AI & ML developments in medical diagnostics

Adding Artificial Intelligence

While these applications are already useful for physicians and patients alike, with extremely promising growth in the space, there is a desire amongst many in the medical engineering community to push the abilities of this software further with the addition of AI. There have been several applications in which AI has been adopted to improve the efficiency of medical processes, for example, for devices such as endoscopy capsules, AI guidance systems for radiology, or irregular heartbeat warning sensors.

AI can play several roles in such technologies, from an analysis tool to an active aid. For example, endoscopy capsules are ingested by the patient to take images of the gastrointestinal tract to prevent large-scale endoscopy which requires a surgery room, taking pressure off hospitals. AI becomes incredibly helpful for post-imaging analysis, where AI has been developed to flag areas of concern throughout the 24-hour or so video feed to the physician for ease of examination.

This is achieved via the deep learning process, a term used in AI which refers to showing an AI a large quantity of relevant information to establish a reference database for its tasks, in this case comparing the video feed against imagery of healthy and unhealthy digestive tracts.

This AI application can actually also analyse the images and suggest what conditions might be present in the flagged areas based on the knowledge accrued within their database, making the process even easier for physicians. This is where the issue of trust and reliability can emerge. Can the AI system accurately, consistently, and reliably identify all risk cases within the video feed, and is there a risk of influencing the decisions of the physician by suggesting what condition may be affecting the patient? The short answer is maybe.

The future of AI within medical devices

While AI systems are still developing and not every instance of a health condition will fall within two standard deviations of what the AI’s knowledge database contains, the same can be said of human physicians. There is always a risk that something might be missed during medical examination or procedure, whether AI is involved or not, but with the addition of this supplementary tool, paired with competent and vigilant physicians, there is a real opportunity in the industry to significantly decrease cases of misdiagnosis, error, and improve patient autonomy.

Of course, these improvements can only be made under the assumption that artificial intelligence within the medical device space will be utilised solely or for the most part as an assisting tool rather than a replacement for a human skilled physician, with its use applied with prudence and due diligence. This is especially important due to the “hallucination” phenomena that can sometimes occur, where AI systems generate phantom data or results and thus can provide false negative or positive results, prompting a need for careful monitoring of their results.