How AI promises a healthier future

Artificial intelligence (AI) can diagnose disease, assist surgeries, predict outcomes, and personalize medicine. In the words of the Introduction to AI and Privacy Issues for Healthcare Systems, "AI technologies can redefine the healthcare landscape in a gamut of ways, such as more accurate diagnostics, better, more personalized treatment plans, and improved care." 

Consequently, combining human compassion with machine precision holds immense potential, but only if privacy and ethics are carefully considered and integrated into the development and implementation of AI in healthcare. As AI advances, healthcare systems must maintain patient privacy and data security and uphold ethical considerations to promote patient trust and well-being.

Understanding AI

According to the book, AI "is the simulation of human intelligence processes by machines, especially computer systems that are engaged in these processes." Machine learning allows computers to "learn by themselves from experiences," using past data to make smarter decisions. In healthcare, AI could learn, reason, and self-correct to potentially help providers save lives, but only if developed responsibly.

More specifically, AI in healthcare could analyze medical images, predict patient outcomes, and assist in diagnosing diseases at an earlier stage. This has the potential to improve patient care, reduce human error, and ultimately save lives. So, what are some of the uses of AI in healthcare?

AI in diagnostic imaging

AI revolutionizes “the accuracy and efficiency of image analysis in diagnostic imaging," the text explains. In traditional practice, a radiologist might analyze hundreds of scans a day, risking fatigue and human error. 

However, when using AI, they "can analyze effortlessly these images with a precision that often eludes the human eye," leading to "higher accuracy" and "early detection of conditions like cancer, cardiovascular diseases, and neurological disorders."

Here, AI doesn't replace radiologists but augments them, complementing “the ability of radiologists to provide additional insights and give them a second opinion in decision-making." 

Another example of AI augmenting medical professionals is in pathology, where AI algorithms can assist pathologists in analyzing tissue samples for diseases like cancer. The collaboration between AI and human experts can lead to faster and more accurate diagnoses, resulting in better patient outcomes.

AI in preventative medicine

Healthcare providers can use AI to improve preventive medicine through predictive analytics. It can combine patient histories, genetics, and lifestyle data to "provide the likelihood of certain conditions," allowing for early interventions that could save lives. "Predictive analytics enables the identification of high-risk patients who require intensive monitoring and care," making healthcare proactive rather than reactive.

Additionally, there is the possibility of enhancing personalized medicine, where instead of "the one-size-fits-all strategy in medical treatment," AI allows for "bespoke treatment plans for individuals" based on genomic and lifestyle data. 

So, providers can use AI to examine genetic, clinical, and lifestyle data on an individual to create treatment plans to better suit each patient and improve success rates.

AI in organizational operations

AI-driven automation is "smoothening operations and raising the quality of care delivered," with capabilities like "appointment scheduling, billing, and management of patient data." Its administrative efficiency frees up healthcare providers to focus on patients.

Since healthcare costs are notoriously high, especially in the United States, AI "brings about cost reduction through improved efficiency," "optimizes operations," and "reduces the chances of errors." Fewer misdiagnoses, fewer unnecessary tests, and faster administrative processing lead to better value for patients and providers.

For example, AI-powered chatbots can assist patients with scheduling appointments and answering questions, freeing up time for healthcare providers to focus on more complex cases and improving overall patient satisfaction.

Yet, for all these benefits, the authors wisely caution that "the adoption of AI will raise critical concerns over data privacy, ethics, and the need for robust regulatory frameworks." 

Medical data is a prime target

According to the Introduction to AI and Privacy Issues for Healthcare Systems, AI in healthcare demands "robust regulatory frameworks" due to "data privacy concerns, ethical dilemmas, and the requirement of an ideal regulatory framework."

Currently, HIPAA regulations are the primary framework governing data privacy in healthcare, but they may not be sufficient to address the complexities of AI technology.

HIPAA protects patients by safeguarding the privacy and security of protected health information (PHI). Therefore, healthcare providers must follow strict procedures when handling sensitive information.

