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AI Diagnostic Tool for Medical Image Analysis

Generative AI technologies are transforming entire industries, including healthcare. Medical imaging systems, which are pivotal in patient diagnosis and treatment, will witness the transformational changes that AI offers. Particularly, Generative AI imaging models like MidJourney and Dall-E, bring exciting opportunities in medical image analysis that you don’t want to miss. 
There is no better time for entrepreneurs to invest, innovate, and release their own AI-powered medical imaging tools. According to Statista, AI in the healthcare industry will be worth $187 billion in 2030, an increase of 33% CAGR from 2022. Such optimism is expected as AI was proven to have saved physicians 17% of their time in administrative tasks. 

Yet, generative AI is continuously evolving, which complicates efforts to bring such products to the market. Moreover, healthcare is a tightly-regulated industry that requires specific knowledge to ensure a compliant product. This begs the question - how do you develop an AI medical imaging product while navigating multiple hurdles along the way?


As the co-founder and tech lead at Uptech, I’ve helped numerous companies with our AI development services. We’ve integrated a generative AI virtual assistant to Plai, built an AI summarizer, Hamlet, and released our AI-based social avatar generator, Dyvo AI. And we’re ready to help you unleash your own medical AI solutions.
So, read on and learn how to train, develop, and deploy an AI diagnostic tool for healthcare professionals.

What is a Medical Imaging AI Platform for Healthcare

A medical imaging AI platform comprises hardware and software that medical professionals use to obtain, store, and analyze imaging data. Physicians use the AI-powered platform to detect various medical symptoms, including cardiovascular diseases, cancer, dermatitis, and fractures. Such platforms are powered by artificial intelligence, specifically computer vision models that enable scanning, enhancing,  analyzing, and predicting clinical symptoms from image data. 

The use of AI separates such platforms from conventional radiology machines, such as X-Ray and CT scanners. With AI, data scientists can train the medical imaging tool to recognize specific disease representations with annotated datasets of medical images. In simpler words, the medical image AI model learns to predict what a disease looks like after learning from various images of the disease.  

Training a medical imaging AI tool requires collaborative efforts from machine learning engineers and medical experts. An AI computer vision application must be trained with various pathological representations to infer with strong confidence, even for marginal cases. This is critical to ensure physicians are supported with accurate results when diagnosing and treating patients. 

Benefits of AI computer vision in medical imaging

benefits of AI Diagnostic Tool

Integrating generative AI capabilities with medical imaging offers patients and healthcare providers wide-ranging benefits.

Early disease recognition

Some pathological symptoms might escape manual human inspections because they are subtle and hardly discernable from the imaging data. Generative AI models like convolutional neural networks (CNN) can extract in-depth spatial features from the imaging results. This allows patients to be aware of possible symptoms and seek further consultations. 

Diagnosis time reduction

Applying AI in medical imaging processing streamlines operational setups and analysis. Instead of manually analyzing radiology scans, doctors can speed up medical diagnoses with the AI scanner's summaries. As such, healthcare providers can scale medical imaging processes to meet growing demands. 

More accurate image interpretation

Generative AI is trained to extract and analyze geospatial data to find hidden patterns that escape human eyes. When integrated with medical systems, it provides exceptional capabilities to accurately interpret X-ray, CT, MRI, and other imaging results. For example, a recent study found that CNN-aided computer systems can detect brain tumors in MRI images with up to 98.56% accuracy.

Accessibility of diagnostics in a variety of clinical settings

AI-assisted medical imaging enables clinicians to analyze and process imaging data beyond medical departments or facilities. Patients can receive imaging reports on mobile apps integrated with imaging systems. Likewise, clinics in rural areas facing limited resources can use portable AI diagnostic tools. 

More effective and cheaper treatment

AI allows hospitals to optimize their resources and healthcare workforce for better patient outcomes. Instead of spending substantial time on screening and analyzing the results, clinicians use AI-generated summaries to scale patient treatment. Medical image AI solutions also help healthcare providers reduce expenses by eliminating repetitive and redundant processes. 

Types of AI Vision Applications in Healthcare

From pre-screening to long-term treatment, AI imaging solutions offer wide-ranging possibilities for radiology use cases. 

Types of Medical Image Analysis

Cardiovascular risk assessment

AI diagnostic tools can augment visual inspections in cases of symptomatic complaints of cardiovascular diseases. During preliminary inspections, radiologists obtain reports from x-rays and radiographs, which they can feed to the AI tool for further analysis. Deep learning models allow physicians to narrow down the symptoms, such as artery blockages, and render appropriate treatments.

