Ethical Considerations of Leveraging AI in Respiratory Care
Healthcare professionals must understand various ethical considerations for leveraging AI in respiratory care to mitigate potential adverse implications. According to Zhang et al. (2020), these professionals must understand various ethical principles of using AI, including needing more patient autonomy. Patients have the right to recommend their preferred treatment and management options. Thus, healthcare professionals must align recommendations provided by AI platforms with patient needs (Chawla & Walia, 2022). In addition, healthcare professionals must apply AI platforms that foster data privacy to safeguard the personal information of patients experiencing respiratory illnesses. According to Ellertsson et al. (2023), data privacy regarding sensitive information is one of the fundamental patient’ rights that caregivers must adhere to in respiratory care. Hence, conducting a comprehensive forensic analysis of the adopted AI platform is vital to ensure it guarantees data privacy. Failure to provide data safety places healthcare professionals and institutions at risk of legal consequences due to mandatory provisions obliging patients’ rights.
Furthermore, healthcare professionals face ethical issues of responsibility for decisions converged by AI platforms. According to Chawla and Walia (2022), healthcare professionals should emphasize using accurate and reliable AI platforms as they are liable for decisions made by AI platforms should they instigate complications. As a result, healthcare institutions must conduct extensive assessments to ensure they leverage accurate and reliable AI platforms in respiratory care. Additionally, healthcare professionals must understand the potential bias prompted by AI platforms that may impact respiratory care (Zhang et al., 2020). Hence, these professionals must ensure they feed accurate information on the platforms to reduce the possibility of bias. According to Kaplan et al. (2021), AI platforms exhibit a potential bias toward underserved patient populations, such as minority community members. Thus, healthcare professionals should make keen observations when dealing with such patient populations.
AI Impacting the Functioning of Clinicians and Respiratory Therapists
Clinicians play a critical role in respiratory care, for instance, monitoring medication adherence among patients, especially children. AI is revolutionizing the scope of monitoring medication adherence through smart inhalers that monitor drug delivery techniques and medication adherence (Stivi et al., 2024). These smart inhalers provide real-time feedback to physicians to enable them to monitor patients effectively. According to Bonini et al. (2022), around half of patients experiencing respiratory illnesses using inhalers do not adhere to their prescribed medication, thus increasing the risk of disease exacerbation. As a result, leveraging AI improves the ability of clinicians to enforce effective medication adherence to improve patient outcomes. Additionally, AI incorporated effective image recognition, increasing the ability of respiratory therapists to classify skin biopsy lesions. According to Pai et al. (2022), enhanced image recognition enables respiratory therapists to note marginal abnormalities and subtle changes in lesions to converge at the appropriate mechanism to tackle them before they escalate.
Respiratory therapists in traditional care faced the challenge of missing small details that would have changed the scope of administered interventions. These missed details hindered the therapists from recommending appropriate mechanisms before the patient suffered severe symptoms (Stivi et al., 2024). AI also aids in analyzing pulmonary function tests to determine subtle changes in functionality to determine the need to change medication prescription by assessing the effectiveness of the prescribed medication regimen. Furthermore, AI has influenced the ability of respiratory therapists to diagnose fibrotic lung disease, which was a concern in traditional respiratory care. AI platforms leverage high-resolution CTs to diagnose the disease without surgical procedures, unlike traditional respiratory care that hinged on surgical procedures to diagnose the condition (Pai et al., 2022). The ability of AI platforms to detect temporal and spatial patterns supports respiratory therapists in administering effective medical procedures (Bonini, 2022). Deep neural networks supported by the platforms increase diagnosis accuracy, enhancing respiratory care effectiveness.
