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抗抑郁藥效果不佳?讓人工智能來開藥

LINDSEY LEAKE
2024-08-20

數(shù)據(jù)顯示,患者經(jīng)常會(huì)嘗試至少兩種藥物,才能找到對(duì)癥之藥。不過人工智能有望解決這一問題。

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弗吉尼亞州費(fèi)爾法克斯喬治梅森大學(xué)公共衛(wèi)生學(xué)院的研究人員開發(fā)了免費(fèi)工具M(jìn)eAgainMeds.com,利用人工智能向抑郁癥患者推薦最適合的抗抑郁藥物。圖片來源:RIDVAN_CELIK—GETTY IMAGES

生活中很少有事在第一次嘗試時(shí)就能奏效,抗抑郁藥也不例外。美國(guó)國(guó)家心理健康研究所(National Institute of Mental Health)的數(shù)據(jù)顯示,患者經(jīng)常會(huì)嘗試至少兩種藥物,才能找到對(duì)癥之藥。不過人工智能有望解決這一問題。

弗吉尼亞州費(fèi)爾法克斯喬治梅森大學(xué)(George Mason University)的研究人員改進(jìn)了免費(fèi)工具M(jìn)eAgainMeds.com,利用人工智能根據(jù)患者人口統(tǒng)計(jì)特征和病史推薦抗抑郁藥。喬治梅森大學(xué)公共衛(wèi)生學(xué)院健康信息學(xué)教授法羅克·阿萊米主導(dǎo)了該項(xiàng)研究。

“很多服用抗抑郁藥的人都感覺自己變得陌生,Me Again Meds工具正是針對(duì)這一問題設(shè)計(jì),”阿萊米接受《財(cái)富》雜志采訪時(shí)表示,“我們想幫助選擇副作用更少,效果也更好的抗抑郁藥。”

阿萊米的努力也有自己的原因。一位親人自殺后,近年來阿萊米投入大量精力研究抑郁癥管理方面的人工智能。

研發(fā)Me Again Meds的過程中,阿萊米和同事發(fā)表了幾項(xiàng)研究。2021年發(fā)表在《電子臨床醫(yī)學(xué)》(eClinicalMedicine)雜志上的一項(xiàng)研究中,研究人員用OptumLabs健康保險(xiǎn)數(shù)據(jù)庫(kù)分析了近370萬名確診重度抑郁癥且正服用抗抑郁藥的美國(guó)患者。從2001年到2018年,患者經(jīng)歷了超過1020萬次治療或療程。

研究人員分析了服用15種最常見處方抗抑郁藥的患者,包括西酞普蘭(Celexa)、艾司西酞普蘭(Lexapro)、氟西汀(Prozac)和舍曲林(Zoloft)等,發(fā)現(xiàn)不同人群服藥后的效果差異巨大。例如,服用氟西汀的青少年男孩中有25%癥狀緩解,而65-79歲的女性有59%癥狀緩解。

沒有哪種藥適合所有人群,在年齡/性別分組中,最好的抗抑郁藥比最差的藥療效平均高出20多倍。阿萊米團(tuán)隊(duì)研究顯示,如果臨床醫(yī)生開出緩解率最高的藥物,癥狀緩解的患者人數(shù)將增加1.5倍,癥狀緩解的治療也將增加160萬次。

“人們?cè)谡业阶詈线m的藥之前都要試上三到四次。很多人甚至一直找不到合適的藥,”阿萊米說,“非裔美國(guó)人開不到合適的藥物,西裔美國(guó)人吃錯(cuò)藥;各種少數(shù)族裔之間差異被忽視。病史信息也遭忽視。”

身為工程師,阿萊米總是希望系統(tǒng)能正常運(yùn)行。盡管臨床醫(yī)生出于好意,但美國(guó)醫(yī)療系統(tǒng)開抗抑郁藥的做法“總讓人想起了18世紀(jì)的醫(yī)學(xué)水平,”他說,“為什么做不到一開始就吃正確的藥?”

2004年3月23日,星期二,在邁阿密拍攝的幾瓶抗抑郁藥(從左到右)Wellbutrin(安非他酮)、Paxil(帕羅西汀)、Lexapro(艾司西酞普蘭)、Effexor(文拉法辛)、Zoloft(舍曲林)和氟西汀。圖片來源:JOE RAEDLE—GETTY IMAGES

人工智能能否滿足抗抑郁藥的需求?

