You’ve heard of LLMs, but how about LMMs? The former are large language models, which can generate text using AI trained on vast amounts of data. The latter are not unrelated: Trained on reams of medical data, large medical models help with patient diagnosis and treatment. And they’re swiftly changing how healthcare works.

LMMs Take Pressure Off Strained Healthcare Systems

Aging populations, rising costs, personnel and infrastructure shortages, and increased demand for services — healthcare systems worldwide are facing multiple challenges. This puts immense pressure on healthcare professionals, leading to burnout and threatening the availability of quality care.

Imagine a world where a supercharged assistant can analyze countless medical records in seconds, predict diseases early, analyze drug interactions, and tailor treatments to individual patients. That possibility is already here.

The Emergence of Large Medical Models

Working alongside and supporting healthcare professionals, LMMs gather and organize extensive medical data, including textbooks, patient records, and research papers, to create a massive digital library containing millions to billions of parameters. Using advanced AI techniques, LMMs train on the supplied information and apply their knowledge at great speed to assist doctors and other clinicians, like superhuman interns.

The year 2023 was a breakthrough time for LMMs, which have been demonstrating positive real-world outcomes. A new report from Tencent Research Institute, "From Clinic to Cloud: Challenges and Future Exploration of Large Medical Models," outlines the benefits and applications of these advanced systems. We learn that these models are already shaping the way doctors make decisions, how scientists conduct research, and how hospitals manage operations.

6 Ways AI is Improving Health Outcomes

The report finds that large medical models are already showing their usefulness across several areas in healthcare. Here are six of them.

1. Enhancing Clinical Decisions

Doctors can use LMMs to quickly analyze clinical texts, extracting crucial information that speeds up diagnosis and treatment. For example, a doctor faced with a complex or puzzling case can consult an AI program that instantly reviews similar cases and suggests the most effective treatments or course of action.

2. Interpreting Medical Images 

Reading medical images is time consuming, subjective and open to human error. AI can step in to analyze X-rays, CT scans, and MRIs, detecting abnormalities such as tumors with remarkable accuracy and speed. It’s like having an expert second opinion.

3. Customizing Treatment Plans

By analyzing DNA sequencing data, these models can identify genetic mutations linked to diseases, allowing for personalized treatment plans. It can incorporate new medical research and knowledge. This could mean tailored cancer and other therapies that are more effective and less debilitating than traditional treatments.

4. Monitoring Patient Health

LMMs can monitor the health of people with chronic illnesses remotely, analyzing physiological data to help manage conditions and reduce the need for hospital visits. Picture an AI that continuously tracks a diabetic patient’s blood sugar levels, alerting them and their doctor to any developments of concern.

5. Optimizing Hospital Management

Optimizing patient flow and resource allocation is a complex puzzle. LMMs can analyze patient needs and priorities, improving everything from queuing systems to ward management, ultimately enhancing patient satisfaction, resource allocation, and operational efficiency.

6. Accelerating Drug Development

In the world of pharmaceuticals, large models can predict drug-protein interactions and drug toxicity, aiding in the evaluation of a drug’s efficacy and safety. This can speed up the development of drug discovery and getting new medications to market.

Managing Challenges and Concerns

Before LMMs can be widely adopted however, we must address several critical challenges, including:

• Accuracy: Models are trained on data and word associations, so lack true understanding. Quality data is needed to ensure accuracy and overcome issues such as bias.
• Data privacy and security: Protecting sensitive and confidential personal health information is paramount. This requires advanced encryption, anonymization, storage, and access control.
• Transparency: It’s often unclear how answers are generated. Being able to reference sources and contributing data is important in building trust.
• Ethical concerns: Patients and regulators are understandably concerned about how sensitive personal health information is used and clinical decisions made. Robust safety and privacy nets must be in place to enable widespread adoption.

Case Study: Tencent Health’s HunYuan

Launched in 2023, Tencent’s HunYuan general large model has been upgraded with several AI products to improve patient care with user-friendly features that make healthcare more efficient and personalized, reduce the workload on medical staff, and deliver more precise treatments. These include:

• Intelligent Dialogue: Aids interactions with patients and healthcare providers
• Case Structuring: Efficiently organizes patient records, enabling doctors to make informed decisions quickly
• Detailed Imaging Reports: Rapidly analyzes medical images and highlights areas of concern
• Diagnostic Assistance: Provides reliable second opinions for accurate and timely diagnoses

These features are developed to be integrated into the patient care process to enhance service efficiency and quality.

Trust is fundamental to realizing the potential of LMMs. To achieve this, we must find effective solutions to key challenges.