Generative AI for Healthcare

While earlier AI models were largely limited to analyzing and interpreting existing data, generative AI systems are capable of creating new content. This capability has led to a surge in its adoption and use by many professionals, including health care providers. Now, with generative AI, health care providers might also lean heavily on AI-assisted decision-making.

While the application of generative AI in health care has yielded promising results, it is crucial to recognize that this technology is not a panacea. It cannot be universally applied to solve all problems in every health care setting. Physicians and health care providers must deploy generative AI discerningly to mitigate unintended consequences; responsible use is key to harnessing its benefits while avoiding adverse outcomes.

Generative AI performs optimally in environments characterized by high repetition and low risk. This effectiveness stems from the technology’s reliance on historical data to identify patterns and make predictions, under the premise that future conditions will mirror those of the past. Utilizing such technology in low-risk situations, particularly where errors carry minor consequences, is prudent. This cautious approach offers several advantages: It enables health care providers and, more importantly, patients to gradually comprehend the AI’s capabilities and establish trust in its utility. Additionally, it affords AI developers valuable opportunities to rigorously test and refine their systems in a controlled environment before deployment in higher-stakes scenarios.

Routine information gathering

Generative AI can enhance the efficiency of information collection and reporting by engaging with patients in understandable language, resolving uncertainties, and summarizing data for health care providers. An AI system can assist health care providers with collecting the medical histories of their patients by posing specific questions in a conversational manner. An additional advantage of AI is its ability to tap into health information exchanges (HIEs) to retrieve patient medical records, analyze them, and formulate pertinent inquiries based on the patient’s medical background.

Diagnosis

AI has shown potential in enhancing diagnostic procedures, especially for conditions with substantial data availability. Nevertheless, achieving accurate diagnoses and mitigating biases remain challenges, particularly for less common diseases with limited data representation. The effectiveness of AI in diagnosing rare diseases is hindered by this scarcity of data, which means the AI might not perform as well due to the insufficient learning sample. Even for common conditions, where ample data exists, it’s crucial that AI systems have access to comprehensive datasets, both to improve their performance and to avoid the development of a balkanized AI landscape where big health systems with access to large amounts of proprietary data widen their advantages over their smaller counterparts.

Treatment

While AI may have potential applications in the diagnostic process, its use in treatment raises significant challenges, particularly due to accountability and liability concerns, issues with patients’ trust and acceptance, and technological and practical limitations. Health care providers bear the ultimate responsibility for the treatments they administer. Altering the existing legal framework to shift treatment responsibility to AI developers seems improbable, and it would likely pose too great a risk for AI developers to assume liability for malpractice.

Post-Treatment Monitoring And Follow-Up

This area holds considerable promise for AI deployment, driven by two main factors. First, while patient adherence to post-treatment advice is crucial, medical providers have limited means to ensure compliance. Non-adherence can diminish treatment effectiveness, negatively affecting patient health and potentially resulting in financial repercussions for providers. Second, the proliferation of wearable technology, smart devices, and smartphones equipped with an array of sensors offers an unprecedented opportunity to monitor patient behavior outside clinical settings. AI can leverage this data to provide real-time monitoring and personalized recommendations and interventions. With access to such extensive data, AI can also enable medical providers to proactively address patient health deterioration by alerting providers when immediate medical attention is necessary.

Population health management

Leveraging extensive datasets from electronic health records (EHRs) and HIEs, medical providers can significantly improve the management of patient populations. This can be done even more effectively through the integration of predictive analytics, utilizing AI to identify the most at-risk patients who would substantially benefit from timely medical interventions. For instance, AI algorithms can be trained to assess the likelihood of hospital readmissions post-discharge by examining a set of patient characteristics. Following these predictions, customized care plans can be formulated with direct human involvement to ensure that such patients receive necessary support to prevent further serious health events.