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Healthcare AI Is An Exciting Frontier — But Don't Forget About Equitable Development

Bair and Djulbegovic are resident physicians with expertise in digital health and AI.

The development of large language models like ChatGPT and, more broadly, generative artificial intelligence (gen AI), have been undeniably remarkable. These AI models, which can create new, synthetic text, images, and video by learning patterns from existing data, have pushed us to fundamentally rethink how we work -- or what work even means. Healthcare is no exception. As resident physicians, we are excited by reports of ever-growing gen AI use cases in healthcare systems, from helping physicians respond to patient messages and order diagnostic tests to tracking patient care gaps and designing "smart hospital rooms" that integrate sensors for patient safety monitoring.

However, a predominance of these initiatives is unfolding at a select number of institutions. As we continue to develop and experiment with AI technologies, equitable access to these technologies is crucial to prevent a widening divide in healthcare quality. We must work to ensure that smaller clinics, community hospitals, and underfunded institutions are not left behind.

Drivers of AI Disparity

Innovative uses of AI at top institutions are commendable and exhilarating. But the adoption, or even awareness, of gen AI tools is lacking or absent at most other hospitals that do not have the resources of elite research universities.

At our institution, for example, there was perhaps one didactic session (out of more than 120) during this past academic year that specifically discussed AI, and only then at a broad, superficial level. Opportunities for trainees to learn more about gen AI tools are limited by lack of awareness and accessibility. Our experiences likely represent the overwhelming norm of residents and clinicians nationwide.

This disparity is influenced by several factors. First, there are financial constraints. Most institutions do not have the endowments and funding for the research and development of AI technologies. In a self-perpetuating cycle, underfunded institutions also face challenges in securing funding due to less grant-writing expertise and connections to attract investment.

Then there are the educational gaps. The expertise required to implement gen AI effectively and monitor its use is significant, often requiring the creation of entire teams or offices (Stanford, for example, has a dedicated "Artificial Intelligence Clinical Integration" team). Hospitals that have been able to deploy AI often harness the skills of engineers and researchers drawn from affiliated schools of engineering -- affiliations absent at most hospitals. This educational gap extends to medical schools, where curricula often do not adequately cover the latest AI advancements.

Third, at elite research institutions, there is often strong support of the hospital administration in pushing for innovation, manifested through investments in AI programs and strategic partnerships. Smaller hospitals can struggle with administrative inertia, where leadership is either unaware of the potential of AI or understandably reluctant to invest in new technologies over uncertainty about the return on investment. Finally, cultural resistance to AI still exists. Healthcare professionals may be concerned about the possibility of gen AI fundamentally altering their roles. This hesitation is often rooted in a lack of understanding of the ways that AI can complement rather than replace human expertise.

Implications of Generative AI Disparities

Scientific and medical advances have always been pioneered by a few select institutions. So why do disparities in gen AI implementation matter? We believe the exponential pace of AI development and sheer extensiveness of its impact warrants special consideration.

Patient care quality

AI promises to enhance patient care by improving diagnostic accuracy and patient monitoring, creating more personalized treatment plans, and providing real-time medical decision support -- all at a faster rate. By automating away documentation and other non-patient-facing tasks, AI also indirectly allows clinicians to spend greater amounts of time helping patients make sense of their care.

If these technologies fulfill their promises, disparities in AI accessibility and capability will lead to variations in patient care. In the long term, populations served at AI-equipped hospitals will likely experience lower rates of chronic diseases, higher life expectancy, and overall better health outcomes.

Economic efficiency

One of the most common applications of AI is improving operational efficiency in healthcare delivery. This includes streamlining administrative tasks, clinical workflows, and resource management. Hospitals can pass on the resulting economic savings to further improve patient care. However, ironically, well-resourced hospitals are best positioned to reap these benefits. Smaller and underfunded facilities are unable to leverage AI for administrative tasks and must continue to rely on labor-intensive and costly processes.

Technological lockouts

The momentum of AI advancement raises the risk of "technological lockouts," in which institutions that fall behind in AI adoption find it increasingly difficult to catch up.

