Artificial Intelligence for Diabetic Eye Disease

First published 2023

Diabetes is a widespread chronic condition, with an estimated 463 million adults affected globally in 2019, a number projected to rise to 600 million by 2040. The rate of diabetes among Chinese adults has escalated from 9.7% in 2010 to 12.8% in 2018. This condition can cause serious damage to various body systems, notably leading to diabetic retinopathy (DR), a major complication that affects approximately 34.6% of diabetic patients worldwide and is a leading cause of blindness in the working-age population. The prevalence of DR is significant in various regions, including China (18.45%), India (17.6%), and the United States (33.2%).

DR often goes unnoticed in its initial stages as it does not affect vision immediately, resulting in many patients missing early diagnosis and treatment, which are crucial for preventing vision impairment. The disease is characterised by distinct retinal vascular abnormalities and can be categorised based on severity into stages ranging from no apparent retinopathy to proliferative DR, the most advanced form. Diabetic macular edema (DME), another condition that can occur at any DR stage, involves fluid accumulation in the retina and is independently assessed due to its potential to impair vision severely.

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Diagnosis of DR and DME is typically made through various methods such as ophthalmoscopy, biomicroscopy, fundus photography, optical coherence tomography (OCT), and other imaging techniques. While ophthalmoscopes and slit lamps are common due to their affordability, fundus photography is the international standard for DR screening. OCT, despite its higher cost, is increasingly recognised for its diagnostic value but is not universally accessible for screening purposes.

The current status of diabetic retinopathy (DR) screening emphasises early detection to improve outcomes for diabetic patients. In the United States, the American Academy of Ophthalmology recommends annual eye exams for individuals with type 1 diabetes beginning five years after diagnosis, and immediate annual exams for those with type 2 diabetes upon diagnosis. Despite these guidelines, compliance with screening is low; a significant proportion of diabetic patients do not receive regular eye exams, with only a small percentage adhering to the recommended screening intervals.

In the United Kingdom, a national diabetic eye screening program initiated in 2003 has been credited with reducing DR as the leading cause of blindness among the working-age population. The program’s success is attributed to the high screening coverage of diabetic individuals nationwide.

Non-compliance with screening recommendations is attributed to factors such as a lack of disease awareness, limited access to medical resources, and insufficient medical insurance. Patients with more severe DR or those who already have vision impairment tend to comply more with screening, suggesting that the lack of symptoms in early DR leads to underestimation of the need for regular check-ups.

The use of telemedicine has been proposed to increase accessibility to screening, exemplified by the Singapore Integrated Diabetic Retinopathy Program, which remotely obtains fundus images for evaluation, reducing medical costs. Telemedicine has been found cost-effective, especially in large populations. Recently, the development of artificial intelligence (AI) has presented an alternative to enhance patient compliance and the efficiency of telemedicine in DR screening. AI can potentially streamline the grading of fundus images, reducing reliance on human resources and improving the screening process.

AI’s origins trace back to 1956 when McCarthy first introduced the concept. Shortly after, in 1959, Arthur Samuel coined the term “machine learning” (ML), emphasising the ability of machines to learn from data without being explicitly programmed. Deep learning (DL), a subset of ML, uses multi-layer neural networks for learning; within this, convolutional neural networks (CNNs) are specialised for image processing, featuring layers designed for pattern recognition.

CNN architectures like AlexNet, VGGNet, and ResNet have been pivotal in advancing AI, achieving high accuracy through end-to-end training on labelled image datasets and optimising parameters via backpropagation algorithms. Transfer learning, another ML technique, leverages pre-trained models on new domains, allowing for effective learning from smaller datasets.

In the medical field, AI’s image processing capabilities have significantly impacted radiology, dermatology, pathology, and ophthalmology. Specifically in ophthalmology, AI assists in diagnosing conditions like DR, glaucoma, and macular degeneration. The FDA’s 2018 approval of the first AI software for DR, IDx-DR, marked a milestone, using Topcon NW400 for capturing fundus images and analysing them via a cloud server to provide diagnostic guidance.

Further developments in AI for ophthalmology include EyeArt and Retmarker DR, both recognised for their high sensitivity and specificity in DR detection. These AI systems have demonstrated advantages in efficiency, accuracy, and reduced demand for human resources. They’ve shown to not only expedite the screening process, as evidenced by an Australian study where AI-based screening took about 7 minutes per patient, but also to outperform manual screenings in both accuracy and patient preference.

AI’s ability to analyse fundus photographs or OCT images at primary care facilities simplifies the screening process, potentially improving patient compliance and significantly reducing ophthalmologists’ workloads. With AI providing immediate grading and recommendations for follow-up or referral, diabetic patients can more easily access and undergo screening, therefore enhancing the management of DR.

To ensure the efficacy and accuracy of AI-based diagnostic systems for diabetic retinopathy (DR), it is crucial to have a well-structured dataset that is divided into separate non-overlapping sets for training, validation, and testing. In the development of AI-based diagnostic systems for diseases such as diabetic retinopathy, the dataset is meticulously organised into three distinct categories—each with a specific function in the training and validation of the algorithm. The training set forms the foundation, where the AI algorithm learns to identify and interpret fundus photographs; this set must be extensive and comprise high-quality images that have been carefully evaluated and labelled by expert ophthalmologists. As per the guidelines provided by Chinese authorities, if the system uses fundus photographs, these images should be collected from a minimum of two different medical institutions to ensure a varied and comprehensive learning experience. Concurrently, the validation set plays a pivotal role in refining the AI parameters, acting as a tool for algorithm optimisation during the development process. Lastly, the testing set is paramount for the real-world evaluation of the AI system’s clinical performance. To preserve the integrity of the results, this set is kept separate from the training and validation sets, preventing any potential biases that could skew the system’s accuracy in practical applications.

The training set should have a diverse range of images, including at least 1,000 single-field FPs or 1,000 pairs of two-field FPs, 500 non-readable FP images or pairs, and 500 images or pairs showing other fundus diseases besides DR. The images should be graded by at least three qualified ophthalmologists, with the majority opinion determining the final grade. For standard testing, a set should include 5,000 FPs or pairs, with no fewer than 2,500 images or pairs for DR stage I and above, and 500 images or pairs for other fundus diseases. A random selection of 2,000 images or pairs should be used to evaluate the AI system’s performance on the DR stages.