AI Impact on Healthcare Costs Through Predictive Modelling to Predict Disease Exacerbation
AI is vital in reducing healthcare costs in respiratory care by predicting disease exacerbation to counter disease progression. According to Kaplan et al. (2021), AI accurately predicts disease exacerbation due to its ability to detect marginal changes in pulmonary function and vital signs indicative of respiratory illnesses. AI platforms monitor patient data to determine potential changes that indicate worsening conditions despite the administered treatment procedures. These platforms communicate real-time data to pulmonologists to conduct more scans and leverage AI to analyze medical images (Barakat et al., 2023). AI platforms can recognize marginal changes that human analysis cannot detect in traditional respiratory care. Detecting minor changes enables healthcare professionals to utilize the appropriate interventions to counter the symptoms before progressing to severe symptoms requiring advanced treatment procedures (Ellertsson et al., 2023). In traditional respiratory care, patients incur increased treatment costs in managing severe complications as human tests recognize significant symptom exacerbation, negatively affecting treatment interventions.
Moreover, predictive modeling assists healthcare professionals in tracking how a patient responds to the administered medication regimen. According to Barakat et al. (2023), some respiratory patients initially exhibit tolerance to a given medication regimen but then start experiencing sensitivity as time progresses. The drug sensitivity increases the possibility of disease exacerbation or suffering from other medical complications posed by drug side effects. According to Ellertsson et al. (2023), AI assists in continually monitoring vital signs and functionality to identify whether the administered medication regimen enables the patient to manage or alleviate the experienced symptoms. The move aids healthcare professionals in changing the regimen, prompting the possibility of disease exacerbation or side effects to mitigate potential adverse health effects (Kaplan et al., 2021). The process assists in reducing the healthcare costs incurred during recurring hospitalization or treating long-term side effects emanating from the treatment processes.
Current Scenario in Respiratory Care and Future Scenario with AI
The current scenario in respiratory care faces three primary challenges: patient monitoring, treatment planning, and effective diagnosis. According to Mekov et al. (2020), these three aspects affect effectiveness in delivery care; however, AI platforms contain the tools to revolutionize these aspects despite some issues, such as ethical considerations when leveraging AI platforms. The current scope of patient monitoring necessitates patients to visit their caregivers physically, which is time and resource-consuming (Rashid et al., 2022). AI eases patient monitoring through platforms such as telehealth that enable healthcare professionals to monitor patients remotely. Besides, AI-powered devices track vital signs and pulmonary functionality to communicate real-time data to the patients and their caregivers for further monitoring subject to the detected changes (Farzaneh et al., 2023). In addition, the current scope of treatment planning needs to leverage extensive data to establish the treatment routine. The routines fail to consider genetic patterns, sensitivity to certain medication regimens, and patient outcomes.
AI platforms impact treatment planning by fostering the development of personalized treatment routines. AI utilizes extensive data in developing personalized treatment routines, such as genetic patterns, patient records, patient outcomes, and medical imaging outcomes (Rashid et al., 2021). This extensive data aids in developing effective treatment routines that adopt a multifaceted approach to addressing respiratory illnesses. Furthermore, the current scope of respiratory diagnosis is characterized by inaccurate diagnosis and diagnosis delays. A1 platforms leverage deep machine learning techniques to interpret medical images accurately and detect marginal abnormalities to facilitate accurate and early diagnosis (Farzaneh et al., 2023). Besides, these platforms detect marginal changes to limit disease progressions. Integrating AI in future respiratory care prompts some challenges, mainly ethical considerations. Healthcare professionals must address ethical considerations when utilizing AI platforms in respiratory care, for instance, ensuring that the platforms guarantee data privacy (Mekov et al., 2020). Additionally, healthcare professionals must ensure patient autonomy despite leveraging AI in respiratory care.
References
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Bonini, P. H. O. U. M. (2022). The Current and Future Role of Technology in Respiratory Care.
Chawla, J., & Walia, N. K. (2022, November). Artificial intelligence-based techniques in respiratory healthcare services: a review. In 2022 3rd International Conference on Computing, Analytics and Networks (ICAN) (pp. 1-4). IEEE.
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