ABSTRACT
This paper examines the moral and legal implications of artificial intelligence (AI) in healthcare. AI-driven medical advancements have enormous potential to enhance patient care, including robotic surgery, personalised medicine, and diagnostic tools. But these developments also carry with them challenging moral and legal circumstances. The article offers a comprehensive analysis of the challenges presented by artificial intelligence (AI) in healthcare, as well as the regulatory frameworks that are in existence. This study makes an extensive evaluation of the literature and an in-depth examination of case studies in an effort to demonstrate the critical equilibrium between innovation and patient rights. Artificial intelligence (ai) systems have been welcomed by the medical and healthcare industries in the hopes of bettering care quality, increasing efficiency, lowering costs, etc. Machine learning and generative artificial intelligence are two types of ai-systems. Artificial intelligence (AI) has the power to fundamentally change both medical practice and the way that healthcare is delivered. AI is a powerful and advanced area of computer science. This article describes current developments in the field, explores possible future directions for AI-augmented healthcare systems, and provides a roadmap for creating effective, trustworthy, and secure AI systems.
KEYWORDS: Artificial Intelligence (AI), Healthcare, Complex Algorithms, Medical Research
INTRODUCTION
Put simply, artificial intelligence (AI) is the science and engineering of creating intelligent computers by programming them to follow a set of rules or algorithms that simulate human cognitive processes like comprehension and solving issues. Globally, life expectancy has increased in line with the rapid advancement of medical research. Nevertheless, as people live longer, healthcare systems must deal with a rising demand for complex services, rising expenses, and a staff that finds it difficult to meet patients’ diverse needs. A few of the numerous unstoppable factors driving demand are the ageing population, changing patient expectations, changing lifestyle choices, and the never-ending cycle of innovation. Artificial intelligence (AI) is rapidly advancing in the healthcare sector because of its ability to use massive datasets and get insightful knowledge that can support based on proof medical decisions and accomplish value-based treatment. Health leaders need to understand the state of AI technology today and how to use it to advance the technological evolution of the healthcare sector while improving the efficacy, protection, and availability of medical treatments. Healthcare and medicine are experiencing the effects of the quick adoption of new AI technologies, particularly generative AI and machine learning, as is the case with numerous aspects of society. Artificial intelligence (ai) systems are being developed, established, and utilised more and more in healthcare and medical settings and practices, apparently to improve efficiency in various settings and practices, reduce healthcare costs, increase access, and improve healthcare overall. AI systems are being introduced, and their use is growing. This is causing a number of complicated ethical conflicts.
WHAT IS ARTIFICIAL INTELLIGENCE IN HEALTHCARE?
In the context of healthcare, artificial intelligence refers to the application of complex algorithms intended to carry out specific activities automatically. Computers can be used by scientists, physicians, and researchers to input data into algorithms that can analyse, evaluate, and even recommend remedies for challenging medical issues.
The two types of AI used in medicine are virtual and physical. Examples of the virtual component include applications such as neural network-based treatment decision support systems and electronic health record systems. Evidence-based medicine relies heavily on the development of interactions and patterns from the existing database in order to establish clinical correlations and insights. We employed statistical tools to identify these patterns and relationships. Two common techniques that computers employ to master the art of medical diagnosis are flowcharts and the database approach.
A doctor uses the flowchart-based strategy, which integrates patient symptoms, to ask a sequence of questions in order to arrive at a likely diagnosis. This is an interpretation of the process of taking a history. This requires downloading a significant amount of data onto cloud-based machine learning networks, considering the range of disease processes and symptoms encountered in general care. Due to the robots’ inability to recognise and gather information that medical professionals can only observe during a patient visit, the method’s effectiveness is restricted. On the other hand, the database approach utilises the machine learning or pattern recognition idea, which comprises utilising iterative algorithms to train a computer to recognise particular clinical/radiological images or symptom patterns.
RESEARCH METHODOLOGY
In order to have a thorough grasp of the implications of artificial intelligence in healthcare, this paper’s research technique is descriptive in nature and is dependent on secondary sources. The media, books, and the internet are examples of further sources of information that are employed in the research.
REVIEW OF LITERATURE
The literature review focuses on three main areas: legal frameworks, ethical considerations, and challenges associated with AI in healthcare.
Legal framework
In the U.S., the 21st Century Cures Act of 2016 defined the medical device as a tool “intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease, in man or other animals, or intended to affect the structure or any function of the body of man or other animals”.
There are several ways in which the use of artificial intelligence in the medical field will impact the legal system. First, there are worries about how the existing legal framework will address acknowledged health-related AI challenges, such as the following:
- Whether regulations governing medical devices, medical malpractice, product liability, professional self-regulation, and certifications will sufficiently address the potential for AI error;
- Whether the current guidelines for assigning blame for medical errors are suitable in cases where an AI tool suggests or even administers a damaging course of therapy, and how responsibility should be divided between medical experts and AI producers and developers;
- Whether algorithmic bias, in which AI technologies unjustly provide differing results for historically disadvantaged groups, may be addressed by current anti-discrimination and human rights laws;
- If the huge data requirements of AI and the real-time data collection of machine learning (ML) tools are taken into account, do current privacy rules adequately protect patients?
