By Venky Ananth, EVP and Global Head of Healthcare, Infosys
The healthcare industry is witnessing a paradigm shift with the integration of artificial intelligence (AI) across the spectrum of patient care, diagnosis, and treatment planning. The role of AI, particularly, in enhancing diagnostic accuracy stands out as a transformative innovation. Ensuring diagnostic accuracy has long been a critical challenge in the field of healthcare. Misdiagnosis and delayed diagnosis lead to poor patient outcomes, with a study showing that diagnostic errors affect 5% of patients in US. These errors often stem from cognitive biases, clinician fatigue, or the sheer volume of data that must be processed.
AI, with its ability to process data on a large scale, can identify patterns invisible to the human eye and deliver consistent, reproducible results. With the help of AI, healthcare is moving from a paradigm of reactive care to one that emphasises precision, prevention, and hyper-personalisation. AI streamlines workflows by reducing the time and resources required for diagnostics and automating tedious administrative tasks such as documentation.
Diagnosis is both an art and a science. AI must therefore augment, not replace, human expertise; it should be viewed as a decision-support system. In addition, training programs that familiarise diagnosticians with AI coupled with human-centric design are key to ensuring successful integration.
Evolution of AI capability has brought in new tools, while augmenting old ones
Advancements in AI, from Machine Learning to Deep Learning to Generative AI, have revolutionised diagnostics. Deep Learning laid the foundation for this evolution with its ability to detect patterns in medical data, while Generative AI advanced it further by providing intelligent multi-modal assistants that can synthesise diverse data such as imaging, genetics, and patient history into comprehensive insights for personalised care. These advancements are powered by cutting-edge hardware, including high-performance GPUs, edge computing, and AI-integrated imaging systems, which enable real-time diagnostics.
Early detection and enhanced accuracy
A compelling application of AI in diagnostics is in detecting diseases at an early stage. In oncology, early detection significantly increases survival rates. AI-powered tools trained on thousands of mammograms can identify early signs of breast cancer, including microcalcifications, which are often missed in traditional screenings. Google’s DeepMind has demonstrated that these tools can achieve or even surpass human-level accuracy in cancer detection. In dermatology, AI systems have been shown to outperform human dermatologists in diagnosing skin cancer from dermoscopic images. AI can also help in detecting diabetic retinopathy and age-related macular degeneration through retinal scans. Such tools are also making specialised care accessible to underserved populations, where expertise is scarce.
In laboratory medicine, AI can analyse blood tests and diagnostic markers to detect subtle variations that indicate early disease states, often before conventional methods flag abnormalities. Notably, AI can predict the onset of sepsis hours ahead of traditional diagnostic criteria.
A holistic approach to diagnosis
AI’s diagnostic capabilities extend beyond imaging. The integration of diverse data sources—electronic health records (EHRs), genomics, lifestyle metrics from wearable devices, lab results, and even social determinants of health— provides a comprehensive assessment of a patient’s condition This helps in detecting and predicting complex conditions like cardiovascular disease. AI tools can match symptoms with global databases of known cases, providing clinicians with possible diagnoses and even suggesting relevant genetic tests.
Overcoming barriers to adoption through responsible AI guardrails
Data privacy and security remain top concerns, focusing on how patient information is collected, stored, and shared. Compliance with regulations such as the EU AI Act, HIPAA, and GDPR is essential to building trust in these systems. Technical guardrails for privacy and security like encryption, federated learning, differential privacy, and others, can be leveraged for this purpose.
Transparency and bias are critical aspects of AI; the former is essential, and the latter must be avoided. It is important for clinicians to understand how AI-generated diagnoses have been arrived at. This requires explainable AI (XAI), which provides insights into how a model reaches its conclusions. Without this, clinicians may hesitate to adopt these tools, regardless of their accuracy.
The future of AI in diagnostics
As AI matures, its potential in diagnostics will expand. The integration of AI with emerging fields like quantum computing and blockchain could unlock new levels of computational power and data security, which in turn will accelerate innovation. The development of small language models will allow AI diagnostic tools to operate effectively in resource-limited settings, further expanding access to advanced diagnostic capabilities. Thus, AI will play a crucial role in ensuring global health equity.
