The complexity and growth of data in healthcare means the increasing use of artificial intelligence (AI) in the field. Several types of AI are already being used by payers and care providers, and life science companies. The main types of applications include medical recommendations, patient interactions and care, and administrative tasks. Although there are many areas where AI can outperform humans in healthcare tasks, the difficulty in implementing it will prevent it from working at a large scale for quite some time. There are also ethical issues to consider and discuss. This article discusses ethical issues when considering the potential of artificial intelligence in healthcare.
Artificial intelligence (AI), which has become increasingly prevalent in fields such as business and society, is now being used for healthcare as well. AI technologies have the potential to transform patient care and the administrative processes that govern healthcare within provider, payer, and pharmaceutical organizations. Many studies show that AI is outperforming humans in critical healthcare tasks, for example diagnosing diseases, research, detecting tumors, etc. Despite this, there is a perception that AI will not replace humans in healthcare anytime soon. This article debates about AI in healthcare and the obstacles to its implementation.
Artificial Intelligence (AI) is not a single technology but a collection of them, most of which are critical to healthcare. However the specific tasks they perform vary greatly. Some of the specialized AI technologies of high importance to healthcare are defined and described below.
Machine learning is an application of AI that can perform tasks without instructions by using algorithms and statistical models to analyze and draw conclusions from patterns in data. It is one of the most common forms of AI; employed by most companies in the US according to a survey conducted among 1100 American managers from various companies. It is a pervasive technique at the core of many approaches to AI and has many variants. Machine learning has different forms depending on its complexity.
The most common and least complex is precision medicine. It involves predicting which treatments are likely to be successful based on various characteristics of the patient and the treatment context. Most precision medicine applications require a training dataset (the initial data used to train the machine learning model) where the outcome variable (e.g. disease onset) is known; this is called supervised learning.
A more complex form of machine learning is neural networks – a technique that has been well established in healthcare research for several decades. Neural networks have been a notable machine learning AI technique for healthcare institutions. It takes into account inputs, outputs, and variables to predict whether a patient may develop a particular disease in the future. It acts similar to the neural process of the human brain when it receives information. It is used for classification applications i.e. whether a patient will have particular diseases. According to the inputs, the networks will generate the best possible results.
The most complex forms of machine learning include deep learning. It is neural networks but has many layers that predict outcomes. It has been used in oncology and radiology for accurate diagnosis. Such models can have many hidden features that can be rapidly exposed due to today’s technology. Deep learning is often used in radiology to recognize cancerous tissues.4 It can recognize potentially cancerous lesions in radiological images and radiomics to detect clinically relevant data invisible to the naked eye. Deep learning has also been used for speech recognition.
However, this type of learning is complex and beyond the interpretation of common human observers. Artificial intelligence (AI), which has become increasingly prevalent in fields such as business and society, is now being used for healthcare as well. AI technologies have the potential to transform patient care and the administrative processes that govern the healthcare sector. Many studies show that AI is outperforming humans in critical healthcare tasks, for example diagnosing diseases, research, detecting tumors, etc. Despite this, there is a belief that AI will not replace humans in healthcare anytime soon. The article discusses the potential of using AI and the obstacles to implementation.
Understanding human language has long been a major goal of AI researchers. This technology attempts to recognize human language for writing descriptions and includes speech recognition, textual analysis, and translation. It is helpful in the healthcare sector for documentation, publication of research, analysis of unstructured clinical notes on patient diagnosis and care, preparation of notes, and transcription of patient conversations. NLP uses the following approaches – statistical and semantic. Statistical NLP incorporates deep learning neural networks to achieve better accuracy in speech recognition.
These systems combine a series of rules in a pattern of “if then” coding to provide information about the medical domain. It is used to aid clinical decisions and is used by many electronic health record (EHR) providers today. Although this system is effective and easy to understand, it can break down due to conflicts between a large number of rules. These rules are not in sync with the evolving knowledge in the medical field and are time consuming to edit. Thus, more effective machine learning algorithms are taking its place.
Physical robots are well known by this point, given that more than 200,000 industrial robots are installed worldwide each year. They perform pre-defined tasks such as lifting, repositioning, but have become increasingly more collaborative and easier to train by humans. With other Artificial Intelligence (AI) capabilities being incorporated into their ‘brains’ i.e. operating systems, they are becoming more intelligent. Due to increasing Artificial Intelligence (AI) capabilities and human collaboration, surgical robots are being popularized to help surgeons perform operations by enhancing their ability to see and make precise and minimal incisions, sutures, etc. Although not involved in major medical decisions, these surgical robots are used for gynecological surgeries, prostate surgeries, and head and neck surgeries.
This computer program performs administrative tasks such as authorization, updating records, and billing. They are cheaper, easier to program, and transparent in their actions compared to other forms of AI. Robotic Process Automation (RPA) involves computer programs on servers. This technology combines workflow, business rules, and presentation layer with an information system to follow a set of rules to accomplish digital tasks. It can be combined with various technologies like image recognition to extract data from faxed images and transcribe them digitally. This combination will ensure a more holistic solution in the future.
