (presented in descending breach reported date order):

Gekko Group, a subsidiary of Accor Hotels, in November 2019 with over 660,000 customer records exposed

Quest Diagnostics in June 2019 with about 11.9 million patient records affected

First American Financial Corporation in May 2019 with about 885 million records of mortgage transactions affected

Facebook in April 2019, via third-party hosted servers with over 540 million records exposed

Capital One in March 2019 with over 106 million breached customer information

Fornite, owned by Epic Games, in January 2019 with about 200 million users affected worldwide

We are now only the third day into the new decade and already a retail foreign-exchange specialist company; Travelex; was reportedly forced to shut down its online services following a virus attack on its systems.

Cybersecurity and cyberattack issues are becoming ever-increasing struggles for businesses and organisations. And these can so easily affect all organisations, regardless of size.

With new risks & threats emerging every other day, the risks of compromising customer or employee data are becoming ever increasingly challenging to organisations as more severe consequences are being put in place and followed through by regulatory agencies.

Reputational damages caused by security incidents and data breaches could be incalculable.

data breach

So, with 83% of enterprise workload expected to move to the cloud by the end of this year, here are some security risks and threats that I expect (predict) to cause major headlines this year:

Ransomware

It is reported that a business will fall victim to a ransomware attack every 14 seconds in 2019 and every 11 seconds by 2021. This suggests that a similar prediction for 2020 will put the figure less than every 11 seconds. That is appears to be a worrying prediction.

Ransomware attacks were very prevalent in 2019. With the increase in sophistication and complexities; particularly enterprise ransomware; I envisage this trend will continue in 2020, particularly against healthcare organisations, small businesses, schools, and local government institutions.

Phishing & Social Engineering

According to the 2019 Data Breach Investigation Report published by Verizon, 32% of data breaches involve phishing.

And according to the 2019 Internet Security Threat Report (ISTR) published by Symantec, phishing security incidents have declined year on year for the last four years; I expect this trend to continue.

Formjacking

The report by Symantec reveals that formjacking remains the greatest threat to online retailers and other organisations within goods supply chain. I expect to see this trend continue to rise.

internet of things iot

Internet of Things (IoT)

Gartner projects that there will be around 20.4 billion IoT devices by the end of this year (i.e. 2020).

Symantec reports that, in 2018, network routers accounted for 75% of infected devices, and connected digital cameras accounted for 15% of infected devices. Understandably, routers were the most targeted devices simply because they are gateways (i.e. entry/exit points) from internal networks to the internet.

China, North America, and Europe collectively represent two-third of installed and in-use IoT devices, with consumer items (smart televisions, virtual assistant smart speakers, smart refrigerators, digital set-up boxes, etc.) representing more than 60% of IoT connected devices.

Adoption of 5G technologies, infrastructure, and systems is expected to have a global reach in 2020, and this should give rise to a plethora of new IoT devices.

I envisage that there would be an increase in the number of devices at risk as more consumers and businesses introduce more IoT connected devices to more everyday activities.

Malware

According to a study by Accenture, the average cost of a malware attack on a company is $2.6 million.

Another similar study by IBM, indicates that the average cost of a data breach is $3.9 million.

Given the magnitude of potential damages this could cause to organisations, this appears to be one area of risks and attacks that shows no sign of letting off in 2020.

Mobile Devices

Mobile devices used within corporate environments remain high risk concerns to businesses.

Symantec reports states that, in 2018, 1 in 36 mobile devices had high risk apps installed, and infections on mobile devices were up by a third when compared to 2017.

Mobile devices will become prime phishing attack vectors as attackers continue to employ machine learning and artificial intelligence in attacks.

I envisage that this upward trend is expected to continue in 2020, especially with the anticipated advent of 5G networks and even more mobile devices.

Internal Actors

Insider attacks are forecast to rise in 2020. This is a high impact, high severity, and high probability risk because of the potential for disgruntled or careless employees to inflict great harm on any organisation.

One-third of data breaches involved internal actors.

The human factor remains the weakest link in cybersecurity implementation. Blackmails, hefty bribes, threat to life & family members remain enough duress to make hereto loyal employees to reluctantly reconsider their decisions and choices.

It’s important to emphasise that, as far as regulatory laws and legislation are concerned, the final responsibility and accountability for protecting data in the cloud lies with the organisations that choose to use cloud services, and not the cloud service providers.

This, therefore, mandates that businesses & organisations must ensure that the appropriate and fitting risk mitigating plans, incident response plans, monitoring, controls, and reporting are put in place.

The report by Verizon indicated that 56% of breaches took months to discover. I envisage that effective cyber and information security strategies and implementations could help organisations promptly identify risks, respond and manage security incidents & events.

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