Current research has indicated some issues with the training sets used in existing AI systems. These include the use of FPs from a single source and including fewer than the recommended 500 non-readable images or pairs. Furthermore, some training sets sourced from online datasets do not provide access to important patient demographics like gender and age, which can be crucial for comprehensive training and accurate diagnostics.

The Iowa Detection Program (IDP) is an early example of an AI system for diabetic retinopathy (DR) screening that showed promise in Caucasian and African populations by grading fundus photographs (FP) and identifying characteristic lesions, albeit without employing deep learning (DL) techniques. Its sensitivity was commendable, but it suffered from low specificity. In contrast, IDx-DR incorporated a convolutional neural network (CNN) into the IDP framework, enhancing the specificity of DR detection. Clinical studies highlighted that while IDx-DR’s sensitivity in real-world settings didn’t quite match its testing set performance, it nonetheless demonstrated a satisfactory balance of sensitivity and specificity.

EyeArt expanded AI’s reach into mobile technology, becoming the first system to detect DR using smartphones. A study in India involving 296 type 2 diabetes patients revealed a very high sensitivity and reasonable specificity, proving its potential for remote DR screening. Moreover, systems like Google’s AI for DR screening can adjust sensitivity and specificity thresholds to meet clinical needs, suggesting that a hybrid approach of AI and manual screening could maximise efficiency and minimize missed referable DR cases.

However, most AI systems for DR rely on FPs, which are limited to two dimensions and can only detect diabetic macular edema (DME) through the presence of hard exudates in the posterior pole, potentially missing some cases. Optical coherence tomography (OCT), with its higher detection rate for DME, offers a more advanced diagnostic tool. Combining OCT with AI has led to the development of systems with impressive sensitivity, specificity, and area under the curve (AUC) metrics, as reflected in various studies. Despite these advancements, challenges such as accessibility remain, especially in resource-limited areas, as demonstrated by Hwang et al’s AI system for OCT, which still necessitates OCT equipment and the transfer of images to a smartphone, indicating that issues of accessibility for patients in underserved regions persist.

The landscape of AI-based diagnostic systems for diabetic retinopathy (DR) is expansive, yet it confronts numerous challenges. Many systems are trained on online datasets such as Messidor and EyePACS, which are limited by homogeneity in image sources and quality, as well as disease scope. These datasets often fail to encapsulate the diversity of real-world clinical environments, leading to potential misdiagnoses. A lack of standardised protocols for algorithm training exacerbates this, with the variability in sample sizes, image quality, and study designs from different sources undermining the generalisability of these AI systems.

Furthermore, while most research adheres to the International Clinical Diabetic Retinopathy Severity Scale for classifying DR severity, debates continue about its suitability. Some argue that classifications like the Early Treatment Diabetic Retinopathy Study may be more appropriate, as they could reduce unnecessary referrals by better reflecting the slower progression of milder DR forms. Inconsistencies in classification standards among studies affect both algorithm validity and cross-study comparisons.

Compounding these issues is the absence of a unified criterion for evaluating AI algorithms, with significant discrepancies in testing sets and performance metrics such as sensitivity, specificity, and area under the curve (AUC) across studies. Without universal benchmarks, comparing and validating these tools remains challenging. Moreover, AI diagnostics suffer from the “black box” phenomenon—the opaque nature of the decision-making process within AI systems. This obscurity impedes understanding and trust in the algorithms, as users cannot ascertain the rationale behind the AI’s assessments or intervene if necessary.

Legal and ethical concerns also arise, particularly regarding liability for misdiagnoses. The responsibility cannot squarely fall on either the developers or the medical practitioners using AI systems. Presently, this has restricted AI’s application primarily to DR screening. When compounded with obstacles such as cataracts, unclear media, or poor patient cooperation, the reliance on AI is reduced, necessitating ophthalmologist involvement.

Patient data security represents another critical issue. As AI systems for diabetes screening could process vast amounts of personal information, ensuring this data’s use solely for medical purposes and preventing breaches is paramount.

Finally, there’s the limitation of disease specificity in AI systems, where most are trained to detect only DR during fundus examinations. However, some studies have reported AI systems capable of identifying multiple conditions simultaneously, like age-related macular degeneration alongside DR, which could streamline diagnostic processes if widely adopted. Addressing these multifaceted challenges is crucial for the advancement and reliable integration of AI into ophthalmic diagnostics.

Artificial intelligence (AI) holds considerable promise in the field of diabetic retinopathy (DR) screening and diagnosis, with the potential to reshape current approaches significantly. The future could see the proliferation of AI systems designed for portable devices, such as smartphones, enabling patients to conduct DR screenings at home, which may drastically reduce the dependency on professional medical staff and advanced medical equipment. This shift could make DR screening much more accessible, particularly under the constraints imposed by events like the COVID-19 pandemic, where telemedicine’s importance has surged, providing vast benefits and convenience to both patients and healthcare providers.

Most AI-assisted DR screening systems currently rely on traditional fundus imaging. However, as newer examination techniques evolve, AI is expected to integrate with diverse types of ocular assessments, such as multispectral fundus imaging and optical coherence tomography (OCT), which could further enhance diagnostic accuracy. Beyond screening, AI is poised to play a crucial role in DR diagnosis. Some studies have already shown that AI can match or even surpass the sensitivity of human ophthalmologists, supporting the potential of AI-assisted systems to augment the diagnostic process with higher precision and efficiency.

Overall, in countries where DR screening programs are established, integrating AI-based diagnostic systems could significantly alleviate human resource burdens and boost operational efficiency. Despite the optimism, the datasets currently used to train AI algorithms are somewhat restricted in scope. For AI to be more broadly applicable in clinical settings, it’s essential to leverage diverse clinical resources to create more varied datasets and to refine standards for image quality and labeling, ensuring AI systems are both standardised and effective. At this juncture, the technology is not yet at a point where it can replace ophthalmologists entirely. Therefore, in the interim, a combined approach where AI complements the work of medical professionals may offer the most realistic and advantageous path forward for the clinical adoption of AI in DR management.

