How AI is Revolutionizing Healthcare: Benefits and Barriers
AI is transforming healthcare, enhancing care, but not without ethical challenges.
The Future of Medicine: How AI is Shaping Diagnostics, Treatment, and Patient Care
This article explores the transformative role of AI in healthcare, highlighting its impact on diagnostics, treatment, and patient care, while addressing the challenges and ethical considerations involved.
AI is a technology that has an impact on many sectors, and its role in healthcare is crucial. Healthcare companies are using Artificial Intelligence in order to transform the medical practices, enhance the patient’s health and optimize the healthcare processes. From identifying patient’s conditions to developing individualized treatment regimens, artificial intelligence is reshaping the healthcare industry and the methods used to handle patients’ information.
“AI is not just a tool but a transformative force in healthcare, reshaping everything from patient care to the way medical data is managed and analyzed.”
AI in healthcare has its benefits and risks. The use of generative AI in healthcare has potential in the development of drugs and medical image analysis. Nevertheless, there are also some drawbacks of AI in healthcare, including data protection issues and the lack of legal frameworks. This article aims to discuss the benefits and drawbacks of AI in healthcare, the present use of AI in healthcare, the possible future use of AI in healthcare, and the issues that need to be resolved in order to properly implement AI in healthcare.
Some of the Present Uses of AI in Healthcare
AI tools deliver personalized care directly to patients’ devices.
Diagnosis and Treatment
AI is revolutionizing healthcare by improving the diagnostic and therapeutic outcomes. Applications are being built based on artificial intelligence to help in the diagnosis of medical images including CT scans and X-rays. For instance, the AI algorithms have been used to detect lung nodules in CT scans with some level of accuracy. It has been proved to be efficient in comparison with other methods for the prediction of cancer risk (NIHR, 2023). This technology has the possibility of helping clinicians in decision making and enhancing the patients’ conditions.
“AI is rapidly transforming healthcare, offering new possibilities in diagnostics and personalized treatment that were previously unimaginable.”
When it comes to the heart, the advancements of AI are rapid. An AI based smart stethoscope has shown that the system has the potential to accurately diagnose heart failure in 90% of the cases irrespective of the age, sex or race of the patient (NIHR, 2023). This innovation could help general practitioners to identify heart failure at an early stage and therefore improve the patient’s prognosis and decrease the costs of the treatment.
AI is also being used in the fight against cancer to tailor the treatment according to the patient. Some systems are capable of predicting the behavior of tumor cells and their reaction to some of the specific drug combinations within 12 to 48 hours, which can help in providing better and more targeted treatment plans (NIHR, 2023). Further, new AI applications are being created to support the identification of bacterial infections and treatment of blood-borne diseases, construction from the earlier systems such as MYCIN that was developed in the 1970s (NCBI, 2023).
Patient Engagement
It has been seen that AI is also helping in increasing the patient participation and their compliance with the treatment plans. AI is being used by the healthcare providers to design patient care plans and enhance patient adherence to the recommended treatment. These systems use data from different sources such as EHRs, biosensors, and smartphones to identify patients’ needs and provide tailored recommendations (NCBI, 2023).
A major area of utilization is in the area of patient reminders and alerts. This is where the use of AI algorithms comes in since they are capable of creating content that is specific to the target audience and communication interventions to help patients act when they are most likely to benefit from doing so in their care pathways (Mend, 2024). This approach is useful in solving the last mile challenge in the health sector since patients’ non-adherence to the recommended treatment plans is a major challenge.
Administrative Tasks
In the administrative domain, AI is being applied in several areas with the aim of optimizing the healthcare delivery system and thereby taking the pressure off healthcare workers. Some of the applications of RPA include claims processing, clinical documentation, management of medical records and reports (NCBI, 2023). This automation can help free up a lot of the healthcare providers’ time that would otherwise be spent on paperwork and other administrative work so that they can spend more time with patients.