This kind of information, like genetic data, psychiatric diagnoses, or treatment for stigmatized conditions, is highly valuable to cybercriminals, who will often seek to monetize it in the form of financial fraud or identity theft. Once exposed, this kind of information can't be taken back, and it can cause long-term harm to individuals.

The increased use of AI in healthcare introduces new challenges. AI models must be continuously monitored to ensure that they uphold ethical standards and do not undermine patient safety. The majority of advanced models, especially those based on deep learning, depend on huge amounts of labeled data, posing potential vulnerabilities in the training and deployment phases.

Furthermore, healthcare data is often fragmented across institutions and systems, hence achieving "interoperability with a legacy of diverse systems" is a monumental task.

Go deeper: The pros and cons of using AI in healthcare

Ethical concerns when using AI

AI is only as good as the information it's been trained on. When datasets are skewed, incomplete, or mislabeled, the outcomes can perpetuate inequities or misdiagnoses, especially in marginalized communities that already suffer disparities in care.

There are also many other ethical concerns. How much transparency do patients have about AI involvement in their care? If an AI system recommends a course of treatment that a human doctor disagrees with, who bears responsibility? As the text emphasizes, "It becomes very important that healthcare providers understand these challenges and know how to overcome them." 

There is also the question of trust. Healthcare relies fundamentally on a bond of trust between patient and provider. Will patients trust systems they cannot see or understand? 

Algorithms can guide decisions or diagnoses, but their invisibility may lead to distrust or fear. When patients do not feel included in learning how their data are used, they could potentially choose to opt out of care altogether. To continue trusting a health system increasingly grounded in invisible, automated processes, we need transparency, ethical design, and patient-centered communication.

According to the CDC’s commentary titled ‘Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and Medicine’, “To promote health equity and ethical AI use in public health and medicine, it is recommended to develop inclusive AI policies, enhance ethical frameworks, and ensure transparency and accountability.”

Furthermore, “Investing in public and professional education about AI, fostering community engagement, and integrating social determinants of health into AI models are essential.”

Additionally, “diverse funding for research and evidence, continuous monitoring and evaluation of AI systems, and interdisciplinary collaboration are crucial strategies to ensure AI technologies are fair, equitable, and beneficial for all populations.” These strategies can help address biases and promote transparency in AI development and deployment.

The way forward

Current laws like HIPAA were not built with AI in mind. Protecting health data in a world of predictive analytics, genomic surveillance, and AI-driven decision-making requires new rules, standards, and enforcement mechanisms. Anything less exposes patients to potential data breaches, compromising their PHI.

The responsibility also lies within healthcare organizations, which must demand better from AI vendors. The responsibility also lies within healthcare organizations, which must demand better from AI vendors and work with vendors who prioritize transparency in their algorithms. 

Patients must be informed participants in their care, aware of when AI is being used, and given a say in how their data is handled. Informed consent should be considered as an assemblage of “autonomy and non-domination on the one hand, and self-ownership and personal integrity on the other,” argues Dr. Joanna Smolenski, an assistant professor at the Center for Medical Ethics and Health Policy at Baylor College of Medicine.

Technology companies, too, must implement privacy-by-design and ethics-by-design, rather than building safeguards after the fact.

The IEEE describes privacy-by-design as “privacy protections proactively into the design of technologies, business practices, and systems, rather than bolting them on later, organizations can uphold ethical data handling, comply with evolving regulations, and build user trust.”

Ultimately, this approach ensures that privacy and ethical considerations are integrated into a company's operations, leading to more sustainable and responsible practices in the long run. It will also help technology companies avoid potential legal and reputational risks associated with data breaches and privacy violations.

FAQs

Is patient consent required for email communication?

Yes, providers must first obtain explicit patient consent before sending PHI via email.

Can providers use regular email services for HIPAA compliant emails?

No, regular email services, like Gmail, do not offer the security features required for HIPAA compliance, so providers must use a HIPAA compliant platform, like Paubox, to send emails containing PHI.

Learn more: HIPAA Compliant Email: The Definitive Guide

Can AI be integrated into HIPAA compliant emails?

Yes, AI-powered features can be integrated with HIPAA compliant emailing platforms, like Paubox, to automate processes like patient consent management and send personalized emails while maintaining HIPAA compliance.