Introduction
Enhanced operational efficacy and cost-effectiveness.
Intelligence medical systems can carry out various duties related to healthcare services straightforwardly and affordably. Even without human assistance, some of the jobs can be completed. A pill cam with AI integration can replace the traditional upper endoscopy. Secinaro et al. created a noninvasive AI method for diagnosing acute leukemia. by looking at the peculiarities of the bone marrow structure. Artificial intelligence (AI) has the potential to significantly improve operational efficiency and cost-effectiveness in healthcare in several ways. One way is automating tasks such as data entry, analysis, and decision-making, reducing the need for human labor and increasing accuracy (Lee, (2021).
Additionally, AI can assist in identifying patterns and trends in large amounts of patient data, leading to improved patient outcomes and reduced costs. For example, AI-assisted diagnostic tools can improve diagnoses’ speed and accuracy, leading to earlier treatment and better patient outcomes. Additionally, AI can optimize clinical trial design and help identify new drug targets, leading to more effective treatments and reduced costs. Overall, the implementation of AI in healthcare has the potential to improve patient outcomes while reducing costs by streamlining processes and increasing efficiency.
Enhancing biomedical research
Artificial intelligence (AI) can significantly enhance biomedical research in several ways. One way is to analyze large amounts of data, such as genomic data, which can help identify new disease markers and drug targets. Additionally, AI can analyze images, such as microscopy images, to help identify new biological structures and mechanisms.
AI can also simulate biological systems, which can help develop new drugs and therapies. For example, AI-based virtual screening can be used to identify new drug candidates by simulating the interactions of drugs with proteins, which can be more efficient and cost-effective than traditional experimental methods (Secinaro et al., 2021).
Another way AI can enhance biomedical research is by using natural language processing (NLP) to extract information from the scientific literature. This can help researchers quickly identify relevant studies, track the progress of a field, and identify new research opportunities.
AI in biomedical research can accelerate the discovery of new therapies, improve the efficiency and cost-effectiveness of drug development, and lead to new insights into the underlying mechanisms of diseases. AI is a “doctor” to identify, treats, and forecasts ailments. The indexing of research records can benefit greatly from AI. It can create research questions, quickly explore the literature, and test scientific theories. This may save considerable time and enable the researchers to complete compelling studies with pertinent conclusions as quickly as feasible.
Drug discovery
Drug discovery is identifying and developing new drugs to treat diseases. Artificial intelligence (AI) has the potential to enhance this process in several ways significantly. One way is through computer-aided drug design (CADD), which uses AI algorithms to predict the structure and activity of potential drug candidates. CADD can help to identify new drug candidates that are more likely to be effective and have fewer side effects.
Another way AI can enhance drug discovery is through virtual screening. Virtual screening is a computational method that uses AI algorithms to simulate the interactions between drugs and target proteins. This can identify new drug candidates likely to bind to a specific target protein and therefore have a therapeutic effect.
Using AI in drug discovery can accelerate the identification and development of new drugs, improve the efficiency and cost-effectiveness of the drug development process, and lead to new insights into the underlying mechanisms of diseases. Reinforcement learning offers a wide range of intriguing applications. These include de novo treatment strategies, the prognosis of drug activity, the prognosis of drug-receptor interactions, and prediction regarding drug reaction. They also involve enhanced image analytics, predictions of molecular structure, function, and automated production of novel chemical entities. An AIDD technique (Artificial Intelligence for Drug Discovery) was created by NuMedii, a biopharma company, to quickly discover linkages between medications, diseases, and systems, if any. Researchers developed Eve, an AI “robot scientist,” hoping to expedite medication discovery more cost-effectively.
Potential drawbacks of applying AI to the healthcare sector.
The following are a few of the biggest obstacles to the broad application of AI:
Data privacy and cyber security
Data privacy and cyber security are significant concerns in the healthcare industry, especially regarding the application of artificial intelligence (AI). The use of AI in healthcare requires large amounts of data, such as patient medical records, to be collected and analyzed. If this data is not adequately secured, it could lead to sensitive information being exposed or stolen. Additionally, AI systems can be vulnerable to cyber attacks, which could compromise the integrity of the data and potentially harm patients. Ensuring that AI systems are secure and that patient data is protected is essential to maintaining trust in the healthcare system and avoiding negative consequences. Privacy issues may occur when private patient information is gathered and shared by AI-based systems/technologies on massive datasets. As a result, AI technology must abide by legal requirements, medical ethics, and legislation. Patients’ sensitive, confidential information is susceptible to access by criminals who might harm the patient’s social life. Additionally, there may be a high likelihood of misdiagnosis due to AI systems’ incorrectly faked data. One study demonstrated that by only adding adversarial noises or just rotating, benign moles could be mistakenly identified as cancer(Alhashmi & Mhamdi (2019).
Reliability and security
Reliability and security are two potential drawbacks when applying AI in healthcare. Reliability refers to the ability of an AI system to produce accurate and trustworthy results consistently. In healthcare, decisions based on AI-generated insights can have significant consequences for patients. Therefore, these systems must be highly reliable and produce results that healthcare professionals can trust. However, AI systems are complex and can be influenced by data quality, model design and bias, and lack of transparency. This can lead to unreliable results and undermine the system’s credibility and users’ trust.