Links

https://www.gov.uk/guidance/diabetic-eye-screening-programme-overview

https://drc.bmj.com/content/5/1/e000333

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559815/

https://www.mdpi.com/2504-2289/6/4/152

https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30250-8/fulltext

https://diabetesatlas.org/

https://pubmed.ncbi.nlm.nih.gov/20580421/

https://www.aao.org/education/preferred-practice-pattern/diabetic-retinopathy-ppp

https://pubmed.ncbi.nlm.nih.gov/27726962/

https://onlinelibrary.wiley.com/doi/10.1046/j.1464-5491.2000.00338.x

https://iovs.arvojournals.org/article.aspx?articleid=2565719

An Examination of the Four Pillars of Medical Ethics in the UK Healthcare System

First published 2023

Medical ethics stands at the intersection of science, philosophy, and humanity, guiding healthcare professionals in delivering care that is not only medically sound but also morally justifiable. Within this broad spectrum of medical ethics, four primary principles emerge as guiding pillars: autonomy, beneficence, non-maleficence, and justice. While these principles form a foundational framework, their practical application in the rapidly evolving world of medicine, especially in the UK healthcare system, requires a deeper exploration. This essay seeks to delve into these four pillars, evaluating their relevance and application in contemporary medical practice in the UK. By examining their interplay with institutional guidelines, real-world scenarios, and the overarching philosophy of care, we aim to shed light on the intricate balance between ethical theory and medical practice.

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Autonomy, the first of these principles, champions the right of patients to make informed decisions about their own healthcare. It underscores the importance of informed consent, where patients are provided with all necessary information to make a decision regarding their treatment. The concept of autonomy intersects with the objectivity of science in that while science can provide evidence and data, it is up to the individual to decide how to act upon that information. This raises questions about the nature of morality: is it moral to prioritise individual choice over evidence-based recommendations? Philosophy of science contends that while science can provide facts, it cannot dictate values. Hence, autonomy remains paramount, recognising the patient’s values and beliefs.

Beneficence, the second principle, mandates that healthcare professionals act in the best interest of the patient. It encourages actions that promote the well-being of the patient. The intersection of beneficence with the objectivity of science is evident when considering treatments. Science can offer various treatment options based on evidence, but it is the ethical duty of the physician to recommend what they believe is best for the patient. However, this leads to philosophical debates. Can a universally ‘best’ treatment be identified, or is it subjective and variable based on individual circumstances?

Non-maleficence, often encapsulated by the phrase “do no harm,” requires medical professionals to ensure that potential harms of a treatment do not outweigh its benefits. This principle aligns closely with the scientific method, which stresses rigorous testing and evaluation to ensure the safety of medical interventions. However, the nature of morality and the philosophy of science come into play when considering cases like that of George Best, a renowned footballer who underwent a liver transplant but later resumed alcohol consumption. While the transplant, in itself, adhered to the principle of non-maleficence, questions arise about the morality of allocating resources to someone who might not adhere to post-operative recommendations.

Lastly, justice pertains to the fair distribution of healthcare resources and ensuring that all patients have equal access to care. In an era where scientific advancements often come with high costs, the challenge lies in ensuring equitable distribution. The philosophy of science emphasises evidence-based allocation, but the nature of morality may argue for a more compassionate approach, especially for marginalised communities.

In the UK, the four pillars of medical ethics are more than just theoretical constructs; they form the backbone of practical medical guidelines and standards for doctors. However, as significant as these pillars are, they offer a broad moral framework rather than specific instructions for every conceivable ethical dilemma a physician might face. This gap between theory and practice is addressed by institutions like the General Medical Council (GMC), which provides more detailed guidance on ethical standards.

The GMC’s “Good Medical Practice” outlines the core ethical values and attributes doctors should embody. It categorises these values into four domains: knowledge, skills, and behaviours; safety and quality; communication, partnership, and teamwork; and maintaining trust. This guidance not only specifies what is expected of doctors but also provides a benchmark for patients, colleagues, and managers regarding the professional standards they should anticipate from medical practitioners. Additionally, this document is foundational for doctors’ annual appraisals and revalidation processes.

A crucial facet of medical ethics is the principle of confidentiality, sometimes even referred to as the ‘fifth pillar’. The GMC’s guidelines on confidentiality and health records underscore the sanctity of patient information and articulate eight core principles that govern the handling of such data. These principles emphasise the minimal and lawful use of personal data, the importance of protection against unauthorised access, and the significance of patient consent in information disclosure.

Decision-making and consent are other paramount areas within medical ethics. Good Medical Practice promotes patient-centered care, advocating for the active participation of the patient in decisions about their treatment. The GMC lays out seven core principles for decision-making and consent, emphasising the rights of patients to be involved and supported in their care decisions.

Furthermore, doctors in the UK are often in leadership roles, making decisions that impact patient care directly. Therefore, it’s imperative for them to demonstrate qualities such as teamwork, leadership, and resource efficiency, as laid out by the GMC. Moreover, professionalism isn’t just a buzzword; it’s a lived reality for doctors. The GMC offers guidance on maintaining professional boundaries, even in modern contexts such as social media.

Specific populations, like children and young people, require additional ethical considerations. Comprehensive guidance is available from the GMC, BMA, and MDU to ensure the best interests of younger patients are always prioritised.

Prescribing medication, especially controlled drugs, is another area that demands strict ethical adherence. The GMC provides explicit guidelines to ensure the safety and appropriateness of prescriptions. End-of-life care, a particularly sensitive area, has its own set of guidelines, emphasising the respect, dignity, and compassion required when dealing with patients nearing the end of their lives.

Lastly, in an era where transparency in healthcare is paramount, the principles of candour and raising concerns are vital. Doctors have a duty to voice concerns if they believe patient care is being compromised. The GMC provides guidance on how to navigate such situations, ensuring patient safety remains at the forefront.

