Introduction
Predictive AI is rapidly expanding into the healthcare domain. AI is expected to improve patient outcomes, increase the efficiency of hospitals' patient management, and improve bidding processes within the healthcare value chain, potentially saving augmented revenue for research and development. For patients, predictive AI can help to process de-personalized and anonymized health data in order to analyze data in greater detail. If applied appropriately, this could help to identify rare or detailed biomarkers for patients who have fallen out of the current majorities or determine a diagnosis early, which may help to treat the patient. That said, while predictive AI has benefits, the use of predictive AI in the healthcare domain brings about several ethical, social, and medical-legal issues. These issues span AI feature identification and explanation, de-identification and privacy, consent, accountability, and ensuring that value for patients and the health system is considered.
To limit any negative impact of AI, we recommend an ethical framework in which the value proposition for patients increases through more efficient and detailed treatments, with safeguards such as the correct diagnosis illustrated for patient use. Additionally, it could have implications regarding the assembly of in silico.
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Ethical Considerations in Predictive AI
Driving patient safety, privacy, and autonomy are primary duties of healthcare professionals considered from an ethical point of view and guided by key principles: beneficence, non-maleficence, and respect for persons. Artificial intelligence has created a new sphere of related indirect obligations stemming from how AI tools may impact patient care. The need to better train AI models in subsets of the healthcare professional’s typical population has implications for distributive justice. The creation of AI models that may increase clinical trial representation is seen as benefiting the greater good by broadening the clinical applicability of effective treatments. A better question is less about what ethical principles are compromised by AI technologies, and more about how AI can help us improve our adherence to ethical principles.
Ethical implications of predictive AI in healthcare have sparked meaningful debate over the last few years. One reverberating concern has been AI bias—where algorithms may either intentionally or unintentionally embed systemic racism or sexism into their encoded, value-laden decisions, potentially leading to mistreatment of people based on their biological inheritance. The development and application of AI should be done in transparent ways, with a focus on creating AI-enabled health systems that put people and communities first. Having transparent processes and use cases can help stakeholders and potential end users determine not only whether this data source is balanced and appropriate outside the development data setting but also whether it translates into a process that is fair and appropriate in the given context. Informed consent in AI predictive technologies brings forth ethical concerns related to patient autonomy and may change the relationship between the patient and the healthcare professional. Although patients should be informed of the implications of AI tools on care decisions, predictions of phenotypic states are offered to all patients as part of their standard of care. At a minimum, people have a right to know when they are engaging with an AI and the potential implications of such decisions. What’s more, healthcare professionals have an ethical duty to be able to explain such predictions and recommend appropriate recourse.
Patient Privacy in Healthcare AI Systems
There is a long tradition of regulating patient privacy in the US. Laws were enacted to protect exposed information, such as medical histories and clinical diagnostics, from reaching unauthorized third parties. These privacy-enabling policies were not enforced to prevent or slow down a wave of adoption of medical AI tools. Whether current patient privacy regulations could apply in the AI context and to such data collected, stored, and used in the development of predictive tools is uncertain. Data is collected by and from healthcare providers, at the point of care or remotely, and is then used to train a model. This typically requires raw or minimally pre-processed discriminative signals annotated by experts or guidelines. Here and in companion papers, we treat patient data accessed by health-related predictive analytics models as images, time-series strips, or tables. After training, the AI model is used prospectively, either directly by medical doctors or online by remote individuals.
Beyond policy, informatics and computer science experts have worked diligently to develop privacy solutions that can coexist with the extraction of value from de-identified patient data. Local methods, like secure multi-party computing, homomorphic encryption, or private aggregation of teacher ensembles, enable healthcare providers that lack trust to collectively develop a predictive model that is blinded from the data themselves but remains predictive. Similarly, federated learning combines a model centrally, but multiple centers contribute locally, ensuring that the raw data do not leave the collaborating centers. Private decentralized solutions minimize harm by holding de-identified data with the patients. Many experts stress the necessity and autonomy assumed by patient consent and control over the use of their personal, identifiable health information. The ability to make an informed choice about the prediction of one’s future health status is a final objective of the proposed work. Users may opt out of model utilization if desired. Similarly, regulations require patient consent for the handling of proprietary data. Ensuring patients' awareness is especially important if external data providers or brokers will participate in training. We assume that a broker’s primary motive is profit, not to improve patient convenience, healthcare access, or outcomes. If private entrances assist in training, the predictive model may also de-anonymize individual patient data by splitting and releasing data that corresponds solely to those individuals on which a model’s prediction had a violation label of 0. The tool can hide violations if or when penalties are calculated on variable alone. In such a case, a privacy-preserving predictor is trained for y, and the data is computed on the combiner of the private predictions.
