AI News, AI might diagnose rare disorders by examining a photo of a face artificial intelligence

Artificial Intelligence in Pharma

Artificial intelligence, or AI for short, is one of the most highly anticipated digital healthcare technologies.

For example, in oncology, using consensus algorithms combined with each individual patient’s clinicodemographic data and medical history, the computer can review the available treatment alternatives and recommend the most appropriate drug combination.

Machine learning uses multilevel probabilistic analysis, which allows computers to simulate the data processing of the human mind.

Among other applications, machine learning can be used for: As one example, Berg, an innovative US biopharma company, is using AI to research and develop diagnostics and therapeutics in the fields of oncology, endocrinology, and neurology.

For example, advanced predictive analytics can analyze genetic information to identify the appropriate patient population for a trial.

Machine learning can also determine the optimal sample sizes for increased efficiency and reduce data errors such as duplicate entries.

Using AI, body scans can detect cancer and other diseases early, as well as predict health issues people might face based on their genetics.

It reportedly examined millions of oncology research papers in 10 minutes after which it successfully diagnosed the patient and recommended a personalized treatment plan.

Finally, in the early process of drug discovery, machine learning has many potential uses, from initial screening of drug compounds to prediction of the success rate of a drug.

AI implementation for drug trials could reduce the time it takes a drug to get approval and reach the market, consequently reducing the overall cost.

Deep learning hold great promise for diagnostic purposes as it can be used to accurately analyze pathology, dermatology, opthalmology, and radiology images.

In fact, right now, deep learning is about 5–10% more accurate than the average physician and this gap is expected to widen further.

By separating features such as size, color, and shape before integrating the outcomes, deep learning could completely transform diagnostic medicine.

In addition to biomedical imaging and visual pattern recognition, other areas that deep learning shows promise in include chemo-informatics (and thereby drug discovery) and computational genomics.

To get out of this situation, more efficient and automated processes, data-driven decisions, and better predictive analytics tools are needed to increase R&D success and develop new drugs.

Getting physicians, especially more conservative ones, comfortable with the idea of a machine telling them what to do or analyzing their behaviors will take time.

In order for AI to positively impact patients and healthcare professionals, pharma needs to become invested in the conversation by championing the advantages of AI technology.

Fortunately, Impetus Digital can assist with establishing an online advisory board platform, leveraging the expertise of various stakeholders to give timely and expert advice.

The enrolled advisors engage through a series of online touchpoints either in the form of synchronous web meetings or online asynchronous assignments in the form of questionnaires, discussion forums, or annotation exercises.

The virtual nature of the boards and working groups can help increase the engagement rates of the advisors, who are often extremely busy.

Furthermore, the asynchronous assignments give the advisors time to pause, reflect, process, and review their colleagues’ comments on their own time, allowing for more thoughtful insights.

Although still an imperfect technology, it is clear that AI is here to stay, and pharmaceutical companies will doubtless benefit from adopting and investing in this technology if they want to stay ahead of the competition.

AI might diagnose rare disorders by examining a photo of a face

Using just a photograph of a face, a new artificial intelligence tool is surprisingly accurate at diagnosing rare disorders, say researchers.

People with Bain type intellectual disability have small chins and almond-shaped eyes (see image above).

They then trained it on a publicly available data set comprising over 17,000 photographs of people with rare disorders.

The software used deep learning to recognize which patterns of markers were associated with hundreds of genetic syndromes.

With this study, we’ve shown that adding an automated facial analysis framework, such as DeepGestalt, to the clinical workflow can help achieve earlier diagnosis and treatment, and promise an improved quality of life.”

Dysmorphology is the study of congenital anomalies or structural malformations (people commonly use the term ‘birth defects’).

DeepGestalt spotted patients with Angelman syndrome and Cornelia de Lange syndrome versus other rare disorders more accurately than human doctors.

In an article in Science, Frankie Schembri added: “This could help reduce the time, cost, and emotional burden of the ‘diagnostic odyssey’

on which millions of families embark each year, seeking care for someone with a rare genetic syndrome.”

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