AI in Healthcare: What should be considered

Artificial intelligence (AI) has the potential to transform healthcare, making it a powerful tool to augment human expertise rather than replace it. Its applications can extend far beyond information storage and retrieval or task automation. It can encompass and enhance diagnostics, treatment optimization, administrative tasks, and patient education. When used as an adjunct to traditional healthcare measures, it can revolutionize patient care in various settings, from the hospital to home health to public health and safety. It can enhance efficiency, reduce costs, and ultimately improve patient outcomes.

AI shows promise in the healthcare landscape primarily due to its ability to rapidly analyze massive datasets and reveal subtle patterns and insights that healthcare professionals may miss. It may be vital to revolutionizing the decision-making process, improving diagnostic accuracy, and reducing errors. In patient care, it has demonstrated the ability to personalize healthcare by creating individualized care plans, optimizing medication choice and dosages, and providing virtual health assistance and education.

Beyond the bedside, AI is beneficial for many administrative tasks that take healthcare professionals away from their primary goal and calling, caring for the patient. Automating paperwork, scheduling appointments, and managing electronic health records (EHFs) using AI can free clinicians to provide more direct patient care, enhancing healthcare efficiency and effectiveness.

However, although integrating AI into healthcare has some astounding benefits, it is crucial to recognize and acknowledge the limitations inherent in using AI in such a high-risk area. The adoption of AI into the practice of medicine presents several challenges and concerns. Healthcare professionals and patients should be aware of the ethical, regulatory, and practical considerations that warrant careful consideration before allowing AI to take over healthcare. From data privacy and security to bias and transparency in algorithms, the complexities of integrating AI into healthcare should not be taken lightly.

In the following article, we will explore the nuanced landscape of AI in healthcare and consider some of the challenges accompanying its implementation. Although AI offers many advantages, ignoring the issues can create more problems than it solves. By critically examining these issues, we can better understand the opportunities and pitfalls associated with AI in healthcare, paving the way for informed decision-making and responsible innovation in this rapidly evolving field.

Safety and Reliability Issues

AI use in a healthcare setting can lead to errors with severe consequences. An example is when an AI app predicting pneumonia-related complications inappropriately advised doctors to discharge patients with asthma. [1]

Safety and reliability issues are particularly concerning when using ChatGPT as generative AI in healthcare. An article published by the Lancet in 2023 stated that generative AI could have substantial ethical implications for the healthcare sector. The article authors identified various ethical considerations around copyright, attribution, plagiarism, and authorship of content.

ChatGPT also has the potential to produce inaccurate or misleading information, which could create irreparable damage in a healthcare scenario. [2] It has been seen to make assumptions about user intent and desire instead of asking for clarification when the provided information is insufficient. Furthermore, ChatGPT can only integrate information from 2021 and earlier and may not piece this information together correctly or in a way that makes sense. Without detailed prompts, it may provide outdated, unreliable, inaccurate, or incomplete results. [3]

Data Privacy and Security Concerns

The healthcare sector works with large amounts of private patient information, leading to a high risk of privacy and security issues when introducing AI applications into the system. Massive datasets are necessary for machine learning, but imputing large amounts of information for AI training and machine learning presents legitimate privacy and data security concerns. Other forms of AI are available to detect cyber-attacks and protect confidential information. However, the risk remains, and cyberattacks remain a significant threat to confidential and critical patient information. [1]

Bias and Discrimination in AI Algorithms

We’ve all heard the phrase, “Garbage in, garbage out.”

AI uses the information available to it. Any biases fed into the algorithm will be present in the final output. AI output is subject to the errors and biases of human contributors. [4]

AI needs training data. If the training data does not include all the variations across patient populations or lacks information about the complexities of the various medical conditions present in the population, AI will not pick up on these nuances. It will reinforce and strengthen any data biases present. For example, it may fail to consider or report information about an underrepresented population or an under-reported medical condition or disease. It may also not consider other critical data, such as location or economic factors, leading to healthcare inequalities and disparities in diagnosing or treating certain conditions or population subsets. [1]

Another problem is AI’s tendency to fabricate false information without evidence of its claims. [5][6][7]

Lack of Transparency and Interpretability

Transparency and accountability are critical concerns in healthcare. AI’s internal mechanisms to reach its conclusions are often not transparent and may be too complex for humans to comprehend, making it difficult for people to trust AI-made decisions. AI’s accountability in making healthcare decisions for individuals and its ability to compensate those affected by its use are emerging concerns.

The “black-box nature” of some AI algorithms hampers understanding and trust. Health care professionals (HCPs) have no idea what AI is measuring, how it comes up with its results, or how it arrives at its decisions. They only see the final output. The possibility of an HCP being blamed for AI errors increases the HCP’s perception of risk. [1] Implementing explainable AI techniques and transparent chains of responsibility can address these challenges. The roles and duties of manufacturers, healthcare institutions, and clinicians must be established before AI is widely accepted in healthcare. [8]

Patient Trust and Provider Adoption

Doctor-patient trust has been the backbone of healthcare since the beginning. Some patients are tentative about AI replacing or augmenting the healthcare treatments traditionally provided by humans. For this reason, a lack of trust in AI on the patient’s end also presents a problem. Although the research has mixed results, studies have indicated that many people, while generally willing to use AI-powered tools such as wearable technologies or ChatGPT, are uncomfortable with providers utilizing AI to diagnose and treat their medical conditions. Other factors that must be considered are the patient’s age, gender, education level, cultural background, and previous experience with technology and AI. [5]

On the other hand, healthcare professionals may also resist the widespread adoption of AI in the clinical setting. Some may fear that AI will diminish or entirely replace their roles. Additionally, training healthcare professionals to use AI can be costly and time-consuming. Administration may not see the addition of AI as a cost-effective solution, or some clinicians may struggle to integrate AI applications into their well-established daily tasks and routines. Others may worry that because AI has historically been predominantly used in non-clinical settings, attempting to implement it in critical patient care settings may be unwise at this time.[1]

Regulatory and Legal Frameworks

As the use of AI increases, so will the necessity of creating proper governance. Regulatory and legal frameworks need to be established. Proper governance over AI technologies is critical to patient safety and crucial to maintaining healthcare system accountability. Proper governance may also increase HCPs’ confidence in AI and improve acceptance in the healthcare community. Standardized guidelines and oversight mechanisms are needed to define responsibility, ensure informed consent, and balance innovation with ethical considerations. [1]

Conclusion

Artificial intelligence holds immense potential to transform healthcare, augmenting human expertise across various domains. From diagnostics to treatment optimization, administrative tasks, and patient engagement, AI promises improved efficiency and better patient outcomes. However, integrating AI into healthcare raises significant concerns.

Safety and reliability issues persist, exemplified by AI errors leading to inappropriate medical decisions. Biases in AI algorithms and data privacy concerns pose additional challenges, threatening patient confidentiality and perpetuating healthcare disparities. Moreover, the lack of transparency in AI decision-making undermines trust and accountability.

Patient acceptance and provider adoption of AI technologies remain uncertain. Regulatory frameworks are essential to ensure AI’s ethical and responsible use, safeguard patient safety, and maintain healthcare system accountability.

Navigating these challenges requires collaborative efforts and proactive measures. By addressing concerns surrounding safety, transparency, and ethical use, we can unlock AI’s full potential to revolutionize healthcare while upholding the highest standards of patient care and ethical practice.

Sources:

Read our other Healthcare related blogs here