Chatbots are also being used with the help of Artificial Intelligence for patient engagement, mental health counseling, and remote medical assistance (NCBI, 2023). These tools are capable of answering simple questions and offering basic assistance so that the human agents can focus on more complicated questions.
In addition, AI is applied in appointment scheduling and management and in decreasing the number of patients who fail to turn up for their appointments. Through analyzing the patient records and the history of appointments, the AI systems can estimate the probability of the patient’s cancellation or missed appointment. This makes it easy to manage the resources in the right manner as compared to the traditional systems (Mend, 2024).
AI in Healthcare: Possibilities and Challenges
AI assists in diagnosing with detailed patient data.
Improving Patient Outcomes
AI has a lot of potential in improving the quality of patient care in all aspects of the healthcare system. In the diagnosis, the AI systems have been found to be very efficient in their diagnosis of diseases. For example, a study in the UK revealed that AI based interpretation of mammograms decrease the rate of false positives by 5.7%. Specificity was increased by 9% and sensitivity was decreased by 9%. Breast cancer diagnosis has been found to be increased by 4% with the help of mammography screening (NCBI, 2023). Likewise, in South Korea, AI was more accurate than the radiologists in the identification of breast cancer with a sensitivity of 90% as compared to 78% of the radiologists.
AI has the possibility of going beyond cancer diagnosis. Some of the deep learning algorithms have been effective in diagnosing diabetic retinopathy, EKG changes, and cardiovascular disease risk factors screening (NCBI, 2024). In the identification of pneumonia, the AI algorithms had a sensitivity of 96% while the radiologists had a sensitivity of 50%. These advancements in AI-assisted diagnosis have the possibility of enhancing early diagnosis and treatment of various diseases hence improving the health of the patients.
Enhancing Efficiency
AI is capable of enhancing the management of healthcare procedures and increasing the productivity of the system. In clinical laboratories, AI has been used in the improvement of testing processes through increasing the reliability and effectiveness of the testing process. Techniques in blood cultures, susceptibility testing, and molecular platforms have become automated in many laboratories across the world thus increasing efficiency (Biomed Central, 2024).
“AI’s role in healthcare is set to grow, offering both opportunities and challenges that must be carefully navigated.”
In emergency departments, AI algorithms can use the patient’s information to support the triage process and identify patients at higher risk and decrease the waiting time. Decision support systems that are backed up by artificial intelligence can assist in the formulation of decision making by the health care providers in real time. This can result in quicker clinical data analysis that is important in determining the severity of the given case and the need for an urgent response.
Advancing Medical Research
AI has become a game changer in medical research especially in drug discovery and development. Organizations such as Verge Genomics are applying machine learning to analyze genomic data of human beings and come up with drugs that may likely cure neurological diseases such as Parkinson’s, Alzheimer’s, and ALS at a cheaper rate as compared to other forms of treatment (NCBI, 2024). AI techniques can enhance the drug discovery process because they can estimate the efficacy of various agents and assess the potential toxicity of drugs at a more advanced stage of drug development.
In the future, AI can help in developing the so-called “digital twin” of the patient and the clinician can simulate the effects, safety, and experience of the interventions in a virtual environment on the patient before implementing it in real life (NCBI, 2024). This could greatly improve the accuracy and individualisation of medical therapies and thus help to provide better and more specific health care.
Challenges and Ethical Considerations
Balancing innovation with ethical responsibility in AI healthcare.
Data Privacy and Security
The use of AI in healthcare presents a major concern on the aspect of data protection and privacy. The healthcare industry deals with large volumes of data that are sensitive in nature and this makes the organizations vulnerable to cyber threats. The consequences of a data breach are dire and may include identity theft, financial scam, and the negative impact on the delivery of patient care (Lexalytics, 2024).
Laws that are in place today for instance the Health Insurance Portability and Accountability Act (HIPAA) are being seen as inadequate due to the advancement in technology. A research done by the University of California Berkeley claims that HIPAA is no longer relevant due to the recent developments in artificial intelligence especially in the light of COVID-19 pandemic (Lexalytics, 2024).