The delicate balance of ethics and practice in the UK healthcare system, as elucidated through the four pillars of medical ethics, encounters its most profound challenges when confronted with real-world events that shake its foundation. The recent conviction of Lucy Letby is one such event that has sent shockwaves throughout the medical community. A trusted figure in her role as a nurse, Letby’s crimes against the most vulnerable patients – infants – have shattered the inherent trust that forms the bedrock of the patient-healthcare professional relationship.

In examining the foundations of medical ethics, one can’t help but question how such a breach could occur within a system that prioritises patient well-being, autonomy, and justice. The Letby case underscores the limitations of theoretical frameworks when faced with the practical realities and vulnerabilities of the healthcare system. While the pillars of medical ethics provide a moral compass, it becomes evident that they are only as effective as the systems and checks in place to enforce them.

The systemic weaknesses exposed by Letby’s actions necessitate a thorough introspection into how the NHS vets, monitors, and supports its healthcare professionals. It brings to the forefront questions about the ethical and professional standards maintained within healthcare organisations. How could such grave misconduct remain undetected? Are there adequate systems in place that encourage staff to voice concerns without fear of retaliation? The Letby case is a poignant reminder that the pillars of medical ethics need to be bolstered by stringent oversight, transparent communication, and an unwavering commitment to patient safety.

Moreover, the impact of this case extends beyond the immediate victims. It has broader implications on staff well-being and the overall culture within healthcare institutions. Healthcare professionals operate in high-stress environments, often dealing with life and death scenarios. The emotional and psychological toll of such an environment can’t be overlooked. For the pillars of medical ethics to be effectively upheld, there needs to be a supportive environment that prioritises the mental and emotional well-being of its staff.

The media’s role in the Letby case also brings forth the challenges healthcare leaders face in managing public perception and trust. Crisis management and media relations become crucial in such high-stake scenarios. The pillars of medical ethics, while providing moral guidance, need to be complemented with strategies that address public concerns, ensuring that the core values of the NHS remain unblemished.

In conclusion, the intersection of ethical principles and real-world challenges in the UK healthcare system is complex. The Lucy Letby case serves as a stark reminder of this complexity, urging leaders and professionals to continuously reevaluate and fortify the systems that underpin the ethical foundations of medical practice. While the four pillars of medical ethics provide a guiding light, the journey towards ensuring their steadfast application is ongoing, demanding vigilance, introspection, and an unwavering commitment to the sanctity of patient care.

Links

https://www.bmj.com/careers/article/ethical-guidance-for-doctors

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2540719/

https://www.standard.co.uk/hp/front/will-bests-liver-last-7228701.html

https://www.bbc.co.uk/news/uk-england-merseyside-66569311

Ethical and Practical Dimensions of Big Data in the NHS

First published 2022

Understanding the concept of Big Data in the field of medicine is relatively straightforward: it involves using extensive volumes of medical information to uncover trends or correlations that may not be discernible in smaller datasets. However, one might wonder why Big Data hasn’t been more widely applied in this context in the NHS. What sets industries like Google, Netflix, and Amazon apart, enabling them to effectively harness Big Data for providing precise and personalised real-time information based on online search and purchasing activities, compared to the National Health Service?

An examination of these thriving industries reveals a key distinction: they have access to data that is freely and openly provided by customers and is delivered directly and centrally to the respective companies. This wealth of detailed data encompasses individual preferences and aversions, facilitating accurate predictions for future online interactions.

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Could it be feasible to use extensive volumes of medical data, derived from individual patient records, to uncover new risks or therapeutic possibilities that can then be applied on a personalised level to enhance patient outcomes? When we compare the healthcare industry to other sectors, the situation is notably distinct. In healthcare, medical records, which contain highly sensitive personal information, are carefully protected and not openly accessible. Typically, data remains isolated within clinic or hospital records, lacking a centralised system for sharing that would enable the rapidity and scale of data necessary to fully harness Big Data techniques. Medical data is also intricate and less readily “usable” in comparison to the data provided to major corporations, often requiring processing to render it into a readily usable format. Additionally, the technical infrastructure required for the movement, manipulation, and management of medical data is not readily accessible.

In a general sense, significant obstacles exist in terms of accessing data, and these obstacles encompass both philosophical and practical dimensions. To enhance the transformation of existing data into novel healthcare solutions, several aspects must be tackled. These encompass, among other things, the gathering and standardisation of diverse datasets, the careful curation of the resulting refined data, securing prior informed consent for the use of de-identified data, and the capacity to offer these datasets for further use by the healthcare and research communities.

To gain a deeper understanding of the opportunities within the clinical field and why the adoption and adaptation of these techniques haven’t been a straightforward transfer from other industries, it’s beneficial to examine both the similarities and distinctions between clinical Big Data and data used in other sectors. Industries typically work with what can truly be labeled as Big Data, characterised by substantial volume, rapid velocity, and diversity, but often exhibit low information density. These data are frequently freely obtained, stemming from an individual’s incidental digital activities in exchange for services, serving as a proxy indicator for specific behaviors that enable the anticipation of patterns, trends, and outcomes. Essentially, such data are acquired at the moment services are accessed, and they either exist or do not exist.

Comparable data can be found in clinical settings as well. For instance, during surgery, there is the continuous monitoring of physiological parameters through multiple devices, generating substantial volume, high velocity, and diverse data that necessitate real-time processing to identify data falling outside predefined thresholds, prompting immediate intervention by attending clinicians. On the other hand, there are instances of lower-volume data, such as the day-to-day accumulation of clinical test results, which contribute to updated diagnoses and medical management. Likewise, the analysis of population-based clinical data has the capability to forecast trends in public health, like predicting the timing of infectious disease outbreaks. In this context, velocity offers “real-time” prospective insights and allows for trend forecasting. The origin of the data is attributable to its source, whether it be a patient in the operating room or a specific geographical population experiencing the winter flu season.

The primary use of this real-time information is to forecast future trends through predictive modeling, without attempting to provide explanations for the findings. However, a more immediate focus of Big Data is the extensive clinical data already stored in hospitals, aiming to address the question of why specific events are occurring. These data have the potential, provided they can be effectively integrated and analysed, to offer insights into the causes of diseases, enable their detection and diagnosis, guide treatment and management, and facilitate the development of future drugs and interventions.

