Artificial Intelligence (AI) is being used more and more in healthcare systems across the United States. It can help improve the quality of care, lower costs, and make operations run more smoothly, especially in hospitals and medical offices. But as AI becomes a bigger part of healthcare, people worry about ethical, social, and environmental effects. Medical practice managers, owners, and IT teams need to think carefully about how to use AI in ways that bring benefits while also handling issues like fairness and sustainability.
This article talks about sustainable AI in healthcare and how it can be designed and used carefully to meet the changing needs of healthcare in the U.S. without making inequalities worse or wasting resources.
Sustainable AI means making and using AI tools so they use resources well, can change with needs, are fair, and last a long time. It tries to reduce damage to society and the environment while keeping AI useful as it grows. In healthcare, sustainable AI should fit with how doctors work, patient care, and how health services are given.
Thinking about sustainability in AI includes several points:
It is hard to meet all these goals, especially in U.S. healthcare, which has many kinds of patients, unequal access to care, and different levels of technology in clinics and hospitals.
Researchers Haytham Siala and Yichuan Wang studied 253 articles from 2000 to 2020 and made the SHIFT framework. It helps guide responsible AI use in healthcare. SHIFT stands for:
This framework helps healthcare leaders check AI projects and solutions.
Healthcare managers in the U.S. need to use these ideas when adopting AI to handle ethical problems and build systems that work well for all people.
One big way AI is used in healthcare is to automate and improve workflows. AI tools linked with Industry 4.0 ideas like the Industrial Internet of Things (IIoT), digital twins, and big data can make clinics and hospitals run better.
Digital twins are computer copies of healthcare settings, like schedules and patient movements. Simulating these processes helps manage staff, find problems, and cut waiting times. For busy medical offices, AI-based phone systems like Simbo AI can help reduce workload by handling calls efficiently.
Collecting real-time data is important too. Sensors and connected devices gather information about patients and machines all the time. This data powers AI systems that adjust schedules and use resources smartly. For example, predictive maintenance watches medical devices to spot issues before they break down. This stops delays and keeps patient care timely.
Using AI in workflows not only improves efficiency but also saves resources. Better schedules and resource use lower energy use and waste. This helps healthcare places reduce their impact on the environment and improves patient experiences.
Besides this, AI tools help spread care more fairly. Simulations can find gaps in access, and managers can change staff or services to serve all community groups better.
Resource efficiency is often ignored in AI talks, but it is very important for sustainable AI. AI training and data centers use a lot of energy.
Industry 4.0 tools, including AI and digital twins, offer ways to run healthcare more sustainably:
In the U.S., healthcare centers often need to reduce costs and follow environment rules. Using AI for sustainability can bring financial and image benefits.
A big challenge in healthcare AI is stopping bias in algorithms. Biased AI can cause unfair treatment and make health gaps between racial, ethnic, or socioeconomic groups worse.
It is important to use diverse data when training AI. This data should represent the many kinds of patients in the U.S., especially those who often get less care. Also, fairness checks and involving doctors and patients are needed.
Health leaders must make sure AI vendors are open and keep watching for bias. Tools that check fairness help hospitals find and fix problems before AI affects important choices like diagnosis or treatment.
Using sustainable AI in U.S. healthcare is not easy:
Even with these problems, it is important to keep investing in tools, education, and ethics for AI.
Transparency is key for trust in healthcare AI. Managers and IT teams must know how AI makes choices. Clear AI lets people check and test algorithms. This helps with:
Simbo AI, which handles phone automation in medical offices, shows how clear AI can improve patient contact, reduce missed calls, and ease staff work, all without hurting privacy or patient experience.
Investing in sustainable AI is not just new technology; it is a plan that involves many groups. Healthcare leaders should:
When used thoughtfully, AI has the power to change healthcare in the U.S. by making operations better, supporting fair care, and lowering environmental impacts. Medical practice managers, owners, and IT teams play an important role in guiding AI so it meets today’s and tomorrow’s healthcare needs without increasing inequalities. Using sustainable, inclusive, and clear approaches helps create a solid base for AI’s continuing role in healthcare.
The core ethical concerns include data privacy, algorithmic bias, fairness, transparency, inclusiveness, and ensuring human-centeredness in AI systems to prevent harm and maintain trust in healthcare delivery.
The study reviewed 253 articles published between 2000 and 2020, using the PRISMA approach for systematic review and meta-analysis, coupled with a hermeneutic approach to synthesize themes and knowledge.
SHIFT stands for Sustainability, Human centeredness, Inclusiveness, Fairness, and Transparency, guiding AI developers, healthcare professionals, and policymakers toward ethical and responsible AI deployment.
Human centeredness ensures that AI technologies prioritize patient wellbeing, respect autonomy, and support healthcare professionals, keeping humans at the core of AI decision-making rather than replacing them.
Inclusiveness addresses the need to consider diverse populations to avoid biased AI outcomes, ensuring equitable healthcare access and treatment across different demographic, ethnic, and social groups.
Transparency facilitates trust by making AI algorithms’ workings understandable to users and stakeholders, allowing detection and correction of bias, and ensuring accountability in healthcare decisions.
Sustainability relates to developing AI solutions that are resource-efficient, maintain long-term effectiveness, and are adaptable to evolving healthcare needs without exacerbating inequalities or resource depletion.
Bias can lead to unfair treatment and health disparities. Addressing it requires diverse data sets, inclusive algorithm design, regular audits, and continuous stakeholder engagement to ensure fairness.
Investments are needed for data infrastructure that protects privacy, development of ethical AI frameworks, training healthcare professionals, and fostering multi-disciplinary collaborations that drive innovation responsibly.
Future research should focus on advancing governance models, refining ethical frameworks like SHIFT, exploring scalable transparency practices, and developing tools for bias detection and mitigation in clinical AI systems.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
