Artificial Intelligence in Healthcare: Chapter 2 - The rise of artificial intelligence in healthcare applications

Elsevier, Artificial Intelligence in Healthcare, 2020, Pages 25-60
Adam Bohr and Kaveh Memarzadeh

This chapter supports SDG 3 by looking at how artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment.

The Rise of Artificial Intelligence in Healthcare Applications: A Cornerstone for Sustainable Development Goals

In the contemporary era of technology, the landscape of healthcare has been dramatically transformed, with artificial intelligence (AI) being at the forefront of this revolution. AI's rise in healthcare applications presents promising prospects for achieving the United Nations' Sustainable Development Goals (SDGs), particularly those centered on health and wellbeing. This article delves into the potential of AI in healthcare, its applications, challenges, and the implications for sustainable development.

The Emergence of AI in Healthcare

The concept of artificial intelligence, born out of the scientific curiosity of the mid-20th century, has evolved into a transformative force in various sectors, most notably in healthcare. The healthcare industry's shift towards AI has been driven by the need to enhance service delivery, reduce healthcare costs, and improve the quality of patient care.

AI in healthcare is more than a mere technological advancement; it's a paradigm shift. It leverages machine learning algorithms and vast data sets to gain insights that aid in diagnosis, prognosis, and personalised care, thereby reshaping the entire patient care continuum.

AI and Sustainable Development Goals (SDGs)

The potential of AI in healthcare extends beyond individual patient care to the realm of global health and sustainable development. The United Nations' Sustainable Development Goals, particularly Goal 3: Good Health and Wellbeing, directly resonate with the capabilities of AI.

AI's rise in healthcare could significantly contribute to achieving universal health coverage, a core aspect of SDG 3, by improving access to health services and reducing health disparities. Moreover, it could enhance the quality of healthcare, another vital SDG 3 target, by enabling accurate and timely diagnosis, efficient disease management, and personalised care.

AI in Healthcare: Applications and Opportunities

The rise of AI in healthcare applications is visible in numerous areas, including predictive analytics, precision medicine, digital pathology, robotic surgery, and health monitoring.

Predictive Analytics: AI can analyse vast amounts of data to predict disease trends, patient outcomes, and healthcare needs. This can enable proactive healthcare interventions and reduce healthcare costs.

Precision Medicine: AI can personalise patient care by predicting individual responses to treatments based on their genetic makeup, lifestyle, and health history. This can improve treatment efficacy and patient satisfaction.

Digital Pathology: AI can assist pathologists in diagnosing diseases by analysing digital images of tissue samples. This can enhance diagnostic accuracy and speed, particularly for complex diseases such as cancer.

Robotic Surgery: AI-enabled robots can perform complex surgeries with high precision, reducing surgical errors and improving patient outcomes.

Health Monitoring: AI can monitor patient health in real-time using wearable devices and mobile applications. This can empower patients to manage their health and enable early detection of health issues.

Challenges in AI Integration in Healthcare

Despite the numerous advantages of AI in healthcare, the rise of AI also presents several challenges. These include data privacy concerns, the risk of algorithmic bias, the need for regulatory frameworks, and issues related to healthcare workforce adaptation.

Data Privacy: AI in healthcare relies on vast amounts of personal health data, raising concerns about data privacy and security. It's crucial to establish robust data protection measures to ensure patient confidentiality and trust.

Algorithmic Bias: AI algorithms can reflect and perpetuate existing biases if they're trained on biased data. This can result in unfair health outcomes for certain population groups. Therefore, fairness in AI is a critical concern that needs to be addressed through rigorous algorithmic auditing and bias mitigation techniques.

Regulatory Frameworks: AI's integration in healthcare requires suitable regulatory frameworks to ensure its safe and ethical use. This involves setting standards for AI quality, transparency, and accountability, and establishing mechanisms for monitoring and enforcement.

Healthcare Workforce Adaptation: The rise of AI could disrupt the healthcare workforce by automating certain tasks. This calls for strategies to reskill and upskill healthcare professionals, enabling them to work effectively alongside AI.

Implications for Sustainable Development

The rise of artificial intelligence in healthcare applications has profound implications for sustainable development. AI could help to advance several SDGs, particularly those related to health, inequality, and innovation.

Health (SDG 3): AI could enhance global health by improving access to healthcare, reducing health disparities, and enabling proactive and personalised care. It could also help to combat global health threats, such as pandemics and antimicrobial resistance, by enabling rapid disease detection and response.

Inequality (SDG 10): AI could reduce health inequalities by enabling universal access to quality healthcare services, regardless of geographical location or socioeconomic status. However, it's crucial to ensure that the benefits of AI are equitably distributed, and that AI does not exacerbate existing health inequalities.

Innovation (SDG 9): AI is a key driver of innovation in healthcare. It can spur the development of new health technologies, improve healthcare efficiency, and stimulate economic growth.

The Future of AI in Healthcare

The future of AI in healthcare is promising, but it's also contingent on addressing the challenges associated with AI integration. It requires a multi-stakeholder approach, involving healthcare professionals, policymakers, technologists, and patients, to harness the potential of AI in a way that's ethical, equitable, and beneficial for all.

Investments in AI research and development, coupled with strategies for AI education and capacity building, are essential to drive the future growth of AI in healthcare. Policies and regulations need to be put in place to guide the ethical use of AI, protect patient data, and ensure the quality and safety of AI applications.

It's also important to promote public awareness and understanding of AI in healthcare, to ensure its acceptability and adoption. This includes addressing misconceptions about AI, fostering trust in AI, and engaging patients in AI decision-making.

The rise of artificial intelligence in healthcare applications presents significant opportunities for enhancing healthcare and advancing sustainable development. It's a journey fraught with challenges, but with potential rewards that could revolutionise the healthcare landscape and contribute significantly towards achieving the Sustainable Development Goals. As we navigate this journey, it's crucial to keep the focus on the ultimate goal: improving health and wellbeing for all.