As well-funded institutions continue to invest in the latest technologies and modify elements in their health system (such as the electronic medical record) to incorporate these tools, they will develop proprietary AI solutions tailored to their specific needs and adapted to new challenges, thus maintaining their competitive edge. Moreover, gen AI thrives on vast amounts of high-quality data. Leading institutions often have access to comprehensive datasets and the infrastructure to manage and analyze this data effectively, while smaller hospitals can't keep up, hampering their ability to refine their AI models and resulting in significant technological gaps over time.

As certain institutions demonstrate an ability to innovate in gen AI, they will also attract top talent skilled in these tools, creating a concentration of expertise in well-funded hospitals. Healthcare workers in smaller or underfunded hospitals also have limited opportunities for training in AI technologies, perpetuating a skills gap. Finally, well-resourced hospitals are often more adept at influencing and navigating policy and regulatory frameworks, ensuring compliance while advancing their technological capabilities.

Hospitals less equipped to do this will suffer delays in implementing AI solutions. Over time, a fractured healthcare landscape may result, in which a small number of institutions constantly advance technologically while others remain stagnant.

Bridging the Disparities

Preventing widening disparities in AI will require extensive strategies involving government intervention, educational initiatives, collaborative models, and contributions from both the public and private sectors.

Government and policy interventions

Government policies can play a critical role in promoting equitable AI implementation. Policies should focus on providing funding, training grants, and partnership mandates that encourage the adoption of AI in smaller, underfunded, and community hospitals. There are precedents for this; past initiatives have supported electronic health record and health IT adoption. Regulations should ensure that AI technologies address local health challenges and are equitably distributed across different regions.

Education and training programs

To reduce the educational gap, initiatives to enhance gen AI knowledge at all levels of medical education are essential. Professional associations such as the Association of American Medical Colleges have developed resources for this purpose and should continue to offer guidance on the design of medical school curricula and professional development programs. Healthcare systems can collaborate with academic and corporate organizations to create institution-specific AI training modules, as demonstrated by the abundance of existing online courses on gen AI use.

Collaborative models

Creating collaborating models for resource sharing between AI-equipped hospitals and other hospitals has vast potential to reduce disparities. Well-resourced and innovation-focused hospitals should mentor and provide technical support to underfunded or smaller hospitals. Establishing regional AI "hubs" that serve as centers of excellence can facilitate knowledge and resource distribution. Meanwhile, less AI-equipped hospitals ought to proactively consider how gen AI can benefit their workflows.

Similarly, the private sector should be encouraged to invest in affordable AI solutions tailored to the needs of underfunded and community hospitals. By uplifting smaller, underfunded hospitals, the entire system becomes more resilient and capable of handling both public health crises and everyday medical issues alike.

Given how quickly AI tools are evolving, it is not premature to continue developing gen AI in an equitable manner. Neglecting to do so risks creating gaps that will be ever more difficult to bridge. By implementing the strategies outlined above, we can fulfill our ethical imperative to realize more inclusive healthcare systems in which AI technologies benefit all patients, regardless of where they are or the resources available to their healthcare providers.

Henry Bair, MD, MBA, and Mak Djulbegovic, MD, MSc, are resident physicians at Wills Eye Hospital/Jefferson Health in Philadelphia. Bair previously directed several courses on digital health at Stanford University School of Medicine. Djulbegovic is an AI researcher whose work focuses on biomedical applications of large language models.

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The Role Of Artificial Intelligence In Modern Healthcare: Transforming Patient Care

Artificial Intelligence (AI) has become a game-changer in various industries, and healthcare is no exception. AI technologies are revolutionizing patient care by enhancing diagnostics, personalizing treatment plans, and improving efficiency across medical practices. The integration of AI in healthcare promises not only to streamline operations but also to provide more accurate and timely medical interventions. This article explores the significant roles AI plays in modern healthcare and how it is transforming patient care for the better. From advanced imaging techniques to predictive analytics, AI is paving the way for a more proactive and patient-centered approach to medicine.

1. Enhancing Diagnostic Accuracy

AI has proven to be a powerful tool in improving diagnostic accuracy. Machine learning algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, with a level of precision that rivals or even surpasses human experts. These algorithms can detect patterns and anomalies that might be overlooked by the human eye, leading to earlier and more accurate diagnoses. For instance, AI can identify early signs of diseases like cancer, enabling prompt intervention and better patient outcomes. The continuous learning capability of AI ensures that diagnostic tools keep improving, offering healthcare professionals robust support in making critical decisions.