To assimilate this data, substantial computing power well beyond what an individual can manage is required, thus fitting the definition of Big Data. The data will largely be population-specific and then applied to individuals (e.g., examining patient groups with different disease types or processes to gain new insights for individual benefit). Importantly, this data will be collected retrospectively, rather than being acquired prospectively.

Lastly, while non-medical Big Data has often been incidental, freely available, and of low information density, clinical Big Data will be intentionally gathered, incurring costs (borne by someone), and characterised by high information density. This is more akin to business intelligence, where Big Data techniques are needed to derive measurements and detect trends (not just predict them) that would otherwise remain concealed or beyond human inspection alone.

Patient data, regardless of its nature, often seems to be associated with the medical institutions that hold it. However, it’s essential to recognise that these institutions function as custodians of the data; the data itself belongs to the patients. Access to and use of this data beyond clinical purposes necessitate the consent of the patients. This immediately poses a challenge when it comes to the rapid use of the extensive data already contained in clinical records.

While retrospective, hypothesis-driven research can be conducted on specific anonymised data, as is common in research, it’s important to note that once a study concludes, the data should ideally be deleted. This approach contradicts the principles of advancing medical knowledge, particularly when employing Big Data techniques that involve thousands to millions of data points requiring significant processing. Losing such valuable data at the conclusion of a project is counterproductive.

Prospective patient consent to store and use their data offers a more robust model, enabling the accumulation of substantial datasets that can be subsequently subjected to hypothesis-driven research questions. Although foregoing the use of existing retrospective data may appear wasteful, the speed (velocity) at which new data are generated in the NHS makes consented data far more valuable. Acquiring patient consent, however, often necessitates on-site personnel to engage with patients. Alternatively, options like patients granting blanket consent for data usage may be viable, provided that such consent is fully informed.

This dilemma has come to the forefront due to the implementation of the EU General Data Protection Regulation (GDPR) in 2018, triggering an international discourse on the sharing of Big Data in healthcare. In 2021, the UK government commissioned the ‘Goldacre Review’ into how to create big data sets, and how to ensure the “efficient and safe use of health data for research and analysis can benefit patients and the healthcare sector”. The review concluded that it is essential to invest in safe and trusted platforms for data and high-quality data curation to allow researchers and AI creators to realise the potential of the data. This data “represents deeply buried treasure, that can help prevent suffering and death, around the planet, on a biblical scale.”

Following the Goldacre Review, the UK government launched the ‘National Data Strategy’, which supports the creation of high-quality big data, and ‘Data Saves Lives’, which specifically sets out to “make better use of healthcare data and to save lives”. The ‘Data Saves Lives’ initiative exemplifies the progressive approach the UK has taken towards harnessing the power of Big Data in healthcare. Recognising the transformative potential of large-scale medical datasets, the initiative seeks to responsibly leverage patient data to drive innovations in medical research and clinical care. There’s a recognition that while industries like Netflix or Amazon can instantly access and analyse user data, healthcare systems globally, including the NHS, must manoeuvre through more complex ethical, legal, and logistical terrains. Patient data is not just another statistic; it is a deeply personal narrative that holds the key to both individual and public health solutions. Ensuring its privacy, obtaining informed consent, and simultaneously making it available for meaningful research is a balancing act, one that the NHS is learning to master.

In conclusion, the use of Big Data in the realm of healthcare differs significantly from its application in other industries, primarily due to the sensitive nature of the data and the ethical implications of its use. The potential benefits of harnessing this data are immense, from individualised treatments to large-scale public health interventions. Yet, the complexities surrounding its access and use necessitate a thoughtful, patient-centric approach. Initiatives like ‘Data Saves Lives’ signify the healthcare sector’s commitment to unlocking the potential of Big Data, while ensuring patients remain at the heart of the conversation. As the NHS and other global healthcare entities navigate this promising frontier, the underlying ethos must always prioritise patient welfare, trust, and transparency.

Links

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502603/

https://www.gov.uk/government/publications/better-broader-safer-using-health-data-for-research-and-analysis

https://www.gov.uk/government/publications/uk-national-data-strategy/national-data-strategy

https://digital.nhs.uk/services/national-data-opt-out/understanding-the-national-data-opt-out/confidential-patient-information

Turing’s Vision: Navigating the Landscape of Ethical and Safe AI

First published 2022; revised 2023

In the dawn of the artificial intelligence era, there is an imperative need to navigate the complexities of AI ethics and safety. Ensuring that AI systems are both safe and ethically sound is no longer just a theoretical concern but a pressing practical issue that affects the global threads of industry, governance, and society at large. Drawing insights from Leslie, D. (2019) in “Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector”, published by The Alan Turing Institute, this essay explores the varied dimensions of AI’s responsible design and implementation.

The Alan Turing Institute forges its position as an aspirational, world-leading hub that examines the technical intricacies that underpin safe, ethical, and trustworthy AI. Committed to fostering responsible innovation and pioneering research breakthroughs, the Institute aims to go beyond mere theoretical discourses. It envisions a future where AI not only advances in capabilities but also upholds the core values of transparency, fairness, robustness, and human-centered design. Such an ambition necessitates a commitment to advancing AI transparency, ensuring the fairness of algorithmic systems, forging robust systems resilient against external threats, and cultivating AI-human collaborations that maintain human control.

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However, the quest to realise this vision is not an isolated endeavour. It requires broad, interdisciplinary collaborations, connecting the dots between technical experts, industry leaders, policy architects, and the public. Aligning with the UK government’s Industrial Strategy and meeting the burgeoning global demand for informed guidance in AI ethics, the Institute’s strategy serves as a blueprint for those committed to the responsible growth of AI. However, it’s essential to remember that the responsible evolution of AI is not just about mastering the technology but understanding its implications for the broader context of our society.

The dawn of the information age has been marked by an extraordinary convergence of factors: the expansive availability of big data, the unparalleled speed and reach of cloud computing platforms, and the maturation of intricate machine learning algorithms. This synergy has propelled us into an era of unmatched human potential, characterised by a digitally interwoven world where the power of AI stands as a beacon of societal improvement.

