5 attributes every AI system will soon need to make hay in healthcare

“Dr. AI” will never replace a single human physician. But its many makers’ relentless pursuit of perfect performance all but guarantees the technology a prominent role in healthcare. And that’s across diagnostics, therapeutics and drug discovery. 

A doctoral candidate in computer engineering drills into the state of the progress in a paper posted May 31 in KOS Journal of AIML, Data Science and Robotics.

“Future work will focus on developing more transparent, robust and equitable AI systems, fostering seamless human-AI collaboration and ensuring responsible implementation that prioritizes patient well-being and trust,” writes Soren Falkner of Vienna University of Technology in Austria. Ultimately, he adds, 

‘[T]he successful integration of AI into medicine hinges on a balanced approach that harnesses the power of technology while preserving the crucial human element of care.’

In a section looking ahead at what’s to come, Falkner identifies five components that healthcare AI end-users are likely to demand from AI creators over the next several years. 

1. Explainability.  

A major focus will be on developing AI models that can provide clear and understandable explanations for their predictions and recommendations, Falkner predicts. He’s referring to XAI, or explainable AI. Algorithmic transparency “is crucial for building trust among clinicians and patients, facilitating validation and ensuring accountability,” he points out. More: 

‘Techniques like attention mechanisms, rule extraction and visualization methods will be further refined and integrated into medical AI systems.’

2. Causal inference. 

Future AI models will “move beyond correlation to establish causal relationships within medical data,” Faulkner expects.  

‘Understanding the underlying causes of diseases and treatment effects will lead to more robust and reliable diagnostic and therapeutic recommendations.’ 

3. Federated learning. 

To address data privacy concerns and enable collaborative model training across multiple institutions without sharing sensitive patient data, federated learning techniques will become increasingly important, Falkner notes. 

‘This will allow AI models to learn from larger and more diverse datasets while preserving data security.’ 

4. Multimodal data integration. 

Falkner believes future AI systems will be better equipped to integrate and analyze diverse types of medical data, including imaging, genomics, text-based clinical notes, sensor data from wearable devices and physiological signals. 

‘This holistic approach will provide a more comprehensive understanding of the patient’s condition and lead to more accurate and personalized insights.’ 

5. Continuous learning and adaptation.

“AI models will evolve to continuously learn from new data and adapt to changes in clinical practice and patient populations,” Falkner writes before noting: 

‘This will require the development of robust mechanisms for model monitoring, retraining and updating to maintain accuracy and relevance over time.’ 

As AI continues to evolve in healthcare, Falkner states, its potential to transform healthcare and usher in an era of more precise, efficient and personalized medicine will remain “undeniable, paving the way for a future where the ‘AI Doctor’ works hand in hand with human clinicians to deliver the best possible care for all.” 

Kelvin Open Science Publishers has posted the paper in full for free.

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