Artificial intelligence for long-term respiratory disease management

The following sample essay on “Artificial intelligence for long-term respiratory disease management”: presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realize new treatment paradigms.

The application of AI is clearly not specific to respiratory disease management; however, it is a chronic disease that requires on-going monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasizes the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.


As medical technology gets smaller, smarter, and more ready to method giant volumes of information, there are opportunities to require devices that are hospital-based and realise them as a little moveable device, facilitating mobile and remote attention strategies. In addition, there’s a growing trend to embed intelligence into such devices facultative them to make selections supported in progress preferences and inputs, like medical specialty measurements. To this finish a software system platform, not a practitioner, may inform or manufacture medical selections concerning treatment and interventions. There has been a large vary of recent studies wherever AI has been applied to help with clinical choices like the automatic diagnosing of carcinoma, bone abnormalities, help with deoxyribonucleic acid sequencing, and supporting surgery programing.

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Diagnosing of impeding respiratory organ sickness is a neighborhood of potential promise; it may increase detection accuracy and automatize screening to extend patient output.

Unsupervised machine learning leverages unlabeled info to correlate info among a dataset. This allows analysis of information like matching similar data in groups clusters or identification of anomalies outliers. Leveraged supervised machine learning to provide a model that accurately classified the potential for dermoscopy footage to be cancerous, Deep Convolutional Neural Networks (CNN) were used to realise this resolution producing a viable trained model from 900 footages. Enforced supervised machine learning to provide a Support Vector Machine-based classifier capable of distinctive footage of blood smear tests that are apparently to possess a method of leukemia.

All sickness management might have the benefit of AI; respiratory malady management is Associate in Nursing enduring key medical theme that encompasses bronchial asthma, chronic preventative pulmonic malady (COPD), pulmonary cardiovascular disease, bronchiectasis, sleep apnoea, etc and attributes to around two hundreds of UK deaths. COPD is hierarchic because the third most typical world reason for death within the last decade. Respiratory disease is usually diagnosed and monitored exploitation Spirometry (lung operate test) that evaluates the quantity of air expelled from the lungs (forced very important capacity) yet because the rate at that the air is expelled (forced breath volume). A similar, simpler, meter measure the height breath flow (PEF) is usually utilized in everyday clinical follow (often spoken as peak flow meter). This technology has been mechanical in nature however in recent years has been complete as electronic mensuration devices with wireless property, usually Bluetooth. Alternative identification techniques which enhance spirometry embrace the employment of biomarkers for COPD or carcinoma. Such biomarkers could also be complete as implantable monitors or home check kits; using AI would build a robust point-of-care system.

A DSS could also be made from domain data to tell, prompt, and advise respiratory disorder suffers in an exceedingly tailored fashion. This DSS might take inputs from PEF measurements, environmental information, and supervised machine learning classification of an individuals parameters. The realisation of such an answer are going to be the topic of future work and can be told by the standard and amount of knowledge which will be dependably obtained from sufferers of metabolism diseases.


SWOT analysis is a useful technique for understanding your strengths and weaknesses, and for identifying both opportunities open to you and the threats you face. SWOT analysis of AI application to respiratory disease management is an important aspect of understanding the potential of technology. The strength and weaknesses are with respect to internal aspects of AI. Any weaknesses may be addressed using technological reviews. The opportunities and threats are with respect to external aspects of AI. Threats are addressed using methods such as legislation and public education strategies although are more difficult to change. It is a method which act as starting point to tackle the challenge.


AI boasts the ability to recognise objects in images, transcribe speech, control machines, analyse DNA to detect genomic conditions, etc. Such technological strengths expedite the delivery of bespoke personalised care for respiratory disease management. AI’s ability to learn, find patterns, make decisions, etc. This may be facilitated through sensor measurements from spirometers, inhaler and medication usage, biomarkers, etc. In addition, qualitative data may be incorporated from active assistants chat bots to understand feelings, pain, anxiety, breathlessness, etc. Such solutions will assist with making informed machine decisions leading to medication alteration, clinician intervention or hospitalisation. AI could predict a respiratory disease decline, such as an asthma attack, before the patient even considers there to be anything untoward or peak flow measurements show a notable decline in lung function. The abundant recorded data that can be stored from the vast array of patients can help the training of accurate AI models, particularly through deep learning approaches. It is recognised that early detection of exacerbations in COPD can increase positive outcomes and reduce hospital admissions; telehealth-based systems interventions can decrease the costs associated with COPD patients and promote better self-management.