Already, we witness the profound impact of AI across various sectors. Essential social domains such as healthcare, education, transportation, food supply, energy, and environmental management have all been beneficiaries of AI-driven innovations. These accomplishments, however significant they may appear now, are perhaps only the tip of the iceberg. AI’s very nature, its inherent capability to evolve and refine itself with increased access to data and surging computing power, guarantees its continuous ascent in efficacy and utility. As we navigate further into the information age, it’s conceivable that AI will soon stand at the forefront, guiding the progression of critical public interests and shaping the contours of sustainable human development.

Such a vision, where AI aids humanity in addressing its most pressing challenges, is undeniably exhilarating. Yet, like any frontier technology that’s rapidly evolving, AI’s journey is fraught with pitfalls. A steep learning trajectory ensures that errors, misjudgments, and unintended consequences are not just possible but inevitable. AI, despite its immense promise, is not immune to these challenges.

Addressing these challenges is not a mere recommendation but a necessity. It is imperative to prioritise AI ethics and safety to ensure its responsible evolution and to maximise its public benefit. This means an in-depth integration of social and ethical considerations into every facet of AI deployment. It calls for a harmonised effort, requiring data scientists, product managers, data engineers, domain experts, and delivery managers to work in unison. Their collective goal? To align AI’s development with ethical values and principles that not only prevent harm but actively enhance the well-being of communities that come under its influence.

The emergence of the field of AI ethics is a testament to this necessity. Born out of a growing recognition of the potential individual and societal harms stemming from AI’s misuse, poor design, or unforeseen repercussions, AI ethics seeks to provide a compass by which we navigate the AI-driven future responsibly.

Understanding the evolution of AI and its implications requires us to first recognise the genesis of AI ethics. The eminent cognitive scientist and AI trailblazer, Marvin Minsky, once described AI as the art of enabling computers to perform tasks that, when done by humans, necessitate intelligence. This fundamental definition highlights a crucial aspect of the discourse surrounding AI: humans, when undertaking tasks necessitating intelligence, are held to standards of reliability, accuracy, and sound reasoning. We expect them to justify their decisions, and to act with fairness, equity, and reasonableness in their interactions.

However, the rise and spread of AI technologies have reshaped this landscape. As AI systems take over myriad cognitive functions, they introduce a conundrum. Unlike humans, these algorithmic processes aren’t directly accountable for their actions, nor can they be held morally responsible for the outcomes they produce. Essentially, while AI systems exhibit a form of ‘smart agency’, they lack inherent moral responsibility, creating a discernible ethical void.

Addressing this void has become paramount, giving birth to a host of frameworks within AI ethics. One such framework is the FAST Track Principles, which stands for Fairness, Accountability, Sustainability, and Transparency. These principles are designed to bridge the gap between AI’s capabilities and its intrinsic moral void. To foster an environment conducive to responsible AI development, it is vital that every stakeholder, from data scientists to policy experts, familiarises themselves with the FAST Track Principles. These principles should guide actions and decisions throughout the AI project lifecycle, underscoring the idea that creating ethical AI is a collective endeavor.

Delving deeper into the principle of fairness, one must remember that while AI systems might project a veneer of neutrality, they are ultimately products of human design. Humans, with all their inherent biases and contextual limitations, play a pivotal role in AI’s creation. At any stage of an AI project, from data extraction to model building, the spectres of human error, prejudice, and misjudgment can introduce biases. Moreover, AI systems often derive their accuracy by analysing data that might encapsulate age-old societal biases and discriminations, further complicating the fairness equation.

Addressing fairness in AI is far from straightforward. There isn’t a singular, foolproof method to eliminate biases or ensure fairness. However, by adopting best practices that focus on fairness-aware design and implementation, there’s potential to create systems that yield just and equitable outcomes. One foundational approach to fairness is the principle of discriminatory non-harm. It mandates that AI innovations should not result in harm due to biased or discriminatory outcomes. This principle, while seemingly basic, serves as a cornerstone, directing the development and deployment of AI systems towards a more equitable and fair future.

The Principle of Discriminatory Non-Harm sets forth that AI system designers and users should be deeply committed to reducing biases and preventing discriminatory outputs, especially when dealing with social or demographic data. This implies a few specific obligations. First, AI systems should be built upon data that is representative, accurate, and generalisable, ensuring “Data Fairness.” Second, the systems’ design should not include any variables, features, or processes that are morally objectionable or unjustifiable – this is “Design Fairness.” The systems should also be crafted to avoid producing discriminatory effects on individuals or groups – ensuring “Outcome Fairness.” Lastly, the onus is on the users to be adequately trained to use AI systems responsibly, embodying “Implementation Fairness.”

When considering the concept of Accountability in AI, the best practices for data processing as mentioned in Principle 6 of the Data Ethics Framework come to mind. However, the ever-evolving AI landscape brings forward distinct challenges, especially in public sector accountability. Two major challenges emerge: the “accountability gap” and the multifaceted nature of AI production processes. Automated decisions, inherently, are not self-explanatory. Unlike human agents, statistical models and AI’s underlying infrastructure don’t bear moral responsibility, creating a void in accountability. Coupled with this is the intricate nature of AI project deliveries involving a myriad of stakeholders, making it a daunting task to pinpoint responsibility if an AI system’s implementation has adverse consequences.

To address these challenges, it’s imperative to adopt a comprehensive approach to accountability that encompasses both Answerability and Auditability. Answerability stresses that human creators and users of AI systems should take full responsibility for the algorithmically-driven decisions. They should be ready to provide clear, coherent, and non-technical explanations for these decisions, ensuring that every stage of the AI process is accountable. Auditability, on the other hand, focuses on how to hold these AI system designers and implementers accountable. It emphasises the demonstration of both responsible design and use practices, and the justifiability of the outcomes.

Another critical pillar is Sustainability. AI system designers and users must be continually attuned to the long-term and transformative effects their technologies might have on individuals and society at large. This proactive awareness ensures that the systems not only address the immediate needs but also consider the long-term societal impacts.