“Despite its many benefits, the use of AI in healthcare comes with significant ethical challenges, particularly in terms of data privacy and algorithmic bias.”
The importance of the healthcare data to the AI companies has raised the question on violation of privacy and ethical standards. For example, the “suicide detection algorithm” of the social network Facebook that employs AI to analyze the users’ posts and estimate their mental state is not regulated by the HIPAA (Lexalytics, 2024). Likewise, the companies that offer genetic testing services such as Ancestry and 23andMe are not protected by HIPAA which is a cause of concern as to how genetic data may be used (Lexalytics, 2024).
Algorithmic Bias
Algorithmic bias in AI healthcare systems is a situation where an algorithm worsens the effects of injustice based on social determinants of health such as economic status, race, nationality, belief, sex, physical ability, or sexual orientation (NCBI, 2023). This bias can occur in various forms, including performance differences between different patient subgroups in predictive tasks.
The reasons for algorithmic bias are numerous and can be categorized into several groups. Bias often arises from differences in the data used to train AI models, where some categories of people are either missing or underrepresented in the data (NCBI, 2023). Additionally, the inherent complexity of deep learning algorithms makes it difficult to pinpoint and correct biases that may be present in the decision-making process of these algorithms.
Integration with Existing Systems
The integration of AI into current healthcare systems is a significant challenge, primarily due to compatibility issues. One of the major challenges is the lack of integration across different platforms while maintaining the security, integrity, and confidentiality of data (Ominext, 2024). Furthermore, AI systems may be incompatible with existing care protocols, which have been critical in maintaining the quality of healthcare services (Biomed Central, 2024).
To address these challenges, healthcare organizations must implement strong encryption methods, access control measures, and regular audits. Additionally, AI systems should be developed and deployed responsibly, with periodic reviews to identify and mitigate biases, ensuring transparency in decision-making (Ominext, 2024). Collaboration between technology providers and healthcare organizations is crucial to developing integrated systems that incorporate AI into the healthcare infrastructure.
Conclusion
The use of AI in the healthcare sector is transforming medical practice and the delivery of patient care, creating both opportunities and risks. The benefits of AI, including improved diagnostics, enhanced system efficiency, and contributions to medical research, are evident, with numerous examples of AI outperforming traditional approaches in areas such as disease identification and drug development. These advancements have the potential to enhance the quality of care, making treatments more individualized and effective.
“As AI continues to advance, it’s crucial that healthcare providers balance the technology’s benefits with the ethical considerations it raises.”
However, the use of AI in healthcare also presents ethical and practical challenges. Concerns regarding data privacy and security, as well as the potential for algorithmic bias, must be carefully managed to uphold ethical standards. Moreover, the integration of AI systems into existing healthcare infrastructure requires careful planning and collaboration between technology providers and healthcare organizations. The future of AI in healthcare is promising, but it is essential that the benefits of the technology are not outweighed by the risks as it continues to evolve.
At Ampliro, we are dedicated to guiding healthcare organizations through the complex process of integrating AI into their operations. Our team can help you unleash the power of AI to improve diagnostics, personalize patient care, and streamline administrative tasks. We also offer customized “Insights” reports, providing detailed analysis and strategic recommendations to ensure that your AI implementation is both effective and ethically sound. Contact Ampliro today to learn how we can support your journey toward a more innovative and efficient healthcare system.
About the Author
Ampliro’s CEO, Andreas Olsson, has extensive experience in AI and works closely with healthcare organizations to help them integrate this technology to enhance patient care and streamline operations.
References
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3. Mend, 2024. The Future Role of AI and Patient Engagement in Healthcare. Available at: https://mend.com/resource/future-role-ai-and-patient-engagement-in-healthcare/
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10. Ominext, 2024. Challenges of AI Integration in Healthcare. Available at: https://www.ominext.com/en/blog/challenges-of-ai-integration-in-healthcare
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