Primary healthcare is a crucial strategy for achieving universal health coverage. South Asian countries are working to improve their primary healthcare system through their country specific policies designed in line with WHO health system framework using the six thematic pillars: Health Financing, Health Service delivery, Human Resource for Health, Health Information Systems, Governance, Essential Medicines and Technology, and an addition area of Cross-Sectoral Linkages [11]. Measuring the current accessibility of healthcare facilities and workforce availability is essential for improving healthcare standards and achieving universal health coverage in developing countries. Data-driven surveillance approaches are required that can provide rapid, reliable, and geographically scalable solutions to understand a) which communities and areas are most at risk of inequitable access and when, b) what barriers to health access exist, and c) how they can be overcome in ways tailored to the specific challenges faced by individual communities. We propose to harness current breakthroughs in Earth-observation (EO) technology, which provide the ability to generate accurate, up-to-date, publicly accessible, and reliable data, which is necessary for equitable access planning and resource allocation to ensure that vaccines, and other interventions reach everyone, particularly those in greatest need, during normal and crisis times. This requires collaboration among countries to identify evidence based solutions to shape health policy and interventions, and drive innovations and research in the region.

Artificial Intelligence (AI) for health has the potential to significantly change and improve healthcare. However in most African countries, identifying culturally and contextually attuned approaches for deploying these solutions is not well understood. To bridge this gap, we conduct a qualitative study to investigate the best practices, fairness indicators, and potential biases to mitigate when deploying AI for health in African countries, as well as explore opportunities where artificial intelligence could make a positive impact in health. We used a mixed methods approach combining in-depth interviews (IDIs) and surveys. We conduct 1.5-2 hour long IDIs with 50 experts in health, policy, and AI across 17 countries, and through an inductive approach we conduct a qualitative thematic analysis on expert IDI responses. We administer a blinded 30-minute survey with case studies to 672 general population participants across 5 countries in Africa and analyze responses on quantitative scales, statistically comparing responses by country, age, gender, and level of familiarity with AI. We thematically summarize open-ended responses from surveys. Our results find generally positive attitudes, high levels of trust, accompanied by moderate levels of concern among general population participants for AI usage for health in Africa. This contrasts with expert responses, where major themes revolved around trust/mistrust, ethical concerns, and systemic barriers to integration, among others. This work presents the first-of-its-kind qualitative research study of the potential of AI for health in Africa from an algorithmic fairness angle, with perspectives from both experts and the general population. We hope that this work guides policymakers and drives home the need for further research and the inclusion of general population perspectives in decision-making around AI usage.

Pulse oximetry is a noninvasive test that measures the oxygen saturation level in a patient's blood, and it has become an important tool for monitoring many patients, including those with Covid-19. But new research links faulty readings from pulse oximeters with racial disparities in health outcomes, potentially leading to higher rates of death and complications such as organ dysfunction, in patients with darker skin. It is well known that non-white intensive care unit (ICU) patients receive less-accurate readings of their oxygen levels using pulse oximeters -- the common devices clamped on patients' fingers. Now, a paper co-authored by MIT scientists reveals that inaccurate pulse oximeter readings can lead to critically ill patients of color receiving less supplemental oxygen during ICU stays. The paper, "Assessment of Racial and Ethnic Differences in Oxygen Supplementation Among Patients in the Intensive Care Unit," published in JAMA Internal Medicine, focused on the question of whether there were differences in supplemental oxygen administration among patients of different races and ethnicities that were associated with pulse oximeter performance discrepancies.

For years, racism mandated that Black people and other people of color in the United States use back doors to enter restaurants, movie theaters, and other public places. While these practices have ended, digital back doors may once again make them and others second-class citizens when it comes to health. Digital back doors are technological processes and tools used in health care, such as racially biased algorithms, infrastructural limitations, and dirty data. These unwittingly exacerbate existing health inequities, which the World Health Organization defines as "systematic differences in the health status of different population groups." How are digital back doors created? Their root cause is human made, due to the development and application of technology by some health information technology (health IT) developers and clinicians who fail to fully or explicitly consider equity in health care.

When I graduated from medical school and took the Hippocratic Oath, I vowed to not just treat the illness on a patient's medical history form but to treat the person behind the diagnosis. To do this well, clinicians need to understand the whole person and the context in which they live -- their race, gender identity, native language, socioeconomic status, or zip code, among other things -- to ensure equitable care. According to the CDC, health equity is reached when every person has the opportunity to attain his or her full health potential regardless of social position or other socially determined circumstances. Yet, health inequities abound in our healthcare systems. Research says that those Americans who live in rural communities have less access to care and subsequently worse health outcomes than those who live in non-rural communities.

Artificial intelligence (AI) is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP) and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly. AI is able to analyze large amounts of data stored by healthcare organizations in the form of images, clinical research trials and medical claims, and can identify patterns and insights often undetectable by manual human skill sets. AI algorithms are "taught" to identify and label data patterns, while NLP allows these algorithms to isolate relevant data. With DL, the data is analyzed and interpreted with the help of extended knowledge by computers.

Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.

The Palmerston North-based Health Hub Project in New Zealand is aiming to reduce health inequities and increase access to care with the help of artificial intelligence, machine learning and blockchain. Project co-founder David Hill is a GP at the Health Hub Project in Palmerston North, which runs four general practices with around 9000 patients. Hill says clinically trained people are a diminishing resource in healthcare and the system cannot rely on that to ensure its sustainability in the future, therefore technology needs to be used to "balance that inequity of supply and demand". "The whole point of what we are doing is trying to make sure that we use IT in a way that allows or permits greater equity of access to patients and starts to reduce the reliance on the ever-dwindling resource of healthcare workers," he says. "Also, to advance the value proposition that we give to patients."