2. Revolutionizing Data Analysis

In healthcare, data is abundant but often underutilized. AI is changing this by revolutionizing how clinical data is analyzed and used. The modern clinical data analyst is leveraging AI to sift through vast amounts of patient data to identify trends, predict outcomes, and personalize treatment plans. By using AI-driven analytics, healthcare providers can gain insights into patient histories, treatment responses, and potential risk factors. This not only improves patient care but also enhances operational efficiency and reduces costs. The integration of AI in data analysis helps in making informed decisions, ensuring that each patient receives the most effective care possible.

3. Personalizing Treatment Plans

One of the most significant contributions of AI in healthcare is the ability to personalize treatment plans. Traditional treatment approaches often follow a one-size-fits-all model, which may not be effective for everyone. AI enables a more tailored approach by analyzing individual patient data, including genetic information, lifestyle, and medical history. This allows doctors to design treatment plans that are specifically suited to the patient's unique needs. Personalized medicine,

4. Advancing Predictive Analytics

Predictive analytics is another area where AI is making a significant impact. By analyzing historical data and identifying patterns, AI can predict future health events and trends. This capability is particularly useful in preventive medicine, where early intervention can prevent diseases from developing or worsening. For example, AI can predict the likelihood of a patient developing chronic conditions such as diabetes or heart disease based on their health data. Healthcare providers can then implement preventive measures and lifestyle modifications to reduce the risk. Predictive analytics also help in managing hospital resources more efficiently, ensuring that care is delivered promptly and effectively.

5. Improving Patient Monitoring

AI has revolutionized patient monitoring by enabling real-time, continuous observation of patients' health. Wearable devices and remote monitoring systems equipped with AI can track vital signs, activity levels, and other health indicators. These devices provide constant feedback to both patients and healthcare providers, allowing for timely interventions if any abnormalities are detected. AI-driven monitoring systems are particularly beneficial for managing chronic diseases, post-surgical recovery, and elderly care. They offer a proactive approach to patient management, reducing hospital readmissions and enhancing the quality of life for patients. With AI, healthcare providers can offer more attentive and personalized care, even outside traditional clinical settings.

6. Streamlining Administrative Tasks

AI is not only transforming clinical aspects of healthcare but also streamlining administrative tasks. Automation of routine administrative duties, such as scheduling, billing, and record-keeping, reduces the burden on healthcare staff and minimizes errors. AI-powered chatbots and virtual assistants can handle patient inquiries, schedule appointments, and manage patient data efficiently. This allows healthcare providers to focus more on patient care rather than paperwork. By improving administrative efficiency, AI helps healthcare facilities operate smoothly and enhances patient satisfaction.

7. Enhancing Drug Discovery and Development

The process of drug discovery and development is lengthy and expensive. AI accelerates this process by analyzing vast amounts of biological data to identify potential drug candidates and predict their efficacy. Machine learning algorithms can simulate how different compounds interact with biological systems, reducing the time and cost associated with clinical trials. AI also helps in identifying existing drugs that can be repurposed for new treatments. This advancement in drug discovery ensures that effective medications reach patients faster, improving treatment options and outcomes.

8. Supporting Mental Health Care

AI is making strides in the field of mental health care by providing new tools for diagnosis and treatment. AI-driven applications can analyze speech, text, and behavior to detect early signs of mental health issues such as depression and anxiety. These tools offer patients a discreet and accessible way to seek help, reducing the stigma associated with mental health care. AI-powered chatbots can provide cognitive behavioral therapy and other therapeutic interventions, supporting patients between sessions with human therapists. This technology enhances the accessibility and effectiveness of mental health care, ensuring that more individuals receive the support they need.

9. Facilitating Telemedicine

Telemedicine has gained significant traction, especially in the wake of the COVID-19 pandemic. AI plays a crucial role in facilitating telemedicine by enabling remote consultations and diagnostics. AI-powered platforms can analyze patient data and provide preliminary diagnoses, assisting doctors in delivering care remotely. This technology expands access to healthcare, especially for individuals in rural or underserved areas. AI in telemedicine also includes virtual health assistants that can monitor patients' symptoms and provide medical advice, ensuring continuous care even outside traditional clinical settings.