In late 2017 mainstream technical news sources published reports with taglines such as No one really knows how the most advanced algorithms do what they do. This suggests that the decisions that AI makes can be unexplainable or beyond logic. For example, chip maker Nvidia tested a car controlled by AI and later reported that the vehicle didn’t follow a single instruction provided by the programmers, instead using an algorithm that it had taught itself. While this may be good for attention grabbing headlines it creates disquiet for those who are looking to AI to intelligently manage the health decisions of the chronically ill. The vehicle in the test conducted itself appropriately and completed the task very well but the engineers couldn’t describe how it did it. This is not an isolated report but a growing trend. Furthermore, some complex AI systems have been described as inscrutable as it is not possible to make the AI system explain why it made certain decisions; such inability for humans to understand the decision-making processes of the system creates significant risk. These inscrutable solutions typically leverage machine learning based approaches to AI. When that risk is applied to chronic diseases like COPD such inscrutable systems are unacceptable and it would not be possible to verify if a death was an unavoidable outcome or a bizarre AI decision. Validation of medical technology requires substantial clinical testing which costs time and money; however, system reliability and observability are two of the key parameters of any system under test. AI in healthcare cannot be a leap of faith into the unknown and capricious systems may have no future in this space. To this end, such solutions may be limited to offer insight to inform decisions to be made by medical practitioners.


With respiratory disease being a significant strain on healthcare resources it is advantageous to push the monitoring of patients into the community to keep patients out of hospital. Smart homes are increasing popular  and there may be opportunities to integrate medical devices with these networks. Implementing smart healthcare devices into the community also helps to drive technology and services into remote areas to address the recognised issues of rural communities being last to get the latest technical services. Having intelligent sensors and systems that are continually monitoring your wellness is likely to have measurable health benefits over occasional clinic visits; such monitoring resolution will assist with faster responses to deteriorations as well as better diagnoses. This principle has been proven already with smart implanted insulin pumps which actively monitor blood glucose levels and automatically medicate to exacting levels accordingly. Further emerging technologies such as stretchable electronics and long range low power communication systems increase the potential for effective technology deployment.


The literature shows limited work on AI and telemetry for respiratory disease monitoring which means it is not as well understood as some other areas of study. However, some key explorations on the use of machine learning for respiratory disease diagnosis in clinical settings serve to effectively highlight the value of developing the technology. Other external threats may impede the uptake of AI for respiratory disease management including the fear of lawsuits against the system creator if AI is proven to have made a poor judgement or an obvious mistake. Likewise, the over-expectation of investors, technical communities and medical professionals, fears over security of data and hacking, poor investment into the technology, user error of any aspect of the system, issues with poor battery performance for portable devices, and global economics which demands continual cost cutting will all serve to threaten successful adoption of the technology. High profile figures such as the late Professor Stephen Hawking have done little to help the public perception of AI. Furthermore, the notoriously slow uptake of new technology in healthcare may result in the technology being quickly outdated, especially in the context of AI technology changing so rapidly.

A notable high profile AI failure will no doubt cast a long shadow over the trust of AI implementation for some time; in March 2018 an autonomous car being driven by AI was involved in a fatal pedestrian accident. Internet commentators reasoned that the outcome would have been the same regardless of whether a computer or a person was under control of the vehicle, nonetheless the headline will still capture the imagination of those already fearful of machines making life and death decisions. These issues are real and cannot be overcome easily; objections will remain and as such the technology could struggle to enjoy widespread acceptance without significant research and validation of medical AI systems. While automotive AI applications are clearly distinct from healthcare applications the public perception may not be adequately informed to appreciate the different techniques and technologies utilised.


The potential for impact and reduced costs for respiratory disease management is clear, however there are also significant obstacles to implementation and the approval processes in healthcare could make the new technology lag behind AI implementation in other sectors; healthcare could therefore be the last sector to experience the benefits of the IoT AI revolution. Correct implementation however will fundamentally enable fast reactions to emergencies in the short term and disease progression management in the long term. It will also have the capacity to address the increased management complications presented by multiple chronic conditions (MCC).

Non-medical devices purporting to contain AI may serve to impede authorised application in clinical practice as if these consumer electronics lifestyle devices are of questionable trustworthiness they may complicate acceptance of AI in automated healthcare decision making. General recommendations for well-managed implementation includes ensuring the technology is not hurried to meet industry expectations, the use of valid training datasets taken from a cross section of communities and diseases, ongoing healthcare cost evaluations, and the acknowledgement that AI is still in its infancy and has significant maturing to do before being ready for such an audacious task.

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Artificial intelligence for long-term respiratory disease management. (2019, Dec 18). Retrieved from

Artificial intelligence for long-term respiratory disease management
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