- Whether the data governance laws and regulations now in place are adequate to give AI developers access to representative training data sets and allow them to suitably integrate historically underrepresented populations;
- Whether the current informed consent regulations are strong enough to safeguard people when medical professionals decide to utilise AI for diagnosis and treatment.
How does the FDA govern AI products, and under what conditions?
The FDA is in charge of guaranteeing the performance and safety of numerous AI-powered medical devices. Software is primarily regulated by the agency according to its intended purpose and the degree of patient danger if it is erroneous. The FDA classifies software as a medical device if it is meant to treat, diagnose, cure, lessen, or prevent disease or other problems. Software as a medical device (SaMD) encompasses the bulk of products that are categorised as medical devices and that incorporate AI or ML. SaMD examples include computer-aided detection (CAD) software, which processes pictures to assist in the detection of breast cancer, and software that analyses MRI images to assist in the diagnosis and detection of strokes. Some products that are intended for consumers, such as particular apps for smartphones, may fall under the SaMD category. In contrast, a computer programme that is part of a medical device’s hardware, such the one that operates an X-ray panel, is referred to by the FDA as “Software in a Medical Device.” Artificial intelligence (AI) techniques can also be integrated into these items.
Some instances of AI-enabled goods that the FDA has cleared or approved
“IDx-DR: Detects diabetic retinopathy”
With the use of this software, eye images are analysed to determine if a patient should be referred to an eye specialist for more severe diabetic retinopathy or whether they should be rescreened a year later if the images show no signs of more severe diabetic retinopathy.
“ContaCT: Detects a possible stroke and notifies a specialist”
When a suspected big vascular blockage is found, this programme scans CT pictures of the brain for signs often associated with a stroke and texts an expert right away, perhaps involving them earlier than would be the case with routine care.
“Embrace2: Wearable seizure monitoring device”
This product detects physiological signals with a device worn on the wrist. If the system notices behaviour that could be a sign of a seizure, it will send an instruction to a connected wireless gadget that is programmed to alert the patient’s preferred carer. Furthermore, the apparatus will record and store sensor data for a physician’s review at a later time.
DATA PRIVACY AND SECURITY
Given the sensitive and private nature of medical data, data privacy and security are critical considerations when implementing AI in healthcare. It’s critical to properly address these problems because they may have far-reaching effects. The rise in cybercrimes can seriously jeopardise the private data that patients have given AI, since AI frequently needs access to medical histories and patient records. To preserve patient privacy, it is essential to keep this information private. Before implementing AI in the healthcare industry, there is a great deal of anxiety about safeguarding patient data against illegal access, data breaches, and cyberattacks. Healthcare organisations frequently collaborate with outside AI suppliers. Making ensuring these providers follow data privacy and security requirements is essential when working with them.
Relevant conceptions of civil culpability for harm caused by AI
A number of legal and ethical issues are raised by the deployment of AI-enabled medical robots in healthcare settings, particularly with regard to who is responsible for the harm and fatalities these AI-driven machines cause. Upon examination of existing research and related legal provisions, the subsequent civil liability systems have been determined to have potential applicability with regard to injury resulting from artificial intelligence.
Strict Liability
This idea holds that accountability arises automatically anytime damage happens, without the need to establish the defendant’s fault. Using AI-based technology suggests that the hospital may be held liable, even if the operations of the medical device were approved, planned, or under control. It also suggests that the manufacturer may not have taken all necessary precautions when producing, promoting, or selling the AI-enabled device.
This theory of liability might discourage medical professionals from employing robots and other AI-enabled medical equipment, as well as discourage businesses from developing self-learning systems. The acceptance of this concept appears persuasive when it comes to dangerous items that have the potential to seriously adversely impact users, but that is not the case with medical devices, which are largely employed to improve patient care and lower the number of fatalities and injuries.
Negligence (fault-based) liability
Given that cases of medical negligence usually result from carelessness, this has been suggested as a possible legal basis for tackling the harm that AI-enabled medical and surgical devices do. As AI processes in those devices advance to a stage at which the equipment may become fully autonomous, self-aware, and capable to arrive at choice on its own using the data it gathers, it also appears that accountability due to carelessness could grow into a further less beneficial option for handling harm caused by AI-enabled devices.