Diagnosis AI has played an indispensable role in the diagnosis and treatment of diseases since the 1970s, but has seen a serious lack of implementation in healthcare organizations. The development of MYCIN by Stanford for diagnosing blood-borne bacterial infections was significant, however, these rule-based systems were not fully efficient and failed to integrate with clinical practice. IBM’s Watson presented a more accurate system for precision medicine, especially for cancer treatment. This technology combined machine learning and NLP and included a set of cognitive services like application programming interfaces, speech vision, and machine learning based data analysis.
However, Watson could not be a major facilitator in the healthcare market as it proved difficult to teach it new cancer diagnoses,9 and failed to compete with free open source programs. Watson is a set of cognitive functions and thus using it for cancer treatment was a very ambitious project. The implementation of Artificial Intelligence (AI) in healthcare has surprised many, although such technologies are also used in the NHS. The existence of these rule-based systems in organizations has been problematic as they are unable to handle the constantly evolving medical knowledge, especially genomic, proteomic, metabolic healthcare approaches. With advances in technologies for radiology analysis, retinal scanning, or genomic-based precision medicine, the technology of Artificial Intelligence (AI) has seen improvements over the years, claiming more accuracy than physicians as they use statistics based on evidence and probability. But there are many ethical concerns for the patient-doctor relationship.
Companies like Google have collaborated with health delivery networks to create predictive models that warn about high-risk conditions like heart failure. Jvion has created a clinical breakthrough machine that accurately identifies high-risk patients and relevant treatments. Some firms like Foundation Medicine have taken diagnosis and treatment-focused approaches to cancer based on genetic profiles to identify different types of cancer and response to drug treatments. Population health machine learning models are increasingly being used by providers and payers to tailor care, as well as to predict populations at risk for specific diseases, accidents, or to make decisions on hospital readmissions.
While these models are effective at making predictions, they suffer from a variety of challenges. Most emerging technologies remain confined to research laboratories, particularly due to challenges in medical ethics and the patient-physician relationship, leading to a lack of integration within the clinical workflow. Many Artificial Intelligence (AI) technologies address only one issue, limiting involvement in the care process. Population health-based models also do not consider factors such as the socio-economic status of patients, thereby not giving a completely accurate picture.
Regardless of the nature, the integration of Artificial Intelligence (AI) into clinical workflows and EHR systems can be challenging. And it is this integration that is a major hurdle in the implementation of AI in healthcare. Some EHR vendors have started limited use of Artificial Intelligence (AI), but in its early stages. Therefore, adequate integration of Artificial Intelligence (AI) in the healthcare system is necessary to develop an effective mechanism.
Patient engagement and compliance has been the bridge between ineffectiveness and good health outcomes. The more patients actively participate in taking care of their well-being, the better the outcomes in terms of utilization, financial outcomes, and member experience. With Artificial Intelligence (AI), these elements are being accomplished. Providers and hospitals often use their clinical expertise to develop treatment plans for the health of chronic or acute patients. However, medical advice will not achieve the desired results if patients do not follow the treatment plan and do not take measures on their own like eating right, losing weight, etc. or are non-compliant i.e. not taking the prescribed medicines. This can have fatal effects. Through a survey, it was found that most patients do not follow the treatment plan prescribed for them by their physicians. To generate better outcomes, patients need to be involved. There is a growing emphasis on using machine learning and business rule engines to intervene and provide better patient care. Messaging alerts and relevant, targeted content that can nudge patients at critical moments to act in a way that benefits their health is a promising area in research.
Another growing focus in healthcare is on how options are presented to patients so that they can choose and behave in a more predictable way (choice architecture). This is done based on real-world evidence. Through information provided by provider EHR systems, such as watches, smartphones, etc., the software can cater to patients with appropriate recommendations to deal with their diseases by comparing the patient data to other effective treatments given to other people with similar cases. The suggestions can be issued to various stakeholders in healthcare such as patients or nurses, etc.
Healthcare also has many administrative applications, which are not as revolutionary as patient care, but are still very efficient in healthcare. The average American nurse spends a quarter of her work time on regulatory and administrative activities. Artificial Intelligence (AI) technology can be used for various administrative applications in healthcare including claim processing, documentation, medical record management. Some healthcare organizations have also experimented with chatbots for patient interaction, health, and telehealth. These chat boxes can be useful for simple transactions like filling prescriptions, but they require sharing personal data which can cause patient concerns regarding disclosing such data. Patients have expressed privacy concerns about exposing such confidential information and how it will be used.