10. Enhancing Surgical Precision

AI is enhancing surgical precision through the development of robotic surgery systems. These systems use AI algorithms to assist surgeons in performing complex procedures with greater accuracy and control. Robotic surgery reduces the risk of complications and shortens recovery times for patients. AI also enables pre-surgical planning by creating detailed 3D models of patients' anatomy, allowing surgeons to prepare and execute surgeries more effectively. The integration of AI in surgery represents a significant advancement in medical technology, improving outcomes and patient safety.

Conclusion

AI is undeniably transforming modern healthcare, offering innovative solutions that enhance patient care, improve efficiency, and reduce costs. From enhancing diagnostic accuracy to personalizing treatment plans and advancing predictive analytics, AI's impact is profound and far-reaching. As technology continues to evolve, the integration of AI in healthcare will only deepen, promising even more groundbreaking advancements. For healthcare providers, staying abreast of these developments and integrating AI into their practices is essential for delivering the highest quality of care. The future of healthcare is bright, with AI leading the way to more effective, personalized, and efficient patient care.

 [A1]https://www.Ncbi.Nlm.Nih.Gov/pmc/articles/PMC8285156/

The Transformative Impact Of Artificial Intelligence In Health CareAI Demystified

Artificial intelligence (AI) has revolutionized the health care industry for decades, providing solutions that enhance the efficiency, accuracy and accessibility of medical services.

Use cases include the use of robotic technology in surgery, AI algorithms that assist with diagnostics and personalized treatment plans, and large language models that can write a medical note using a transcript from a patient visit. It is clear that AI's applications are vast and continually expanding, and the speed of innovation promises a future where health care is more precise and patient-centered. This article discusses examples of innovative applications of AI within the health care industry and provides an introduction to the patchwork of health care laws and regulations that surround and impact the deployment of such technology.

Diagnostic accuracy and early detectionOne of the most significant AI contributions to health care is improvement in diagnostic accuracy. Machine learning algorithms, trained on vast datasets of medical images and records, detect anomalies and patterns that may be missed by human eyes. For example, AI systems have shown remarkable proficiency in identifying early stages of diseases such as cancer, even before symptoms manifest.

Further, natural language processing (NLP) supports evidence-based research and contributes to the development of treatment plans. Clinical decision support (CDS) software assists providers across medical specialties in the provision of critical care to patients in real time and helps prevent adverse events. These systems analyze medical imaging and other clinical variables with a level of precision that significantly enhances early detection rates, leading to more effective treatments and better patient outcomes.

Personalized medicineAI is also at the forefront of personalized medicine, tailoring treatment based on an individual's genetic makeup, lifestyle and other factors. The expansion of electronic health care records (EHR), nationally and globally, has led to incredible amounts of readily accessible stored data. By analyzing data from various sources, including genomic data and EHRs, AI predicts how a patient might respond to different treatments. This personalized approach improves patient access to effective therapies while lessening harmful side effects, thus optimizing the overall treatment process.

Operational efficiencyBeyond direct patient care, AI is streamlining administrative and operational aspects of health care. NLP tools automate the documentation process, reduce the burden on health care providers, and allow them to focus on patient care. Predictive analytics forecast patient admissions, optimize staffing and help hospitals meet patient needs without unnecessary delays or resource wastage. Further, CDS software supports clinicians with computer physician order entry and the sending of electronic prescriptions. AI-powered virtual health assistants are transforming patient interactions with health care services. These assistants provide 24/7 support, answer questions, schedule appointments and even observe patients' health through remote monitoring devices. Lastly, as we have seen throughout the COVID-19 pandemic, telemedicine that is augmented by AI enables remote diagnosis and monitoring, making health care more accessible, and provides a lifeline for underserved areas.

The regulatory landscapeWhile there is no one comprehensive federal regulation addressing health care and AI, there are several regulatory agencies which have enacted regulations that govern the use of certain AI technologies. First, the U.S. Food and Drug Administration (FDA) regulates the production and sale of medical devices in the United States through the Federal Food, Drug, and Cosmetic Act (FD&C Act) and related rules and regulations.

