Examining the impact of AI in cancer diagnosis
Examining the impact of AI in cancer diagnosis lead image
Artificial intelligence is poised to almost change every aspect of our lives, offering many capabilities unmatched by human power. In medical fields, AI can scour mountains of data for subtle indicators of disease that humans might overlook, and in a fraction of the time. AI-assisted screening can lead to earlier diagnoses and better treatment outcomes for patients.
Deepa et al. examined promising applications of AI in cancer detection, identifying several ways the technology is already being trialed in breast, lung, and skin cancer screenings. In these cases, AI models have been used to analyze mammography images, CT scan data, and skin legion images, respectively, to improve diagnostic results.
While the authors focused on the diagnostic potential of AI, they point out that this is one of many possible uses.
“The most promising applications of AI in medicine, spanning diagnostic imaging, drug discovery, personalized treatment, predictive analytics, telemedicine, natural language processing, genomic medicine, robotic surgery, and mental health support, hold the potential to significantly enhance accuracy, efficiency, and patient outcomes,” said author Rangasamy Deepa.
They also discussed some of the challenges facing this new technology.
“Challenges in the implementation of AI in medicine include ensuring data privacy, addressing biases in algorithms, and navigating regulatory complexities to achieve seamless integration into clinical practice,” said Deepa.
The researchers are looking forward to seeing the immense impact AI will have on medicine.
“I am excited about the prospect of AI-driven healthcare evolving towards comprehensive predictive models, facilitating proactive personalized interventions, and transforming medicine into a more preventive and precise discipline,” said Deepa.
Source: “Healthcare’s new frontier: AI-driven early cancer detection for improved well-being,” by Rangasamy Deepa, S. Arunkumar, V. Jayaraj, and A. Sivasamy, AIP Advances (2023). The article can be accessed at https://doi.org/10.1063/5.0177640 .
