AI Diagnosis: Can AI outperform doctors?

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  Quantumrun Foresight
• March 8, 2022

Insight summary

Artificial intelligence (AI) is predicted to become an integral part of medical facilities, taking over many tasks traditionally performed by doctors. With the ability to provide accurate, cost-effective care, AI offers immense potential for the healthcare industry. Yet, to fully realize this potential, the challenge of winning patient trust must be addressed.

Artificial intelligence diagnosis context

AI in healthcare is making significant strides, showing promise in a range of applications. From smartphone apps that accurately detect skin cancer, to algorithms that identify eye diseases as competently as specialists, AI is proving its potential in diagnosis. Notably, IBM's Watson has demonstrated an ability to diagnose heart disease more accurately than many cardiologists.

AI's ability to detect patterns that may be missed by humans is a key advantage. For instance, a neuropathologist named Matija Snuderl used AI to analyze the full-genome methylation of a young girl's recurrent tumor. The AI suggested that the tumor was a glioblastoma, a different type from the pathology result, which was confirmed to be accurate.

This case illustrates how AI can provide critical insights that may not be evident through traditional methods. If Snuderl had relied solely on pathology, he could have arrived at a wrong diagnosis, leading to ineffective treatment. This result highlights the potential of AI to improve patient outcomes through accurate diagnosis.

Disruptive impact

The integration of AI into medical diagnostics holds transformative potential. Given the raw computational power of machine learning, the role of physicians in the medical diagnostic industry could see significant changes. However, it's not about replacement, but rather collaboration.

As AI continues to evolve, it is more likely that doctors will use AI-based tools as a 'second opinion' to their diagnosis. This approach could enhance the quality of healthcare, with human doctors and AI working together to achieve better patient outcomes. But for this to be feasible, overcoming patient resistance to AI is crucial.

Research shows that patients tend to be wary of medical AI, even when it outperforms doctors. This is largely due to their belief that their medical needs are unique and cannot be fully understood or addressed by algorithms. Therefore, a key challenge for healthcare providers is to find ways to overcome this resistance and build trust in AI.

Implications of AI diagnosis

Wider implications of AI diagnosis may include:

• Increased efficiency and productivity in healthcare.


• Improved outcomes in robotic surgery, leading to precision and reduced blood loss.


• Reliable early-stage diagnosis of diseases like dementia.


• Reduced healthcare costs in the long run due to decreased need for unnecessary tests and harmful side effects.


• A shift in the roles and responsibilities of healthcare professionals.


• Changes in medical education to include understanding and working with AI.


• Potential pushback from patients resistant to AI, requiring the development of strategies to build trust.


• Increased need for data management and protection given the extensive use of patient data.


• Potential for disparities in healthcare access if AI-based care is more expensive or less accessible to certain populations.


• Changes in healthcare regulations and policy to accommodate and oversee the use of AI.

Questions to consider

• Will AI completely replace the roles of physicians, or will it augment their roles?


• Can AI-based systems contribute to reducing overall healthcare costs?


• What will be the place of human diagnosticians in a future where AI plays a significant role in medical diagnosis?