The increasing use and reliance on informal sources of information by patients, particularly the Internet, has long been a problem. well-known trends inside medical system. However, with the advent of generative artificial intelligence (AI), this dependence has not only increased but also rapidly expanded. doctor others medical provider.
While previous AI models were primarily limited to analyzing and interpreting existing data, generative AI systems can create new content. This content creation capability, coupled with the ease of use and accessibility provided through a user-friendly interface, has led to rapid adoption and usage by many professionals, including healthcare providers. Overreliance on digital information sources has traditionally stemmed from patients seeking information such as: better understand their situation. Now, with generative AI, healthcare providers can also rely heavily on his AI assistance. decision making.
The application of generative AI in healthcare is Promising results were obtained, it is important to recognize that this technology is not a panacea. It cannot be universally applied to solve all problems in all medical settings. Physicians and healthcare providers must carefully deploy generative AI to mitigate unintended consequences. Responsible use is the key to harnessing its benefits while avoiding harmful consequences.
Generative AI works best in environments characterized by high repeatability and low risk. This effectiveness stems from the technology's reliance on past data to identify patterns and make predictions, with the assumption that future conditions will reflect past conditions. It is wise to utilize such technology in low-risk situations, especially when the impact of an error is low. This cautious approach has several advantages. This will help healthcare providers, and more importantly, patients, gradually understand the capabilities of AI and establish trust in its usefulness. Additionally, AI developers have a valuable opportunity to rigorously test and refine their systems in a controlled environment before deploying them in high-risk scenarios.
Potential health functions of generative AI
In this context, the suitability of generative AI in various medical activities can be evaluated.
Daily information gathering
Generative AI can improve the efficiency of information gathering and reporting by engaging patients in understandable language, resolving uncertainties, and summarizing data for healthcare providers. AI systems can help healthcare providers gather a patient's medical history by asking specific questions conversationally. An additional benefit of AI is that it can utilize health information exchanges (HIEs) to retrieve and analyze patient medical records and formulate appropriate questions based on the patient's medical background. For example, by cross-referencing a patient's medication list with current health complaints, AI can determine whether the patient is adhering to prescribed regimens or discontinuing conflicting medications in order to consider new medications. It can be verified. prescription. This process helps gather a more comprehensive medical history of the patient, which doctors can use to provide better care.
Additionally, patients who are already familiar with AI applications in various settings may find it easier to adapt to and trust similar AI technologies in the medical field. The tasks these AI systems perform tend to be repetitive and involve relatively low risk, which aligns well with the capabilities of current generative AI technologies. Such systems are adept at handling these processes and are able to perform within this area at a level that is generally considered satisfactory.
diagnosis
AI shows potential to improve capabilities diagnosis This applies especially in conditions where large amounts of data are available. Nevertheless, achieving accurate diagnosis and reducing stigma remains a challenge. challengeEspecially for less common diseases with limited data. Expression. The effectiveness of AI in diagnosing rare diseases is hampered by this lack of data. This means that the AI may not perform well due to insufficient training samples. Because AI systems improve performance even in common situations where data is rich, and as discussed below, in fragmented AI environments where large healthcare systems access large amounts of data. It is important to have access to comprehensive datasets to avoid the development of The abundance of unique data gives it greater advantages over smaller data. Currently available generative AI technologies, such as ChatGPT, are trained only on publicly available data. Reliance on general-purpose AI models for medical diagnosis will be premature unless they incorporate the rich medical history gleaned from widespread efforts to digitize medical records. Therefore, a healthcare provider should be cautious in introducing generative AI for diagnostics until he can train his AI on extensive medical datasets.
Even when healthcare providers train AI systems on sufficiently large medical datasets, it is important to mitigate potential risks. Specific workflows need to be designed in which AI supports, rather than replaces, doctors in the diagnostic process, meaning that AI acts as a valuable assistant rather than a replacement.
process
Although AI has potential applications in the diagnostic process, its use in treatment poses significant challenges, especially for the following reasons: accountability and responsibility concerns about, problems with Patient trust and acceptanceand technical and practical limit. Healthcare providers are ultimately responsible for the treatment they provide. In cases of medical malpractice, it is the medical provider who must justify his or her decisions. Changing the existing legal framework to shift treatment responsibility to her AI developer seems unlikely and could create too great a risk for AI developers to be held liable for medical malpractice. there is. Furthermore, patient trust in AI-managed care has not yet reached a level that can support widespread adoption.