Musculoskeletal injuries detection

Imaging systems are instrumental in assessing musculoskeletal injuries. However, some fractures, tissue injuries, and abnormal musculoskeletal conditions might be overlooked in trauma assessments. With AI, physicians can be alerted of the subtle signs picked up by initial radiology scans. Studies have observed that analyzing CT scans with CNNs will likely improve clinical evaluations of fractures. 

Neurological disease detection 

Brain studies for detecting neurological diseases are challenging with existing medical imaging technologies. Often, it involves iterative segmentation, comparison, and assessments of possible biomarkers detected in the brain MRI. Using AI for diagnostics improves accuracy and speed in classifying possible diseases while reducing false positives. 

Cancer screening

Medical imaging is instrumental in detecting tumors and precursors of common cancers. The challenge that healthcare providers face is to ascertain possible malignancies accurately. AI helps physicians diagnose and provide appropriate treatment by supporting cancer prognosis. It prevents unnecessary invasive treatment in false positive cases and ensures early detection of cancerous cells.

Challenges of Using AI In Medical Image Analysis

As promising as AI medical diagnosis sounds, there are several concerns that founders and developers face. After all, generative AI is still an evolving machine learning technology, with new findings influencing development efforts. Moreover, the medical industry is highly-regulated, presenting several factors that need a deeper look.

Preparing high-quality dataset

Medical imaging AI uses domain-specific datasets to train the underlying deep learning model. The dataset contains annotated laboratory images that enable the model to learn what specific symptoms look like. Any negligence in preparing the dataset will directly impact the imaging model, leading to inaccurate or biased predictions. 

Choosing an appropriate model

Convolutional neural networks and vision transformers are widely used for image detection and classification. However, each variant model has comparatively different performance. For example, some might be more prone to hallucinations than others. Thus, evaluating each model for key inference metrics is vital before choosing a model for medical imaging.

Addressing ethical concerns

Medical imaging models inherit bias and inaccuracies from their training dataset. When applied to patients, they may produce incorrect diagnoses, which physicians must resolve. Besides, regulatory bodies might express concerns about how companies curate medical datasets for training the AI model. 

 AI In Medical Image Analysis

Integrating diverse systems 

Medical imaging AI needs access to radiology results generated by existing machines. To do that, you must ensure disparate systems across medical facilities can exchange data with the AI platform reliably. This involves standardizing data formats like DICOM or upgrading their networking capabilities. 

Complying with regulatory standards

Healthcare providers must comply with applicable regulations or risk hefty penalties when introducing new apps or equipment. For example, hospitals in the US are subjected to HIPAA, which specifies guides for protecting patients’ health data. Meanwhile, AI imaging systems targeting the European market must adhere to the  General Data Protection Regulation (GPDR), 

Enforcing data security

Generative AI applications move vast amounts of data across connected storage, increasing data risks. Rising data breaches affecting medical facilities, as reported by ENISA, called for encryptions, access management, and other measures to produce digitally resilient medical AI. Hence, founders must apply security best practices in the AI system and cloud infrastructure to secure patients' data. 

HIPPA Compliance When Using Generative AI for Healthcare

If you use a computer vision model hosted by AI providers, be mindful of their usage and HIPAA compliance policies. Most providers, including OpenAI, do not allow their models to be used for medical use cases. You must sign a business associate agreement with OpenAI to use its AI models for HIPAA workloads. Therefore, deploying open-source CNN or vision transformer models on a secure cloud infrastructure is better. It allows you better control of data governance, policies, and security according to HIPAA guidelines. 

Challenges of Using AI In Medical Image Analysis

AI Tech Stack for Medical Image Analysis

Neural network models are the foundation of AI-enabled medical diagnostic tools. MidJourney, Dall-E, and Stable Diffusion are popular deep learning models enabling text-to-image generation. These models are pre-trained with vast datasets, which provide them with exceptional ability to render unique and realistic images from prompts. 

When developing AI-enabled medical diagnostic tools, your choice extends beyond popular deep learning models. Many imaging models are variants of convolutional neural networks (CNN). CNN consists of multiple hidden convolutional layers allowing it to extract geospatial data efficiently from image sources. For example, VGG-16, RestNet50, and InceptionV3 are CNN variants capable of detecting and classifying objects in radiology images. 

Another interesting imaging model is the Segment Anything Model (SAM) by Meta AI. SAM can identify and create a boundary on any objects from prompts with zero-shot learning. Researchers have adopted SAM for medical imaging by enabling the model to process DICOM images in a study. They found the model generalizes well even on complex pathology images. 

Step-by-step guide on building AI medical records summarizer 

At Uptech, we’ve implemented generative AI features in new and existing products. We follow specific steps to balance cost, model accuracy, and time-to-market. Below is what it takes to build a functional AI medical analytic tool. 