In tandem with sustainability is Safety. Besides considering the broader social ramifications of an AI system, it’s essential to address its technical sustainability and safety. Given that AI operates in an unpredictable environment, achieving technical safety becomes a challenging task. However, the importance of building a safe and reliable AI system cannot be overstated, especially when potential failures could result in harmful consequences and erode public trust. To achieve this, emphasis must be placed on the core technical objectives of accuracy, reliability, security, and robustness. This involves rigorous testing, consistent validation, and frequent reassessment of the system. Moreover, effective oversight mechanisms need to be integrated into the system’s real-world operation to ensure that it functions safely and as intended.

The intrinsic challenges of accuracy in artificial intelligence systems can be linked to the inherent complexities and unpredictability of the real world. When trying to model this chaotic reality, it’s a significant task to ensure that an AI system’s predictions or classifications are precise. Data noise, which is unavoidable, combined with the potential that a model might not capture all aspects of the underlying patterns and changes in data over time, can all contribute to these challenges.

On the other hand, the reliability of an AI system rests on its ability to consistently function in line with its intended design and purpose. This means that if a system is deemed reliable, users can trust that its operations will adhere to its set specifications, bolstering user confidence in the safety and predictability of its outcomes.

AI systems also face threats on the security front. Security is not just about safeguarding an AI system from potential external threats but also ensuring that the system’s architecture remains uncompromised and that any data or information within it remains confidential. This integrity is paramount, especially when considering the potential adversarial threats that AI systems might face.

Robustness in AI, meanwhile, centres on an AI system’s ability to function effectively even under less than ideal conditions. Whether these conditions arise from intentional adversarial actions, human errors, or misalignments in automated learning objectives, the system’s ability to maintain its integrity is a testament to its robustness.

One of the more nuanced challenges that machine learning models face is the phenomenon of concept drift. When the historical data, which informs the model’s understanding, becomes outdated or misaligned with current realities, the model’s accuracy and reliability can suffer. Therefore, staying attuned to changes in the underlying data distribution is vital. Ensuring that the technical team is aware of the latest research on detecting and managing concept drift will be crucial to the continued success of AI projects.

Another pressing concern in the realm of AI is adversarial attacks. These attacks cleverly manipulate input data, causing AI models to make grossly incorrect predictions or classifications. The subtle nature of these perturbations can lead to significant ramifications, especially in critical systems like medical imaging or autonomous vehicles. Recognising these vulnerabilities, there has been a surge in research in the domain of adversarial machine learning, aiming to safeguard AI systems from these subtle yet disruptive inputs.

Equally concerning is the threat of data poisoning, where the very data that trains an AI system is tampered with, causing the system to generate inaccurate or harmful outputs. This kind of attack can be especially sinister as it might incorporate ‘backdoors’ into the system, which when triggered, can cause malfunctions. Therefore, beyond technical solutions, it becomes imperative to source data responsibly and ensure its integrity throughout the data handling process. The emphasis should be on responsible data management practices to ensure data quality throughout the system’s lifecycle.

In the world of artificial intelligence, the term “transparency” has taken on a nuanced and specialised meaning. While the everyday usage of the term typically evokes notions of clarity, openness, and straightforwardness, in AI ethics, transparency becomes even more multifaceted. One aspect of this is the capacity for AI systems to be interpretable. That is, those interacting with an AI system should be able to decipher how and why the system made a particular decision or acted in a certain way. This kind of transparency is about shedding light on the internal workings of the often enigmatic AI mechanisms, allowing for greater understanding and trust.

Furthermore, transparency isn’t limited to merely understanding the “how” and “why” of AI decisions. It also encompasses the ethical considerations behind both the design and deployment of AI systems. When AI systems are said to be transparent, it implies that they can be justified as ethical, unbiased, trustworthy, and safety-oriented both in their creation and their outcomes. This dual focus on process and product is vital.

In developing AI, teams are tasked with several responsibilities to ensure this two-tiered transparency. First, from a process perspective, there is a need to assure all stakeholders that the entire journey of creating the AI system was ethically sound, unbiased, and instilled with measures ensuring trust and safety. This includes not just designing with these values in mind but also ensuring auditability at every stage.

Secondly, when it comes to the outcome or product of AI, there’s the obligation to make sure that any decision made by the AI system is elucidated in ways that are understandable to non-experts. The explanations shouldn’t merely regurgitate the mathematical or technical jargon but should be phrased in relatable terms, reflecting societal contexts. Furthermore, the results or behaviors of the AI should be defensible, fitting within parameters of fairness, trustworthiness, and ethical appropriateness.

In addition to these tasks, there’s a broader need for professional and institutional transparency. Every individual involved in the AI’s development and deployment should adhere to stringent standards that emphasise values like integrity, honesty, and neutrality. Their primary allegiance should be to the public’s best interests, superseding other considerations.

Moreover, throughout the AI development process, there should be an open channel for public oversight. Of course, certain information may need to remain confidential for valid reasons, like ensuring bad actors can’t exploit the system. But, by and large, the emphasis should be on openness.

Transitioning into the structural aspects of AI development, a Process-Based Governance (PBG) Framework emerges as a crucial tool. Such a framework is pivotal for integrating ethical considerations and best practices seamlessly into the actual development process. The guide might delve into specifics like the CRISP-DM, but it’s worth noting that the principles of responsible AI development can be incorporated into other workflow models, including KDD and SEMMA. Adopting such a framework helps ensure that the values underpinning ethical AI are not just theoretical but find active expression in every phase of the AI’s life cycle.

Alan Turing’s simple sketch in 1936 was nothing short of revolutionary. With just a linear tape, symbols, and a set of rules, he demystified the very essence of calculations, giving birth to the conceptual foundation of the modern computer. His Turing machine wasn’t just a solution to the enigma of effective calculations, it was the conceptual forerunner of the digital revolution we live in today. This innovative leap, stemming from a quiet room at Kings College, Cambridge, is foundational to our digital landscape.