在2021年同一項(xiàng)研究中,阿萊米的團(tuán)隊(duì)跨越年齡和生理性別的限制為患者匹配了最有效的抗抑郁藥。他們綜合參與研究者的病史,生成了近17000個(gè)患者分組。研究人員沒想到醫(yī)生和患者要篩選如此多選項(xiàng),于是選擇用人工智能,由此推出了第一代Me Again Meds。

舉例來說,如果是41—64歲的酒精依賴男性,Me Again Meds根據(jù)對(duì)700多名有類似病史患者的分析,判斷舍曲林緩解癥狀機(jī)會(huì)最大。如果患者是20—40歲女性,體型肥胖且有多囊卵巢綜合征,Me Again Meds建議服用安非他酮,同時(shí)提醒該藥物可能無效,因?yàn)閿?shù)據(jù)庫(kù)中符合該標(biāo)準(zhǔn)的患者很少。該站不要求提供識(shí)別信息,但會(huì)提供報(bào)告ID,可與醫(yī)生分享。

患者的反饋非常積極,然而阿萊米表示臨床醫(yī)生的反應(yīng)喜憂參半。例如,在焦點(diǎn)小組和訪談中有醫(yī)生表示,分析模型與現(xiàn)實(shí)世界中抗抑郁藥處方的細(xì)微差別做不到完全匹配,對(duì)他們正治療的患者代表性也不足。盡管數(shù)據(jù)庫(kù)容量很大,一些臨床醫(yī)生認(rèn)為數(shù)據(jù)庫(kù)并不能算抑郁癥患者的隨機(jī)樣本。

過去三年里,Me Again Meds改動(dòng)過幾次。3月《心理健康政策與經(jīng)濟(jì)學(xué)》(Journal of Mental Health Policy and Economics)雜志發(fā)表的一項(xiàng)研究中,阿萊米的團(tuán)隊(duì)分析了網(wǎng)站約2500名接受過心理治療的患者分組。不過,Me Again Meds仍然是基于調(diào)查的人工智能,根據(jù)受訪者之前的答案輸出多種選擇。工具也很簡(jiǎn)單,只需幾分鐘即可完成。很快就會(huì)出現(xiàn)更先進(jìn)的聊天機(jī)器人。

“最終目標(biāo)是打造獨(dú)立的人工智能系統(tǒng),為患者提供診斷并為行為健康提供治療建議,”阿萊米說,“接診過程是漫長(zhǎng)的對(duì)話,已發(fā)表文獻(xiàn)中還沒出現(xiàn)過長(zhǎng)對(duì)話。”

去年,喬治梅森大學(xué)推出了聊天機(jī)器人原型網(wǎng)站,現(xiàn)在仍在使用;對(duì)話剛開始,機(jī)器人就會(huì)詢問患者有沒有經(jīng)歷過重度抑郁。根據(jù)美國(guó)疾病控制與預(yù)防中心(Centers for Disease Control and Prevention)2015年至2018年收集的數(shù)據(jù),超過13%的美國(guó)成年人服用抗抑郁藥,包括18%的女性和8%的男性。新冠疫情期間,服藥者更多。

“我們意識(shí)到,用藥咨詢服務(wù)的需求會(huì)非常大,”阿萊米說。

阿萊米說,在推廣人工智能醫(yī)生方面,患者安全是最重要的問題。例如,如果患者表現(xiàn)出自殺風(fēng)險(xiǎn),聊天機(jī)器人要終止對(duì)話,立刻讓患者與受過訓(xùn)練的真人聯(lián)系。哪怕現(xiàn)階段規(guī)模很小,也要讓真人實(shí)時(shí)監(jiān)控聊天,有助于維護(hù)聊天機(jī)器人平穩(wěn)運(yùn)行。而且,阿萊米和同事們正在努力減少人工智能幻覺,也就是虛假或誤導(dǎo)性信息。他們還在開發(fā)轉(zhuǎn)診系統(tǒng),幫助沒有基礎(chǔ)醫(yī)保的患者聯(lián)系醫(yī)生。

“這個(gè)產(chǎn)品非常復(fù)雜;不是點(diǎn)開關(guān)就能工作的工具,”阿萊米說,“產(chǎn)品中有很多重要部分,我們正逐個(gè)組件調(diào)試。”

