Evaluating the Accuracy, Reliability, and Ethical Considerations of Using Large Language Models for Diagnosis and Treatment Recommendations in Hospitals

Large language models (LLMs) have improved a lot in the field of medicine in recent years. For example, Google’s Med-PaLM 2 scored 85% on medical exam questions like those on the United States Medical Licensing Examination (USMLE). This test checks how well doctors know medicine. Similarly, OpenAI’s GPT-4 scored 86% on similar exams. These scores show that LLMs can provide medical information and clinical reasoning almost like human doctors.

LLMs can write clinical summaries, answer patients’ questions, and even create question-answer sets that are very similar to those made by doctors. This helps both in teaching and talking with patients. Big health systems, like UC San Diego Health, are using models like GPT-4 in patient portals such as Epic’s MyChart. This lets the LLMs help with patient messages and virtual triage. It can make things faster by lowering the need for doctors to answer routine questions directly.

But the accuracy seen in exam tests might not always match real-life use. Usual tests for natural language processing (NLP), like MedQA, don’t show all the difficulties in real patient care. To solve this, new methods like agent-based modeling (ABM) simulate the interactions between patients, clinicians, and AI agents. This way, they can check how the system works in a more real clinical setting. This also helps to see if LLMs can do multi-step reasoning, make decisions, and use tools properly. It gives a clearer idea of how reliable these models are in hospital work.

Challenges in Reliability and Evaluation of LLMs in Hospitals

Just being accurate does not mean LLMs are always safe or effective for diagnosing and treatment advice. The trustworthiness of these models depends on many things, such as how well they adjust to different hospitals and patient groups. Since many AI models are trained on certain datasets, they might not fit local rules or patient types everywhere.

To test LLMs properly, researchers made Artificial Intelligence Structured Clinical Examinations (AI-SCEs). These are based on traditional tests called Objective Structured Clinical Examinations (OSCEs) that check if medical students have the right skills. AI-SCEs create realistic clinical tasks where AI needs to think deeply, use tools, and understand data. This testing goes beyond simple benchmarks by seeing how AI handles complex and real patient care situations.

Watching the models after they are put into use is also important. Clinical data can change over time as new guidelines or diseases appear. Without regular checks and updates, LLMs could get outdated and make mistakes. Testing on new sets of data after deployment helps catch changes in how the model performs and finds any biases that were missed before.

Ethical Considerations and Bias in Medical AI

Using AI in healthcare means we must think carefully about ethics, especially bias and fairness. Several kinds of bias can affect machine learning models:

• Data bias happens when the training data is incomplete, does not represent all groups well, or favors some groups.
• Development bias comes from choices made during AI design, like picking features or preferred algorithms.
• Interaction bias occurs when models do not handle changes in clinical practice or patient behavior over time properly.

These biases can cause unfair treatment plans, wrong diagnoses, or unequal care. This can hurt patient safety and increase health differences between groups.

The article by Matthew G. Hanna and others says that AI systems should be clear and explainable to doctors and regulators. Being transparent helps users find errors or biases early, which makes patient care safer by holding AI accountable.

Rules and regulations help make sure AI models are well tested before use and stay checked afterwards. It is also important to define who is responsible when AI causes mistakes. This is especially critical in areas like mental health and pathology, where wrong advice can have big effects.

A full evaluation process, covering data choice, bias detection, clinical use, and ongoing watching, is needed to reduce risks. Keeping fairness, openness, and ethical review helps hospitals keep patient trust and make sure AI helps care.

AI and Workflow Automation in Hospital Front Office Operations

While AI in clinical care gets much attention, AI is also changing hospital front office and administrative work. One fast-growing area is phone automation and answering services powered by AI, such as Simbo AI.

Simbo AI uses advanced natural language processing and large language models to manage phone calls and patient scheduling with less human work. Automating routine phone calls helps hospitals run more smoothly, shorten wait times, and lets staff focus on harder administrative jobs.

AI in front-office work offers several benefits:

• Calls for scheduling, cancellations, and general questions are answered quickly and correctly.
• Automated systems lower staff burnout from handling many calls.
• AI messaging helps communication between patients and healthcare providers, supporting virtual-first triage used by hospitals like UC San Diego Health.
• Integration with electronic health record (EHR) systems keeps patient data updated and logs communication automatically.

Hospital IT managers need to think about how front-office AI works with clinical AI systems to make patient care smooth. When systems like Simbo AI’s phone automation combine with clinical LLMs, hospitals can provide more coordinated care that improves both administration and medical work.

Also, these automated tools help raise diagnosis accuracy and treatment safety indirectly. When patient intake and communication happen without errors and delays, doctors get better and fuller information. This lowers the chance of mistakes during diagnosis and treatment planning.

Practical Implications for U.S. Hospitals

Hospitals in the U.S. face special challenges in using AI because of the variety of patients, rules, and resources. Large hospital groups, such as those linked to the University of California (like UC San Diego Health and UCSF), are leading the way in adding LLM agents into patient portals and clinical processes. Their work shows some key points to consider:

• Diversity in patient groups means AI models need to be trained on big, varied datasets or retrained locally often to reduce bias and keep fairness.
• Privacy and legal rules like HIPAA require AI systems to protect patient data carefully, whether for diagnosis support or communication automation.
• Teamwork between fields is needed to build evaluation tests like AI-SCEs. Clinicians, data scientists, IT staff, and ethicists must work together to make sure AI tools fit real clinical needs.

Hospitals that want AI should create teams from different fields to pick, test, put in place, and monitor AI tools continuously. This helps catch bias, technology gaps, and integration problems early.

Also, hospital leaders must balance AI benefits with ethical duties. Being open with patients about how AI is used in diagnosis, treatment recommendations, and office work helps keep trust and supports patient-centered care.

Summary

Large language models in hospitals can help with clinical diagnosis, treatment advice, and automating office work. Models like GPT-4 and Med-PaLM 2 show good medical knowledge and can improve how clinics work and communicate with patients. Still, careful testing with advanced simulation tools such as AI-SCEs is important to make sure they work well in real practice.

Ethical issues like bias, transparency, and responsibility must be addressed to keep patients safe. Front-office call automation, like that from Simbo AI, supports these clinical AI tools by making hospital processes smoother. Together, these technologies show growing use of AI that hospital managers and IT staff in the U.S. need to understand to use AI safely and well.