1. Define the purpose

Determine the goal and problems your product will solve in the healthcare space. For example, some imaging tools are designed for detecting cancer markers, while others may offer different capabilities. 

2. Collect and prepare training datasets

Focus your effort on curating medical datasets related to the product’s purpose. If you’re building an AI tool for cancer detection, you’ll need images with different variations and stages of malignancies. A diverse, equal representation of the subject ensures a fairer imaging model.

3. Choose the right model

Compare imaging models’ performance against machine learning benchmarks and choose that that works best in the specific use case. For example, several models have been tested against the retina, fracture, adrenal, and other MedMNIST datasets. If you’re diagnosing pneumonia, Google AutoML Vision proved the most accurate. 

4. Train the ML-model

We don’t advise training an ML model from scratch because of the immense computational resources needed. Instead, fine-tune pre-trained models with the medical datasets you’ve curated. Adjust the hyper-parameters until the model converges to the required inference level. 

5. Integrate AI with the user platform

Use APIs or other means of communication to set up a seamless data pipeline between the product and the AI model. Remember to secure all data points from adversarial risks.

6. Create a user interface

Clinicians need an intuitive user interface to access the summarized report the AI model generates. Focus on best UI/UX practices using easily understandable text, layout, and controls.

7. Test the medical image analyzer

Put the AI medical imaging tool to several test stages. Evaluate the model for bias, accuracy, overfitting and other metrics. You’ll need to refine the model with additional fine-tuning if it fails to generalize on actual image data.

8. Launch the AI medical analyzer 

Now, your generative AI medical imaging tool is ready for public use. Launch the product to a specific department and scale it facility-wide when it proves stable. In the meantime, be prepared to handle post-release bugs and technical issues.

9. Monitoring and upgrade

Both AI and medical disciplines undergo rapid changes, and so should your product. Observe the model to ensure it performs consistently. Update the platform to meet changing regulations, optimize model performance, improve data accessibility, and more when necessary. 

Tips on using Generative AI 

Integrating generative AI with medical systems requires delicate care, technical expertise, and domain-specific knowledge. These are several tips that help with training, testing, and deploying AI imaging models. 

  • Curate high-quality datasets. When training the model, use datasets that fairly represent the pathological symptoms and patients to minimize bias and ensure model accuracy. Models trained with questionable datasets may produce erroneous results and negatively impact patient outcomes.
  • Apply protectionary measures to safeguard data privacy and comply with HIPAA. This involves encrypting the database, applying multi-factor authentication on endpoints, and other security measures. 
  • Some pre-trained models might exhibit harmful or abusive behavior when interacting with users. Never deploy them as they are. Instead, fine-tune them with methods like reinforcement learning with human feedback (RLHF) to ensure they are helpful and can follow instructions.
  • Be mindful of using imaging models for arithmetic tasks, as they are not designed for that. Never use AI-generated calculations to support medical findings without human review and approval. 
  • Continue to improve the model with anonymized data gathered in actual interactions. This allows the model to refine itself when summarizing radiology results incrementally. 

How can Uptech help? 

Uptech helps international companies build digital AI products that solve real problems. Our multidisciplinary team comprises AI experts who augment developers, designers, and other IT specialists. We’re constantly equipping ourselves with the latest machine learning knowledge and applying them to our projects.

For example, we built Hamlet, an app that uses a language model to generate summaries from text or PDF documents. We integrated the text-DaVinci-003 model with the user interface and backend infrastructure to provide our US client with a scalable AI text summarizer.

developing AI tool in healthcare

Our experience also extends into the healthcare industry with demanding compliance and security challenges. In this project, we built a mental healthcare app enabling users to interact with trained therapists on a mobile app. We thoroughly surveyed users and the healthcare marketplace to provide value-added differentiations such as in-app chat, calendar, and learning modules. 

At Uptech, we’re sensitive to your cost, timeline, and expertise concerns. Our stellar portfolio indicated that we’re consistent with deliverables. Besides that, we can also meet tight deadlines and budget requirements. For example, our team can deliver a proof of concept (PoC) in 1 month for 20K. 

Talk to our team to learn how we did it. 

Summary

The emergence of generative AI enables new possibilities in medical imaging. Healthcare providers and patients will benefit from the cost-saving, accurate, and timely diagnosis of AI diagnostic tools. Medical staff will no longer be burdened by administrative work and can redirect their efforts in treating patients.

However, AI development for radiology scans sparked concerns about ethics, data security, and fairness, which means engaging developers with proven track records help. I’ve discussed ways that enable healthcare companies to mitigate risks associated with generative AI. I’ve also shown you how your team can develop a medical AI diagnostic platform from scratch.

Talk to us to learn more about AI development for medical imaging. 

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