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AI in Healthcare

Ethics and Governance of Artificial Intelligence for Health

The use of Artificial Intelligence for Health especially in medicine raises the notion of AI replacing clinicians and human decision-making. Predictive-based diagnostics and AI diagnostics are being seen as diagnostic aids in a number of ways, including in radiology and medical imaging. Currently, AI is being evaluated for use in radiological diagnosis in oncology (thoracic, abdominal and pelvic imaging, colonoscopy, mammography, brain imaging and dose optimization for radiotherapy), in non-radiographic applications (dermatology) , pathology), in the diagnosis of diabetic retinopathy, in ophthalmology, and for RNA and DNA sequencing to guide immunotherapy. In LMICs, AI can be used to improve the detection of tuberculosis in a system that aids in the interpretation of stained images or to scan X-rays for signs of tuberculosis, COVID-19 or 27 other conditions. 

Artificial Intelligence For Health

As AI improves, it could enable medical providers to make faster, more accurate diagnoses. AI can be used to promptly detect conditions such as stroke, pneumonia, breast cancer by imaging coronary heart disease by echocardiography, and detect cervical cancer. Many low-income facilities are facing chronic shortages of medical staff who need assistance with diagnosis and evaluation and to reduce their workload. It has been suggested that AI can fill the gaps when there is a shortage of healthcare services or skilled workers. AI can be used to predict illness or major health events before they happen. For example, an AI technology that could be adapted to assess relative risk of disease could be used to prevent lifestyle diseases such as cardiovascular disease and diabetes. Predictive analytics can prevent other causes of unnecessary morbidity and mortality in LMICs, such as birth asphyxia. The wider use of AI in medicine also presents technological challenges. While many prototypes developed in both the public and private sectors have performed well in field tests, they often cannot be translated, commercialized, or deployed. Another obstacle is the constant changes in computer management and information technology, whereby systems become obsolete and companies disappear. In resource-poor countries, the lack of digital infrastructure and the digital divide will limit the use of such technologies.

Artificial Intelligence For Health

Healthcare workers will have to adapt their clinical practice dramatically as the use of AI increases. AI can automate tasks, giving doctors time to listen to patients, address their fears and anxieties, and ask about unrelated social factors, even though they still may worry about their accountability and accountability. Physicians will have to update their ability to communicate risks, make predictions, and discuss trade-offs with patients, and express their ethical and legal concerns about understanding AI technology. Even if technology produces predictable benefits, those benefits will only materialize if the individuals managing the health system use them to expand the health system’s capabilities in other areas, such as better availability of medications or other indicated clinical interventions or forms of care.

telemedicine
Artificial Intelligence For Health
telemedicine
Artificial Intelligence For Health
telemedicine
telemedicine

The shift from hospital to home care Telemedicine is part of a larger shift from hospital care to home care, with the use of AI technologies to facilitate patient care. conversion. Even before the COVID-19 pandemic, more than 50 healthcare systems in the United States used telemedicine services. COVID-19, which has discouraged people in many places from visiting healthcare facilities, has accelerated and expanded the use of telemedicine in 2020, and this trend is expected to continue. In China, the number of telemedicine providers has nearly quadrupled during the pandemic. The shift to home care has also been facilitated in part by increased use of (algorithmic) search engines to find medical information as well as an increase in the number of text chatbots or voice for healthcare, performance of which has improved with improvements in natural language processing, a form of AI that allows machines to understand human language. The use of chatbots has also accelerated during the COVID-19 pandemic. Furthermore, AI technologies can play a more active role in managing patient health outside of the clinical setting, such as in “just-in-time adaptive interventions”. These operations rely on sensors to provide patients with specific interventions according to previously and currently collected data; they also notify the healthcare provider of any emerging concerns.

Artificial Intelligence For Health

The development and use of sensors and wearable devices could improve the effectiveness of “just-in-time adaptation interventions” but also raise concerns about the amount of data these technologies are collecting, how they are used and the burden of such technologies may vary for the patient. Using AI to extend “clinical” care beyond formal healthcare AI applications in healthcare are no longer used exclusively in healthcare (or home care) systems as health AI technologies can easily be acquired and used by non-healthcare systems.