阿萊米預(yù)計(jì),年底前聊天機(jī)器人的人工監(jiān)控功就能上線。他還在努力解決為有色人種患者開具抗抑郁藥處方時(shí)的差異。最近阿萊米團(tuán)隊(duì)獲得了美國(guó)國(guó)立衛(wèi)生研究院(NIH)資助,使用Me Again Meds和NIH的All of Us數(shù)據(jù)庫(kù)研究黑人抑郁癥患者對(duì)藥物的反應(yīng)。

根據(jù)美國(guó)國(guó)家心理健康研究所的數(shù)據(jù),患者經(jīng)常會(huì)嘗試至少兩種藥物,才能找到對(duì)癥之藥。圖片來源:DA-KUK—GETTY IMAGES

抗抑郁藥治療什么?

抗抑郁藥并不受限于名稱,治療對(duì)象不僅僅是臨床抑郁癥。美國(guó)食品和藥物管理局(Food and Drug Administration)已批準(zhǔn)某些抗抑郁藥治療下列疾病:

? 雙相情感障礙

? 暴食癥

? 廣泛焦慮癥

? 強(qiáng)迫癥

? 其他抑郁癥

? 驚恐障礙

? 創(chuàng)傷后應(yīng)激障礙

? 社交焦慮癥

此外,臨床醫(yī)生可能會(huì)超說明書使用抗抑郁藥治療偏頭痛、慢性疼痛和失眠等。

Me Again Meds可能會(huì)詢問各種情緒、抑郁和焦慮障礙,但其根本目標(biāo)還是幫助確診重度抑郁癥的患者。

下次看醫(yī)生加入人工智能

阿萊米希望Me Again Meds能為患者和醫(yī)療服務(wù)提供者提供強(qiáng)大的資源,但他指出提供的內(nèi)容不構(gòu)成醫(yī)療建議。網(wǎng)站主要為患者和醫(yī)生之間的討論提供信息,Me Again Meds可以推薦,只有持證臨床醫(yī)生才有權(quán)開藥。

如果已在服用抗抑郁藥,如無醫(yī)囑不要停藥。如果不聽醫(yī)生指導(dǎo),可能出現(xiàn)抗抑郁藥物停藥綜合征。(財(cái)富中文網(wǎng))

譯者:梁宇

審校:夏林

生活中很少有事在第一次嘗試時(shí)就能奏效,抗抑郁藥也不例外。美國(guó)國(guó)家心理健康研究所(National Institute of Mental Health)的數(shù)據(jù)顯示,患者經(jīng)常會(huì)嘗試至少兩種藥物,才能找到對(duì)癥之藥。不過人工智能有望解決這一問題。

弗吉尼亞州費(fèi)爾法克斯喬治梅森大學(xué)(George Mason University)的研究人員改進(jìn)了免費(fèi)工具M(jìn)eAgainMeds.com,利用人工智能根據(jù)患者人口統(tǒng)計(jì)特征和病史推薦抗抑郁藥。喬治梅森大學(xué)公共衛(wèi)生學(xué)院健康信息學(xué)教授法羅克·阿萊米主導(dǎo)了該項(xiàng)研究。

“很多服用抗抑郁藥的人都感覺自己變得陌生,Me Again Meds工具正是針對(duì)這一問題設(shè)計(jì),”阿萊米接受《財(cái)富》雜志采訪時(shí)表示,“我們想幫助選擇副作用更少,效果也更好的抗抑郁藥。”

阿萊米的努力也有自己的原因。一位親人自殺后,近年來阿萊米投入大量精力研究抑郁癥管理方面的人工智能。

研發(fā)Me Again Meds的過程中,阿萊米和同事發(fā)表了幾項(xiàng)研究。2021年發(fā)表在《電子臨床醫(yī)學(xué)》(eClinicalMedicine)雜志上的一項(xiàng)研究中,研究人員用OptumLabs健康保險(xiǎn)數(shù)據(jù)庫(kù)分析了近370萬名確診重度抑郁癥且正服用抗抑郁藥的美國(guó)患者。從2001年到2018年,患者經(jīng)歷了超過1020萬次治療或療程。

研究人員分析了服用15種最常見處方抗抑郁藥的患者,包括西酞普蘭(Celexa)、艾司西酞普蘭(Lexapro)、氟西汀(Prozac)和舍曲林(Zoloft)等,發(fā)現(xiàn)不同人群服藥后的效果差異巨大。例如,服用氟西汀的青少年男孩中有25%癥狀緩解,而65-79歲的女性有59%癥狀緩解。