Ethical Considerations in AI driven healthcare
Patients’ lives and wellbeing are entrusted to healthcare professionals. AI use in the healthcare industry must take into account a number of ethical issues. Many patients don’t trust artificial intelligence (AI) to make important healthcare choices, and some may have a strong preference for human healthcare personnel. When using AI in the healthcare industry, a patient’s consent is required. An extensive explanation of the procedure and use of AI should be given to the patient. To guarantee that patients and healthcare professionals are aware of the reasoning behind AI-generated suggestions, transparency in AI algorithms and their decision-making processes is crucial. If AI systems are not adequately trained and evaluated, they may unintentionally reinforce prejudices in healthcare. Healthcare disparity may result from this. Therefore, it is equally necessary to explain to the patients the effects of AI. When developing AI for healthcare, ethical issues ought to come first. The patients should make the decision on whether or not to utilise AI, and if they object, it cannot be used.
Liability and accountability
The possibility of patient injury resulting from the choices made by a medical instrument driven by artificial intelligence is something that present global safety and accountability systems have yet to adapt to. AI-based solutions themselves, like any other diagnostic tool, are not subject to accountability for the choices and judgements it makes. As a result, it is critical that accountability and duty be assigned at every level of the development and application of AI for health. It is a difficult ethical problem to assign blame for mistakes produced by AI in healthcare. In the event that mistakes relating to AI arise, patients ought to have channels for appeal. Liability concerns need to be clarified by legal frameworks, especially when AI systems are used in clinical decision-making and treatment. When their systems are utilised in clinical judgements or patient care, the creators and suppliers of AI systems employed in healthcare may be held jointly liable. It is required upon them to guarantee that their artificial intelligence solutions meet safety, efficacy, and regulatory requirements. Therefore, it becomes essential to determine who would share responsibility and culpability for any errors made, as well as to what degree, when implementing AI in the healthcare industry.
Knowledge-Based Consent and Self-Governance
Informed consent is a communication process between an individual and the medical professional that includes documenting the patient’s capacity and ability to make decisions along with ethical disclosure. Patients have a right to know about their assessments, well-being, course of action, therapeutic results, test results, costs, insurance status, and other medical information, according to the definition of ethical obligation. Any permission the patient gives should be precise, explicit, and tailored to the intended usage. Concerns about this problem have increased along with the use of AI in healthcare applications. Based on the autonomy principle:
- Patients should be able to know about the treatment process, the risks of screening and imaging, data capture anomalies, programming errors, data privacy, and access control, as well as how to protect a significant amount of genetic information obtained through genetic testing.
- They should also be able to refuse treatment that the health care provider deems appropriate.
- All individuals have the right to information and questions prior to procedures and treatments.
- Patients have a right to know who bears responsibility for malfunctions or errors in these robotic medical devices. The response is critical to patient rights and the health care industry’s workforce.
CONCLUSION AND SUGGESTIONS
The most valuable resource in healthcare today is data, but it’s still unclear if the provision of healthcare will undergo an AI “revolution” or “evolution.” Although early AI applications in some healthcare fields have shown promise, more sophisticated AI tools will need compatible, secure, and high-quality real-world data. This future that can improve safety, affordability, equity, and health outcomes will be made possible by the actions taken by lawmakers and leaders of health care organisations in the upcoming years, starting with short-term opportunities to develop meaningful AI applications that achieve measurable improvements in outcomes and costs.
Looking ahead, artificial intelligence (AI) has enormous promise for better drug development, personalised medicine, and solving global health issues. Healthcare delivery may become more patient-centered, data-driven, and efficient by utilising AI technologies. However, achieving this potential would need a coordinated effort from a number of parties, including patients, legislators, healthcare professionals, and technology developers.
Advances in AI have the potential to completely transform many facets of healthcare and open the door to a future where healthcare is more personalised, precise, predictive, and portable. The influence of these advancements and the opportunity they present for a digital rejuvenation forces health systems to consider the best ways to adjust to the ever-changing environment. It’s unclear if these technological developments will be adopted quickly or gradually. The NHS claims that using these technologies could free up more time for medical staff members to spend with patients.
They will be able to focus on what their patients value most as a result. The “the finest levels of individual understanding” and other internationally democratised data assets will be used in the future to “function at the edges of science” and deliver a shared, exceptional level of care regardless of who delivers it or where. AI may prove to be a crucial instrument for improving health equity on a worldwide scale.
When aiming to use AI for health, leaders in the healthcare industry should at the very least take these factors into account:
- Legal and responsible access procedures: Medical records are highly confidential, inconsistent, divided, and unsuitable for the development, evaluation, use, and adoption of machine learning.
- being able to create and make sense of some of the rules that need to be applied to the datasets in order to obtain the necessary insight by drawing on past knowledge or domain experience.
- having quick decision-making access to sufficient processing power, which is quickly evolving with the arrival of cloud-based computing.
- Implementation research: We need to carefully examine, look into, and investigate the issues that arise when designing “reliable” algorithms for artificial intelligence that are integrated into appropriate procedures.
DHVISHA SHAH
SHREE KES JAYANTILAL H PATEL LAW COLLEGE, MUMBAI