This means that people can now get health care services outside of the healthcare system. For example, AI applications for mental health are often delivered through education systems, workplaces, and social media, and can even be linked to financial services. While there may be support for the expanded use of such medical applications to offset both increased demand and a limited number of providers, they raise new questions and concerns.

Artificial Intelligence For Health

These three trends may require near-constant monitoring (and self-monitoring) of people, even when they are not sick (or “patient”). AI-guided technologies require the use of mobile health apps and wearables, and their use has increased in the direction of self-management. Wearable technology includes those that are placed in the body (prosthetics, smart implants), on the body (insulin pump patches, EEG recorders), or near the body (monitors), activity trackers, smartwatches and smart glasses). By 2025, 1.5 billion wearable devices could be purchased annually. Wearables will create more opportunities to monitor a person’s health and collect more data to predict health risks, often with greater efficiency and in a quicker way. While monitoring such “healthy” individuals can generate data to predict or detect health risks or improve a person’s treatment as needed, it raises concerns, as it allows near-constant monitoring and collects too much data that would otherwise be unknown or uncollected. Such data collection also contributes to the growing practice of “biological surveillance”, a form of monitoring health data and other biometrics, such as facial features, fingerprint, temperature and circuit. The growth of biosurveillance raises significant ethical and legal concerns, including the use of such data for medical and non-medical purposes that may not receive consent. explicit intent or the government or company’s re-use of such data for non-health purposes, such as in the criminal justice or immigration systems. Such data are therefore subject to the same levels of data protection and security as those collected on an individual in a formal healthcare setting. Using AI for resource allocation and prioritization AI is being used to aid decision-making about prioritization or allocation of scarce resources. Prognostic scoring systems have long been available in critical care units.

Artificial Intelligence For Health

The implementation of AI in healthcare exacerbates existing ethical concerns in medicine and raises new problems. Related ethical challenges include transparency, accountability, bias, privacy, safety, autonomy, and justice. We consider these seven ethical challenges to be the most pressing of the concerns generated by the use of AI in healthcare. Some of these challenges have also been raised as the use of AI is increasing in other application areas, especially in the areas of automation and robotics.

Artificial Intelligence For Health

From an ethical perspective, several overarching themes emerged. First, the issue of consent runs through this entire work. This is not surprising given the crucial importance of the consent concept in biomedical ethics and its interaction with the central principle of individual autonomy. The challenges we raise relate to the human relationship in healthcare, the use of patient data, the consequences of a lack of algorithmic transparency, accountability for errors, and Every definition of trust involves consent in some way. A high-level question that could be asked is “how do we meaningfully agree to the use of AI for service delivery, where there may be an element of autonomy in AI decisions or where we don’t fully understand these decisions?” Another major theme that the challenges we identified can be touched upon is the issue of equity. This is particularly relevant to the issues we discuss around health inequities, what patients and the public want from these technologies, and ensure value to stakeholders throughout the development and implementation of AI algorithms. Are the three general principles of distributive equity (responsibility, ability, and need) a useful guide to help address these issues? These principles are open to interpretation, which means it may be necessary to consider new approaches to equity, especially given the rapidly changing nature of these technologies.

Closely related to the concept of fairness is that of rights. Various international frameworks refer to a minimum standard of health to which all individuals are entitled, including Article 25 of the United Nations’ Universal Declaration of Human Rights, Article 12 of the United Nations’ International Covenant on Economic, Social and Cultural Rights, and the preamble of the World Health Organization’s Constitution. With the addition of AI technologies to care pathways, or potentially, the increased reliance on aspects of care being delivered autonomously by AI, a new discourse around rights may emerge, asking “do people have a right to know how much AI is used in their care?” and “do people have a right not to have AI involved in their care at all?” At its core, this issue mentions whether the ‘right to health’ equates to a ‘right to human delivery of healthcare’. It is essential that research focusing on these ethical, social, and political challenges is multidisciplinary, drawing on the expertise of those who develop AI tools, those who will use and be impacted by these tools, and those who have knowledge and experience of addressing other major ethical, social and political challenges in health. Most importantly, it is vital that the voices of patients and their relatives are heard, and that their needs – clinical, pastoral, spiritual and more – are kept in mind at all stages of such research. It is only by developing tools that address real-world patient and clinician needs and that tackle real-world patient and clinician challenges that the opportunities of artificial intelligence and related technologies can be maximized, while the risks are minimized.