Currently, AI lacks the advanced technical capabilities to replicate the nuanced tasks that doctors perform beyond simple medication management. Treatment is often highly individualized, which doesn't match AI's strengths in repetitive, low-risk tasks. Given these complexities, it seems unlikely that AI will be integrated into healthcare processes in the near future.
Post-treatment monitoring and follow-up
This field holds great promise for AI adoption due to two main factors. Firstly, it is important that patients comply with post-treatment advice; importanthealth care providers have limited means to be certain. compliance. Non-compliance can reduce treatment efficacy, negatively impact patient health and, in some cases, have economic consequences. provider. Second, the proliferation of wearable technologies, smart devices, and smartphones with their array of sensors provides an unprecedented opportunity to monitor patient behavior outside of the clinical setting. AI can leverage this data to provide real-time monitoring and personalized recommendations and interventions. Leveraging AI on this breadth of data allows healthcare providers to proactively address deteriorating patient health by alerting providers when urgent care is needed. .
Resident health management
By leveraging extensive datasets from electronic health records (EHRs) and HIEs, healthcare providers can significantly improve the management of their patient populations. This can be done even more effectively through the integration of predictive analytics that leverages AI to identify the highest-risk patients who would benefit most from timely medical intervention. For example, an AI algorithm can be trained to assess the likelihood of readmission after discharge by examining a set of patient characteristics. Based on these predictions, humans can be directly involved to develop a customized care plan and ensure that such patients receive the support they need to prevent further serious health events.
Implementing these AI applications may seem simple at first glance. However, it is important to recognize that its effectiveness depends on the availability of substantial and diverse datasets. Information beyond what is traditionally collected in EHRs and HIEs, such as a patient's social determinants, lifestyle choices, and daily activities, plays a critical role in patient health outcomes. Unfortunately, systematically compiled data are often lacking in these areas; Suboptimal performance of the current forecast model.
To improve the performance of predictive AI models for population health management, it is important for AI systems to access and analyze fairly large and diverse datasets. This can be achieved by integrating information collected from wearable technology and smart devices. Such devices can continuously monitor and record a wealth of health-related data, providing a more comprehensive view of a patient's health profile. Incorporating this data may enable more accurate predictions, resulting in more effective intervention strategies and paving the way for a more proactive and personalized approach to healthcare.
Policy recommendations
transparency
To optimize the adoption of AI in healthcare settings, fostering a climate of transparency among AI developers and fostering synergistic relationships between healthcare professionals and technology experts is paramount. This collaboration ensures that AI recommendations are medically appropriate and carefully scrutinized for accuracy, minimizing the potential for errors that can result from flaws in data entry or algorithmic bias. It is essential to keep it to a minimum.
informed consent
Furthermore, openness in communication with patients is strongly required. A patient needs to be thoroughly informed about the role AI will play in her medical journey. As well as understanding the privacy implications inherent in consenting to the use of AI-driven tools, especially when data collection goes beyond traditional medical records to include information derived from wearable devices and smart technology. It is important.
It is essential to educate patients about how their data will be used, the privacy safeguards in place, and the nuanced benefits and risks associated with AI in healthcare to enable informed decision-making. The above is extremely important. This education not only meets legal requirements; It serves as a foundational element to strengthen trust between patients and healthcare systems that evolve amid technological innovation.
Breaking the data monopoly with HIE
Addressing the potential deterioration of existing monopolies in the healthcare market is perhaps one of the most pressing concerns in this digital transition. As AI systems rely on large amounts of high-quality data for optimal performance, large healthcare providers with broad market share and, as a result, more data strengthen their position. This may unintentionally lead to the following situations: Increase in medical costs. In this scenario, small independent providers will be at a competitive disadvantage and will not be able to leverage AI to the same extent to enhance healthcare delivery. Such disparities can widen disparities in quality of care and further disadvantage underserved communities.
To mitigate this, it is important that industry leaders, regulators, and healthcare consortia spearhead efforts to democratize access to healthcare data for AI development. HIE can help in this effort. HIEs may act as aggregators and integrators of data from numerous providers. By centralizing such data, HIE can facilitate the deployment of AI systems that can learn from vast and diverse medical records.
More importantly, HIE can offer AI to affiliates as a shared service, allowing all member companies, regardless of size, to benefit from the insights gained from larger datasets. Such a collaborative approach could help level the playing field and allow smaller providers to improve service quality through AI. This will contribute to a more equitable healthcare environment where technology acts as a bridge rather than a barrier.