Fast forward to our present day, and we find ourselves immersed in a world where the lines between the physical and digital blur. The seamless interplay of connected devices, sophisticated algorithms, and vast cloud computing platforms is redefining our very existence. Technologies like the Internet of Things and edge computing are not just changing the way we live and work; they’re reshaping the very fabric of our society. AI is becoming more than just a tool or a technology; it is rapidly emerging as the fulcrum upon which our future balances. The possibilities it presents, both optimistic and cautionary, are monumental. It’s essential to realise that the trajectory of AI’s impact lies in our hands. The decisions we make today will shape the society of tomorrow, and the implications of these choices weigh heavily on our collective conscience.

It’s paramount to see that artificial intelligence isn’t just about codes and algorithms. It’s about humanity, our aspirations, our values, and our shared vision for the future. In many ways, the guide on AI ethics and safety serves as a compass, echoing Turing’s ethos by emphasising that the realm of AI, at its core, remains a profoundly human domain. Every line of code, every algorithmic model, every deployment carries with it a piece of human intention, purpose, and responsibility.

In essence, understanding the ethics and safety of AI isn’t just about mitigating risks or optimising outputs. It’s about introspection and realising that behind every technological advancement lie human choices. Responsible innovation isn’t just a catchphrase; it’s a call to action. Only by staying grounded in our shared ethical values and purpose-driven intentions can we truly harness AI’s potential. Let’s not just be passive recipients of technology’s gifts. Instead, let’s actively shape its direction, ensuring that our collective digital future resonates with our shared vision of humanity’s greatest aspirations.

Links

https://www.turing.ac.uk/news/publications/understanding-artificial-intelligence-ethics-and-safety

https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf

Issues in Patient Confidentiality

First published 2022; revised 2023

As healthcare providers, maintaining a patient’s confidentiality, human dignity and privacy is expected at all times. Ethical health research and privacy protections both provide valuable benefits to society. Health research is vital to improving human health and health care. Protecting patients involved in research from harm and preserving their rights is essential to ethical research. The primary justification for protecting personal privacy is to protect the interests of individuals. In contrast, the primary justification for collecting personally identifiable health information for health research is to benefit society. But it is important to stress that privacy also has value at the societal level, because it permits complex activities, including research and public health activities to be carried out in ways that protect individuals’ dignity. At the same time, health research can benefit individuals, for example, when it facilitates access to new therapies, improved diagnostics, and more effective ways to prevent illness and deliver care. 

Contact

Whenever a patient visits a doctor or healthcare organisation, data is collected about them. This data can be broken down into three data categories: demographic, administrative, and medical. Demographic data includes name, address, contact details and NHS number, ethnicity, gender, sex. Administrative data includes details of appointments, or whether patients are waiting for a place in a health and care setting such as a care home or hospital ward. Medical data includes information such as symptoms, diagnosis, weight, medicines, treatments, and allergies, measurements and blood pressure.

Some patient data is confidential. If the data contains information from any two of the three categories, it is classed as confidential. Medical data is confidential if a patent can be identified from it. For example, if a document contained details of a patient’s condition, and their name and address, it would be confidential and is consequently subject to the Data Protection Act.

This is important, as confidential patient data is governed by GDPR, so there are strict rules around who the data can be shared with. Therefore, medical data must be anonymised or pseudonymised, thereby making it non-identifiable, if it is to be collected and used in trials. Unliked anonymised data, pseudonymised data can be securely rematched in databases by giving each patient a uniquely identifying pseudonym.

However even pseudonymisation can pose a risk to confidentiality. Cori Crider, a US-qualified lawyer from Foxglove Legal working in the area of technology, recently posted to Twitter: “slapping a sticker over your NHS number doesn’t suddenly mean your health record needs no protection … people are very easy to re-identify from pseudonymised data” and “Every hospital in England has been told to put patient data into Palantir’s software by the end of this month. We don’t see how this is lawful.” This statement underscores the potential dangers of relying solely on pseudonymisation for data protection, highlighting the ease with which individuals can be re-identified and the legal concerns surrounding the use of such data by external entities like Palantir.

It’s crucial to understand the implications of pseudonymised data use, especially when it falls into the hands of large technology companies with vast computational resources. When this type of data is not appropriately safeguarded, patients’ privacy can be at risk. Notably, re-identifying patients from pseudonymised data may lead to unintended consequences such as blackmail, discrimination, and other adverse personal and professional ramifications.

It also raises ethical questions about the role of tech companies in accessing and handling sensitive patient data. The potential misuse of such data for profit, as well as the risk of data breaches, are concerns that need addressing. Ensuring robust measures, regular audits, and transparent data handling processes can mitigate some of these risks. But as we tread into the future of data-driven healthcare, the debate between the need for comprehensive data for better patient outcomes and the preservation of individual privacy will intensify.

In conclusion, the balance between the vital role of health research and the imperative of patient confidentiality is a complex, nuanced issue. On one hand, the acquisition and analysis of medical data drives innovations in healthcare, offering the potential for improved treatments, diagnostics, and overall care. On the other hand, the sanctity of a patient’s personal information is a fundamental ethical obligation that healthcare providers and researchers must uphold. The demarcation of data into demographic, administrative, and medical categories highlights the intricacy of determining what information is confidential. The advent of pseudonymisation and anonymisation techniques, though valuable in the quest to protect identities, isn’t foolproof. Concerns, such as those voiced by experts like Crider, underscore the vulnerabilities inherent in even the most advanced data-protection methods. It is paramount that as technology and health research evolve, robust mechanisms for ensuring patient confidentiality are not only maintained but also constantly advanced. It is not just about the legal implications; it is about preserving the trust, dignity, and rights of every individual under healthcare.

Links

https://digital.nhs.uk/services/national- data-opt-out/understanding-the-national-data-opt-out/confidential-patient-information

https://www.gov.uk/data-protection

https://www.ncbi.nlm.nih.gov/books/NBK9579/

https://gdpr-info.eu/

💥Breaking: it's already happening.

Every hospital in England has been told to put patient data into Palantir's software by the end of this month. We don't see how this is lawful.

Big @openDemocracy – @thetimes investigation. Pls read and share: https://t.co/efKA0lT2qv

— Cori Crider (@cori_crider) March 17, 2023