Innovations and Challenges in Medical AI
Recent healthcare-focused AI has been focused on predictive modeling to improve treatment and ensure necessary interventions can be put in place. One tool forecasted the onset of sepsis in different patients with an average lead time of almost four days. Implementing recommendations from the tool reduced sepsis occurrences in different patients, with side effects, by nearly a third. Another tool correctly predicted nearly 29% more heart failure hospitalizations than traditional methods, tested in a population of nearly 10,000 patients. In the Netherlands, AI has been used to optimize surgical scheduling across Amsterdam’s largest hospital group, catering to a variety of predicted surgical complications. These are examples of how operational efficiency might be improved when you’re able to effectively predict patient health. After extracting and storing vast amounts of health data, the goal of predictive models is to predict the outcome of a particular health system: whether a patient will develop a certain disease, what treatment might work best for individual patients, and when they will need medical interventions. Such technology is advanced – this sort of real-time data fusion and decision-making would be hard to achieve for human operators alone, as well as highly effective. Ongoing and recent investments in AI infrastructure and data systems for health-related applications would be necessary, in other words, to make such tools feasible on a larger scale.
One significant problem with the immediate deployment of AI in traditional healthcare settings is the range of additional technological and organizational elements that such systems would require. For one thing, they would need to plug into existing information systems, often with huge datasets that may not yet be machine processable, such as images. In many hospitals, connected devices provide additional information on the patients within the hospitals themselves. In addition, patient data management systems would need to change significantly to allow the automated consolidation of data. Running a standalone or even networked predictive tool that automatically pulls in data from, for example, workplace and ambulatory systems, is not yet a reality in most healthcare settings. Where it is yet possible, the implementation, maintenance, updating, and training workflows of AI systems in established healthcare settings are also critical issues. Many pathologists and radiologists worry about the automation of processes that include the interpretation of predictive imaging data. Critics of the prediction approach may also be concerned that patients are reducing care through the use of a black-box tool, making it less effective. This is why much ongoing research is done to study and communicate the reliability of the growing AI in healthcare while their success appears to be clear on various levels, and it is of great clinical importance to know if any predictive tools should prioritize.
Conclusion
This essay has outlined developments in predictive AI and introduced the unique opportunities and ethical challenges concerning its use in patient care. Ethical considerations in the use of AI are crucial not only for the responsible development and deployment of AIs but also for trust in healthcare professionals and public acceptance of the technology. We have suggested that, once an ethically acceptable framework for guiding the use of predictive AI in patient care is decided, the technology has the potential to be transformative. However, significant concerns were raised about the proposed use of predictive diagnostics in alerting healthcare professionals to potential child abuse. A system that automatically alerts healthcare professionals for suspected child abuse would therefore not only compromise the privacy of children and their parents but would also put children at further risk and pose a risk to current social services systems that already struggle to cope with falsely reported cases of child abuse.
Though undoubtedly there is great potential in the use of predictive AI in patient care, we urge that patient comfort, well-being, and rights to access healthcare professionals who can keep their personal information confidential should be prioritized over the potential profits to be made by the application of such technology. Similarly, the rapid growth in patient data digitization and the broad range of current and potential uses of AI for health diagnostics suggest this is an area urgently requiring further research. Potential future directions for research could include developing alternative methods for early diagnostics and greater public awareness and engagement on potential solutions and the responsible development of predictive analytics.
As the relationship between patient data management, healthcare technologies, research, and ethics continues to evolve, a regulatory space should be created where understanding and collaboration will contribute to the development of ethically sensitive guidelines and technology that aims not to further compromise individual rights but to further improve the care individuals receive and the transformative science behind it. The relationship between AI and ethics in healthcare and innovations in child protection, risk management, and systems to facilitate wider public good often presents as oppositional and inherently problematic. However, greater understanding through the responsible applied use of technology to more nuanced lifestyle, environment, and socio-economic predictive healthcare possibilities is what we propose to promote in the future. Crucially, we suggest that further communication among policymakers, ethicists, and those at the front line of actual developments with AI in healthcare is vital in ethically and realistically moving forward.
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