Generative AI in Healthcare
Learn Which AI Models (LLMs) Work Best for Your Medical Case: Diagnosis, Treatment, Imaging, and Patient Engagement.
AI in Healthcare
AI (Artificial Intelligence) in Healthcare represents a transformative suite of computational methods that can analyze complex medical data and support clinical decision-making through machine learning algorithms and neural networks.
The technology encompasses various tools, from image analysis for disease detection to predictive analytics for patient outcomes. These AI systems process vast amounts of clinical data to identify patterns and insights that help standardize and improve care delivery across medical institutions.
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Challenges in Healthcare
The landscape of medical care is dotted with challenges, but AI could offer innovative solutions to these pressing concerns.
Diagnosis Accuracy
Misdiagnoses may lead to incorrect treatments or delayed interventions.
Patient Engagement
Keeping patients engaged in their treatment plans can be challenging.
Operational Efficiency
Manual processing of vast amounts of data slows down operations.
Accessibility of Healthcare Services
Geographic and socioeconomic barriers restrict access to quality healthcare, especially in rural or underserved areas.
Medical Imaging
Processing and analyzing medical images can be time-consuming and prone to human error.
Medical Coding
Complex coding and auditing processes lead to mistakes and inefficiencies in patient records and billing.
AI Healthcare Use Cases
These tools are designed to assist healthcare professionals, not replace their judgment. They should always be used under proper medical supervision.
Use case 1
Symptom Analysis & Diagnosis
Problem: Misdiagnoses are a significant risk, often due to limitations in data analysis speed or access to comprehensive knowledge bases.
Solution: AI can help reduce errors by synthesizing extensive clinical data and medical literature, offering potential diagnoses or red flags for unusual symptoms.
OpenAI GPT 4
GPT 4 diagnostic accuracy, achieved through extensive training on diverse medical literature and patient data, enabling it to effectively process complex medical symptoms. This capability makes it highly valuable for healthcare providers in diagnosing a wide range of common illnesses. More about the model →
Google Gemini 1.5 PRO
Gemini is designed to provide high-confidence outputs with precision but lower recall, suited for clinical scenarios of critical diagnostic accuracy. The priority of certainty and minimization of false positives, render it an ideal choice for high-stakes diagnostic tasks like identifying complex or rare conditions. More about the model →
OpenAI GPT o1-preview
O1-preview can assist in narrowing and identifying overlooked differential diagnoses and help interpret complex test results, but must only serve as a supplementary information tool alongside clinical expertise and validated medical procedures. More about the model →
Use case 2
Medical Imaging & Accessibility
Problem: In areas with limited access to radiology experts, AI-powered imaging insights can offer quality diagnostics where human resources are scarce.
Solution: AI can assist healthcare providers in remote or underserved areas by providing fast and reliable imaging interpretations without the need for on-site specialists.
Anthropic Claude 3 Opus
Claude 3 Opus excels in interpreting clinical histories and imaging findings in radiology quiz cases (62.0% accuracy), specifically in generating differential diagnoses based on textual descriptions of imaging results for cases involving X-rays, CT scans, and MRIs. Learn about the model →
Google Gemini 1.5 Flash
Optimized for speed, Gemini-1.5-Flash processes images in an average of 150 milliseconds per image, making it particularly useful in emergency settings where timely diagnosis is critical. While it maintains a diagnostic accuracy of around 85%, its speed is a significant advantage in urgent care scenarios. Learn about the model →
OpenAI GPT 4o
GPT-4o achieves approximately 90% accuracy in interpreting radiology images like X-rays and MRIs. Its multimodal capabilities allow it to analyze visual and textual data together, enhancing radiologists' workflows and improving communication of findings. Learn about the model →
use case 3
Medical Coding
Problem: Coding and compliance regulations, such as ICD-10, require accurate and consistent application to avoid insurance claim denials and penalties.
Solution: AI can analyze patient records and EHRs and assign precise codes, helping to ensure compliance with regulatory standards and simplify audits.
P.S. Current research shows that GPT-4, Gemini Pro, and Llama-2-70b have low medical coding accuracy (below 50%), but new models are promising better performance.
OpenAI text-embedding-ada-002
Model successfully identifies the relevant code from a set of similar codes 80% of the time (better than GPT 4 with 50 %). Based on embedding textual descriptions of 2023 ICD-10-CM diagnosis codes, provided by the Centers provided for Medicare & Medicaid Services. Learn about the model →
Meta Llama 3.1 405B
Llama 3.1 405B, with a 128K context length and multilingual support, is a practical option for medical coding tasks. It can process long medical texts, handle diverse scenarios, and generate synthetic data, offering flexibility for task automation and fine-tuning. Learn about the model →
OpenAI GPT o1-mini
The o1-mini model advances AI coding capabilities, excelling in writing, generating, and debugging complex code with enhanced speed and precision, especially in research. Compared to previous models like GPT-3.5 — which often struggled with complex logic and required frequent oversight. Learn about the model →
use case 4
Patient Education
Problem: Keeping patients engaged with their treatment and ensuring compliance is crucial but difficult. Patients often forget instructions or lack clear understanding of their condition.
Solution: AI can support continuous, personalized engagement through reminders, educational content, and adaptive feedback tailored to each patient’s journey.
Grok-2
The model provides accurate, conversational responses, can set reminders and simplify medical terminology for patients. Grok 2’s design reduces hallucinations by emphasizing verified information, making it a reliable tool for delivering trustworthy patient education. Learn about the model →
Mistral 7B Instruct v0.3
Powerful solution without high computational costs, ideal for responding quickly to patient queries. When paired with RAG, Mistral can access up-to-date medical data, ensuring precise answers for complex health questions with minimum hallucination risks. Learn about the model →
NVIDIA Llama 3.1 Nemotron 70B Instruct
Excels at following complex instructions due to its reinforcement learning from human feedback, ensuring accuracy in patient assessments and medical inquiries. Its high scores on benchmarks like Arena Hard (85.0) reflect reliability, and the model's bias-minimized, diverse training data makes it suitable for nuanced, fair responses in sensitive medical interactions. Learn about the model →
Use case 5
Clinical Documentation and Research
Problem: Healthcare facilities handle immense administrative loads, from patient records to billing codes. Processing these manually is time-consuming and prone to error.
Solution: AI can optimize workflows by automating documentation, research, and data entry, which significantly reduces bottlenecks and frees up healthcare professionals for patient care.
Deepgram Nova-2-medical
This speech-to-text model excels in medical settings with 16% better medical term accuracy, handling 120-180 words/minute. Best for 15-45 minute consultations in quiet environments, processes audio 5-40x faster than competitors. While perfect for doctor-patient talks and telemedicine, struggles with heavy accents and noisy settings. Learn about the model →
Google Text-multilingual-embedding-002
The model supports over 100 languages, achieves high accuracy on retrieval and classification tasks, and demonstrates resilience in handling diverse user-generated content. Perfect for cross-lingual applications. Learn about the model →
Together M2-BERT-Retrieval-32k
The model enables semantic retrieval of relevant medical records, augmented decision support by understanding broader patient context, and exploratory analysis of clinical datasets to uncover hidden insights beyond simple keyword matching. Learn about the model →