沒有哪種藥適合所有人群,在年齡/性別分組中,最好的抗抑郁藥比最差的藥療效平均高出20多倍。阿萊米團(tuán)隊(duì)研究顯示,如果臨床醫(yī)生開出緩解率最高的藥物,癥狀緩解的患者人數(shù)將增加1.5倍,癥狀緩解的治療也將增加160萬次。

“人們?cè)谡业阶詈线m的藥之前都要試上三到四次。很多人甚至一直找不到合適的藥,”阿萊米說,“非裔美國(guó)人開不到合適的藥物,西裔美國(guó)人吃錯(cuò)藥;各種少數(shù)族裔之間差異被忽視。病史信息也遭忽視。”

身為工程師,阿萊米總是希望系統(tǒng)能正常運(yùn)行。盡管臨床醫(yī)生出于好意,但美國(guó)醫(yī)療系統(tǒng)開抗抑郁藥的做法“總讓人想起了18世紀(jì)的醫(yī)學(xué)水平,”他說,“為什么做不到一開始就吃正確的藥?”

人工智能能否滿足抗抑郁藥的需求?

在2021年同一項(xiàng)研究中,阿萊米的團(tuán)隊(duì)跨越年齡和生理性別的限制為患者匹配了最有效的抗抑郁藥。他們綜合參與研究者的病史,生成了近17000個(gè)患者分組。研究人員沒想到醫(yī)生和患者要篩選如此多選項(xiàng),于是選擇用人工智能,由此推出了第一代Me Again Meds。

舉例來說,如果是41—64歲的酒精依賴男性,Me Again Meds根據(jù)對(duì)700多名有類似病史患者的分析,判斷舍曲林緩解癥狀機(jī)會(huì)最大。如果患者是20—40歲女性,體型肥胖且有多囊卵巢綜合征,Me Again Meds建議服用安非他酮,同時(shí)提醒該藥物可能無效,因?yàn)閿?shù)據(jù)庫(kù)中符合該標(biāo)準(zhǔn)的患者很少。該站不要求提供識(shí)別信息,但會(huì)提供報(bào)告ID,可與醫(yī)生分享。

患者的反饋非常積極,然而阿萊米表示臨床醫(yī)生的反應(yīng)喜憂參半。例如,在焦點(diǎn)小組和訪談中有醫(yī)生表示,分析模型與現(xiàn)實(shí)世界中抗抑郁藥處方的細(xì)微差別做不到完全匹配,對(duì)他們正治療的患者代表性也不足。盡管數(shù)據(jù)庫(kù)容量很大,一些臨床醫(yī)生認(rèn)為數(shù)據(jù)庫(kù)并不能算抑郁癥患者的隨機(jī)樣本。

過去三年里,Me Again Meds改動(dòng)過幾次。3月《心理健康政策與經(jīng)濟(jì)學(xué)》(Journal of Mental Health Policy and Economics)雜志發(fā)表的一項(xiàng)研究中,阿萊米的團(tuán)隊(duì)分析了網(wǎng)站約2500名接受過心理治療的患者分組。不過,Me Again Meds仍然是基于調(diào)查的人工智能,根據(jù)受訪者之前的答案輸出多種選擇。工具也很簡(jiǎn)單,只需幾分鐘即可完成。很快就會(huì)出現(xiàn)更先進(jìn)的聊天機(jī)器人。

“最終目標(biāo)是打造獨(dú)立的人工智能系統(tǒng),為患者提供診斷并為行為健康提供治療建議,”阿萊米說,“接診過程是漫長(zhǎng)的對(duì)話,已發(fā)表文獻(xiàn)中還沒出現(xiàn)過長(zhǎng)對(duì)話。”

去年,喬治梅森大學(xué)推出了聊天機(jī)器人原型網(wǎng)站,現(xiàn)在仍在使用;對(duì)話剛開始,機(jī)器人就會(huì)詢問患者有沒有經(jīng)歷過重度抑郁。根據(jù)美國(guó)疾病控制與預(yù)防中心(Centers for Disease Control and Prevention)2015年至2018年收集的數(shù)據(jù),超過13%的美國(guó)成年人服用抗抑郁藥,包括18%的女性和8%的男性。新冠疫情期間,服藥者更多。