Security concerns the protection of sensitive data and the prevention of unauthorized access or manipulation. In healthcare, patient data is compassionate and must be protected to maintain trust and ensure patients’ privacy. AI systems that handle patient data must be secured against cyberattacks and unauthorized access. However, AI systems are vulnerable to cyber-attacks and hacking, compromising patient data and putting patients at risk. Ensuring that AI systems are secure and that patient data is protected is essential to maintaining trust in the healthcare system and avoiding negative consequences.
Any AI system error, if not corrected promptly, may result in incorrect outcomes for the tasks assigned, which may have detrimental effects. For instance, an AI tool that forecasts the risk that patients may experience problems from pneumonia gave clinicians the incorrect recommendation to send asthma attack patients home.
Accountability of technology use
Accountability refers to the ability to hold someone or something responsible for the actions or decisions made. In the context of AI in healthcare, accountability of technology use is a potential drawback because it sometimes needs to be clarified who or what is responsible for the decisions made by AI systems. This can be a problem when an AI system makes a mistake or a decision that harms a patient, as it can be challenging to determine who should be held responsible.
Another aspect of accountability is the need for more transparency in the decision-making process of AI systems, which can make it difficult for healthcare professionals, patients, and regulators to understand how and why a particular decision was made. This lack of transparency can also make it challenging to identify and correct errors or biases in the system. Finally, the use of AI systems in healthcare raises ethical questions about the responsibility of the healthcare provider, the AI system developer, and the regulator to ensure that the technology is used appropriately and does not harm patients.
Therefore, ensuring accountability of technology use in AI systems is essential to maintaining trust in the healthcare system, avoiding negative consequences, and ensuring the technology is used ethically and appropriately. “Who would be liable for the outcome?” if AI-based technology employed by medical staff results in the patient’s death. As a result, many technical, managerial, and ethical challenges will remain unresolved.
Loss of autonomy and support network
Loss of autonomy and support network are potential drawbacks when applying AI in healthcare. Autonomy refers to the ability of patients and healthcare professionals to make decisions and take actions independently. AI systems in healthcare can automate specific tasks and make decisions on behalf of healthcare professionals, which can lead to a loss of autonomy for healthcare professionals. This can be a problem because it can reduce healthcare professionals’ ability to use their judgment and expertise, leading to suboptimal care.
Support network refers to the network of people and resources that help patients and healthcare professionals to manage their health and well-being. In some cases, AI systems can replace or reduce the need for human support, leading to a loss of support networks for patients and healthcare professionals. This can be a problem because it can make it more difficult for patients and healthcare professionals to get the help and support they need to manage their health and well-being. Therefore, it is essential to consider how AI systems in healthcare can impact autonomy and support networks and to design systems that support, rather than replace, human decision-making and support networks.
AI health apps may give people the tools to manage their symptoms and care for themselves when needed. This might affect how many healthcare personnel are employed. Additionally, this may result in less reliance on family members, loneliness, and behavioral problems. AI agents may severely impact personal autonomy by limiting the range of available treatments and patients’ ability to give informed consent for the surgery.
Problems in generalizing to new populations
For most medical data types, AI systems still need accurate generalizability or clinical applicability.
Technical difficulties
Since non-medical experts typically create AI models, end users (such as patients and healthcare practitioners) have little influence over how the outcomes are determined. One of government policymakers’ most significant problems is more transparency. The limitations of AI technology are another issue because people create it, and even the slightest mistake can produce incorrect outcomes. A significant portion of medical data in the healthcare industry needs to be more structured and handled by AI systems, such as medical imaging. Last but not least, the data that needs to be entered into databases needs to be standardized, which may result in varying outcomes depending on the area (Alhashmi et al., 2020).
Organizational and managerial issues
There are many issues with creating AI, such as data exchange and ownership, as well as the threat of losing qualified healthcare practitioners and low-level employees.
Malicious use
Even though AI has the potential to help humanity, it can be misapplied. AI can stealthily observe and examine motor behaviors that expose a person’s identity and confidential information.
Conclusion
In conclusion, innovation is crucial in the current digital era. Healthcare executives may find AI and similar technologies beneficial adjuncts in various healthcare management functions. They should not be seen as a replacement for medical professionals but as a growing requirement that businesses must adopt to gain a competitive edge. Human Stupidity and Artificial Intelligence coexist to enhance the lives of only other dumb humans.
AI over shine its master in two critical aspects
Connectivity and update ability are vital areas where AI outperforms its master. Healthcare is particularly vulnerable to the potential of AI applications due to its revolutionary character in that sector of the economy. Artificial intelligence applications (AI) can alter the course of patient care and diagnosis and their way of life. In this study, we looked at how AI has affected healthcare and what new opportunities and difficulties it has brought about. Additionally, we advocate for creating an ethical and legal framework for AI and developing a societal consensus among all interested parties (Alhashmi et al., 2019).
References
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271.
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1-23.
Alhashmi, S. F., Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2020). A systematic review of the factors affecting the artificial intelligence implementation in the health care sector. In The International Conference on Artificial Intelligence and Computer Vision (pp. 37–49). Springer, Cham.
Alhashmi, S. F., Salloum, S. A., & Mhamdi, C. (2019). Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. Int. J. Inf. Technol. Lang. Stud, 3(3), 27-42.
Alhashmi, S. F., Salloum, S. A., & Abdallah, S. (2019, October). Critical success factors for implementing artificial intelligence (AI) projects in Dubai Government United Arab Emirates (UAE) health sector: applying the extended technology acceptance model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 393–405). Springer, Cham.
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