Machine learning is very useful for administrative tasks like claims and payments. Insurers have to verify whether most claims are correct or not. Reliably identifying, analyzing, and correcting coding issues and erroneous claims saves time for all stakeholders in healthcare. False claims that slip through the cracks lead to significant financial damage, waiting to be revealed through data-matching and claims audits.
There has been much discussion of the concern that AI replacing workers by automation will lead to job loss and workforce displacement. It was suggested that a significant proportion of health care jobs could be automated within 10–20 years in the UK. Other studies have suggested that although some automation of jobs is possible, a number of external factors other than technology may limit job losses. These factors include the significant cost of Artificial Intelligence (AI) technologies, the labour market, the benefits of automation beyond simple labour replacement, regulations and acceptance in society.
These factors may limit actual job loss to a much lower extent than anticipated. So far there have been no known jobs eliminated by AI in health care. The difficulty of integrating AI into the health care system has been partly responsible for the lack of impact on jobs. If automation is to happen, it will be for jobs involving digital information, radiology, as opposed to jobs involving direct patient contact. But even in jobs like radiology, Artificial Intelligence (AI) penetration in these areas is likely to be slow.
While deep learning is finding its way within radiology, there are several reasons why radiology jobs are at no immediate risk of automation. First, there is the limitation of Artificial Intelligence (AI) to perform radiology tasks. There are thousands of recognition tasks required for radiology beyond just image recognition and deep learning can only perform some specific tasks such as recognizing only certain types of images. Radiologists also consult other physicians on diagnoses, and treat diseases based on images, which need to be generated by tailoring them to the particular patient’s medical condition, linking findings from images to other medical records and test results, discussing procedures and outcomes with patients, and many other activities.
Second, AI-based images are far from being ready for daily use in healthcare. Different imaging technology learning algorithms have different focus points which make it very difficult to embed deep learning systems into current clinical practice. Thirdly, deep learning algorithms for image recognition require labeling of data as there are millions of images of patients who have received a definite diagnosis of cancer, broken bone or other pathology and they need to be organized within labels. However, there is no such storage of radiology images and data, from which deep learning can be performed. Finally, substantial changes in medical regulation and health insurance will be required to start using Artificial Intelligence (AI) in image analysis.
For these reasons, automation of healthcare is unlikely in the near future. There is also a possibility that new jobs will be created to work where Artificial Intelligence (AI) technologies can be used. But the employment of the same or more people in healthcare also means that medical costs will never decrease substantially until it is possible to properly integrate Artificial Intelligence (AI) in the healthcare sector.
Finally, ethical issues associated with AI need to be addressed. The most obvious are privacy, transparency, accountability, and the use of confidential data being shared through Artificial Intelligence (AI). Transparency is perhaps the most difficult. Many Artificial Intelligence (AI) algorithms, especially those used for image analysis, are nearly impossible to explain. Thus, while a patient may know that he or she has cancer, they may not know exactly why or what is wrong as such algorithms can confuse even physicians who are familiar with their functioning. Due to the explanations being difficult to explain, it may be difficult for them to establish accountability if mistakes are made by Artificial Intelligence (AI) systems in diagnosing and treating a patient. There are also likely to be incidents in which patients receive medical information from Artificial Intelligence (AI) systems that they would prefer to receive from a doctor who, being a human, would be more sensitive to their situation, thereby jeopardizing the interaction with the patient. Machine learning systems in healthcare can also be subject to algorithmic bias, and this can result in medical predictions based on gender or race when they are not actually the causal factors.
With Artificial Intelligence (AI) in healthcare we are likely to see many ethical, medical, professional and technological changes. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures and an appropriate policy to monitor key issues that respond in a responsible manner and establish governance mechanisms to limit negative impacts.
We believe Artificial Intelligence (AI), through machine learning, will have a key role in healthcare in the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, which is widely considered a much-needed advancement in care. Although there have been hurdles in early Artificial Intelligence (AI) integration in providing diagnosis and treatment recommendations, there is hope that Artificial Intelligence (AI) will eventually master that area as well. Given the rapid penetration Artificial Intelligence (AI) is making in image analysis, it is expected that Artificial Intelligence (AI) will take over radiology and pathology images for some time. Speech and text recognition are already employed for tasks such as patient communication and capturing clinical notes, and their use will grow.
The biggest hurdle for Artificial Intelligence (AI) in these areas is not whether the technologies will be able to be useful, but rather ensuring proper implementation in daily clinical practice. For widespread adoption to occur, Artificial Intelligence (AI) systems must be properly standardized for use and clinicians must be properly trained in handling these technologies. It will take a long time to overcome these hurdles and thus Artificial Intelligence (AI) in healthcare is an idea that is still far from implementation. It has also become clear that Artificial Intelligence (AI) systems will not largely replace medical personnel but will enhance their efforts to care for patients. Over time, these personnel may move to jobs that require human skills as well as human qualities of empathy and persuasion. Perhaps the only healthcare providers who will lose their jobs over time may be those who refuse to work with artificial intelligence.
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