AI in Diagnostics: The Future of Healthcare

India is one of the countries where the use of artificial intelligence (AI) technology in healthcare is expanding at a very quick pace. The term Artificial Intelligence (AI) was first coined by John McCarthy in 1956 during a meeting on the topic. Nowadays, AI permeates many aspects of our everyday life, including computer games, automated public transit, personal assistants (like Siri, Alexa, and Google Assistant), and flying. AI has also begun to be employed in medicine to enhance patient care by accelerating procedures and obtaining higher accuracy, paving the way for better healthcare provision overall. Machine learning is being utilized to examine radiological images, pathology slides, and patients’ electronic medical records (EMR), assisting clinicians in diagnosing and treating patients and enhancing their skills. AI has advanced and broadened in the field of medical diagnosis, particularly in diagnostic imaging, at an astonishing rate.

In September 2019, the Food and Drug Administration granted GE Healthcare 510(k) clearance for the first AI algorithms to be integrated into a device that helps radiologists prioritize essential cases with a suspected pneumothorax, by quickly flagging key instances for triage. This AI provides first-of-its-kind automatic quality check features, identifying acquisition mistakes and flagging pictures for technicians to analyze and rectify before sending them to radiologists for evaluation. A content-based image retrieval system might provide a comparable visual display to aid doctors in making a cancer prognosis. The field of radiology involves converting patient scans or pictures into higher-dimensional quantitative data that is then mined for better decision support, but radiogenomics is a specialized application where genetic profiles are associated with imaging data, whether radiomic or not.

In the field of medical imaging diagnostics, several successful examples utilizing AI technology—particularly deep learning—have already been documented. Fundus photos may be widely screened for illnesses other than eye conditions like glaucoma, such as diabetic retinopathy and hypertensive retinopathy determining the degree of arteriosclerosis and hypertension. AI detection of skin cancer is now on par with or better than that of medical professionals. A Stanford University research team employed AI in January 2017 to identify skin cancer. In their investigation, pictures were gathered from almost 130,000 skin lesions from the internet, and deep learning was used to understand terms like “skin cancer (melanoma),” “benign tumor,” and so on. As a result, AI was able to detect skin cancer with an accuracy level comparable to that of a dermatologist. AI can be too strong for doctors, even in the field of pathological imaging. According to a research conducted at the end of 2017, pathological pictures were used to detect breast lymph node metastases. Malignancy, the authors compared the judgment findings of deep learning AI with 11 pathologists with an average experience of 16 years, regarding the presence or absence of lymph node metastases. Using over 90,000 chest radiographs, Hwang et al. created a deep learning-based system that can distinguish between major thoracic illnesses that are normal and pathological. According to Google researchers, an AI model utilizing deep 3-dimensional learning could ascertain cancer risk on low-dose CT lung cancer screening trials to the same or greater extent than skilled radiologists. A gadget is used as an AI medical equipment of a novel kind that can be utilized by primary care physicians (or other healthcare professionals) in lieu of a specialist’s diagnostic or analytical findings.

As Nan Wu from the NTU Centre for Data Science aptly puts it, “the transition to AI support in diagnostic radiology should proceed like the adoption of self-driving cars—slowly and carefully, building trust, and improving systems along the way with a focus on safety.” The topic of artificial intelligence (AI) in diagnostics holds great promise for improving healthcare delivery. However, it is imperative that we navigate its evolution while paying close attention to practical, legal, and ethical issues.

In conclusion, AI-powered diagnostics represent a revolutionary turn in healthcare, providing unprecedented levels of precision and efficacy in the identification and treatment of illnesses. This technological revolution calls for significant breakthroughs, but it also raises ethical, privacy, and accuracy issues that must be carefully navigated. As we advance, the smooth assimilation of AI into healthcare systems is a testament to human ingenuity and has the capacity to drastically improve patient care.

Ishwari Sapkal
(Final Year B Pharmacy)