As AI presents itself differently depending on its application ― accessory or component, stand-alone solution or used in the manufacturing process ― it is regulated according to its application. For example, the 21st Century Cures Act amended the FD&C Act to exclude certain clinical decision support software functions from the definition of a device under the law based on its purpose, e.G., functions that provide duplicate testing or prescription product prevention notifications.

Second, the Federal Trade Commission (FTC) Act gives the FTC investigative and certain law enforcement authority regarding unfair or deceptive acts or practices affecting interstate commerce and, therefore, has jurisdiction over a large range of industries, including health care.  The FTC has identified AI as a technology with the potential to harm consumers, such as the risk for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities. The FTC's business blog includes informal guidance on how the FTC applies its principles to AI and advises that AI tools should be "transparent, explainable, fair, and empirically sound, while fostering accountability."

The third example is the Centers for Medicare and Medicaid Services (CMS), which issued regulations addressing Medicare Advantage plans' use of algorithms, software or AI to perform utilization review and make medical necessity determinations. Under the federal rule, insurance companies must ensure that medical necessity determinations are based on the specific individual's circumstances.

As AI thrives on large datasets to inform and train the AI algorithms, health care entities that wish to deploy or are deploying AI software must be cognizant of the relevant federal, state and international laws on data privacy – including the Health Insurance Portability and Accountability Act of 1996 (HIPAA), the General Data Protection Regulation (GDPR), and comprehensive state laws such as the California Consumer Privacy Act (CCPA) as amended by the California Privacy Rights Act (CPRA).

The Biden administration has also made it a priority to address the risks of AI. On October 30, 2023, President Biden issued Executive Order 14110 on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. The executive order will impact many business sectors and is a significant development in the regulation of AI in the United States. Regarding the health care industry, the U.S. Department of Health and Human Services (HHS) is required to create the HHS AI Task Force with the purpose of developing a strategic plan that includes policies, frameworks and regulatory action on the responsible deployment and use of AI and AI-enabled technologies in health care, including in the drug development process. HHS is also responsible for prioritizing grantmaking and other awards, including grants awarded with the goal of increasing the participation and representation of researchers and communities currently underrepresented in the development of AI and machine learning models.

Health care organizations can efficiently and compliantly implement and use AI technologies by treating the software similar to any other technology that is deployed, i.E., understanding where it will be used, the types of data it will rely on, and important rights and obligations within the terms of use. Drafting an AI acceptable use policy, which outlines the guidelines, rules and procedures governing the development, deployment and utilization of the AI systems and aligns such policy with the organization's risk tolerance, can be a beneficial initial step for an organization to take.

Internal AI policies can serve as a roadmap for employees, outlining permissible use cases, data handling practices, transparency requirements, and accountability measures concerning AI technologies. For risk management to be effective, organizations may need to establish and maintain new appropriate accountability mechanisms, including new roles and responsibilities that oversee AI technologies and perhaps even a change to the culture and incentive structures. To accomplish this, an organization may need organizational commitment at senior levels and continuous employee training.

Despite its potential, the integration of AI in health care comes with challenges. Data privacy and cybersecurity safeguards are vital, as the sensitive nature of health data necessitates stringent protection measures. Additionally, ethical considerations regarding the use of AI in decision-making processes can help promote transparency, accountability and the avoidance of biases in AI algorithms in order to maintain trust in these technologies.

The integration of AI into health care is ushering in a new era of medical innovation. By enhancing diagnostic accuracy, personalizing treatment, improving operational efficiency and expanding access to health services, AI holds the promise of significantly improving health outcomes. As technology continues to advance, the collaboration between AI and health care professionals will be necessary to navigate the challenges and maximize the benefits of this transformative technology.

Richard J. Marinaccio, Partner at Phillips Lytle LLP and Leader of the firm's Artificial Intelligence Team, can be reached at [email protected] or (716) 504-5760.

Dorothy E. Shuldman is an attorney at Phillips Lytle LLP and a member of the firm's Artificial Intelligence Team and Health Care and Life Sciences Team. She can be reached at [email protected] or (716) 504-5778.

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