“我們意識(shí)到,用藥咨詢服務(wù)的需求會(huì)非常大,”阿萊米說。

阿萊米說,在推廣人工智能醫(yī)生方面,患者安全是最重要的問題。例如,如果患者表現(xiàn)出自殺風(fēng)險(xiǎn),聊天機(jī)器人要終止對(duì)話,立刻讓患者與受過訓(xùn)練的真人聯(lián)系。哪怕現(xiàn)階段規(guī)模很小,也要讓真人實(shí)時(shí)監(jiān)控聊天,有助于維護(hù)聊天機(jī)器人平穩(wěn)運(yùn)行。而且,阿萊米和同事們正在努力減少人工智能幻覺,也就是虛假或誤導(dǎo)性信息。他們還在開發(fā)轉(zhuǎn)診系統(tǒng),幫助沒有基礎(chǔ)醫(yī)保的患者聯(lián)系醫(yī)生。

“這個(gè)產(chǎn)品非常復(fù)雜;不是點(diǎn)開關(guān)就能工作的工具,”阿萊米說,“產(chǎn)品中有很多重要部分,我們正逐個(gè)組件調(diào)試。”

阿萊米預(yù)計(jì),年底前聊天機(jī)器人的人工監(jiān)控功就能上線。他還在努力解決為有色人種患者開具抗抑郁藥處方時(shí)的差異。最近阿萊米團(tuán)隊(duì)獲得了美國(guó)國(guó)立衛(wèi)生研究院(NIH)資助,使用Me Again Meds和NIH的All of Us數(shù)據(jù)庫(kù)研究黑人抑郁癥患者對(duì)藥物的反應(yīng)。

抗抑郁藥治療什么?

抗抑郁藥并不受限于名稱,治療對(duì)象不僅僅是臨床抑郁癥。美國(guó)食品和藥物管理局(Food and Drug Administration)已批準(zhǔn)某些抗抑郁藥治療下列疾病:

? 雙相情感障礙

? 暴食癥

? 廣泛焦慮癥

? 強(qiáng)迫癥

? 其他抑郁癥

? 驚恐障礙

? 創(chuàng)傷后應(yīng)激障礙

? 社交焦慮癥

此外,臨床醫(yī)生可能會(huì)超說明書使用抗抑郁藥治療偏頭痛、慢性疼痛和失眠等。

Me Again Meds可能會(huì)詢問各種情緒、抑郁和焦慮障礙,但其根本目標(biāo)還是幫助確診重度抑郁癥的患者。

下次看醫(yī)生加入人工智能

阿萊米希望Me Again Meds能為患者和醫(yī)療服務(wù)提供者提供強(qiáng)大的資源,但他指出提供的內(nèi)容不構(gòu)成醫(yī)療建議。網(wǎng)站主要為患者和醫(yī)生之間的討論提供信息,Me Again Meds可以推薦,只有持證臨床醫(yī)生才有權(quán)開藥。

如果已在服用抗抑郁藥,如無醫(yī)囑不要停藥。如果不聽醫(yī)生指導(dǎo),可能出現(xiàn)抗抑郁藥物停藥綜合征。(財(cái)富中文網(wǎng))

譯者:梁宇

審校:夏林

Few things in life work out the first time you try them, and antidepressants are no exception. According to the National Institute of Mental Health, it’s not uncommon for patients to try at least two such medications before finding an effective one. But artificial intelligence is on its way to solving that problem.

Researchers at George Mason University in Fairfax, Va., have revamped MeAgainMeds.com, their free tool that uses AI to recommend antidepressants to patients based on their demographics and medical history. Farrokh Alemi, PhD, a professor of health informatics at GMU’s College of Public Health, spearheaded the effort.

“Me Again Meds, it’s a play on the fact that many people who take antidepressants feel that they are not themselves,” Alemi tells Fortune. “We want to help them with a selection of an antidepressant that has fewer side effects for them and is more effective for them.”

The pursuit is personal. After losing a loved one to suicide, Alemi has in recent years dedicated the bulk of his research to AI in depression management.

Alemi and his colleagues have published several studies in conjunction with the development of Me Again Meds. In research published in 2021 in the journal eClinicalMedicine, they used the OptumLabs health insurance database to analyze nearly 3.7 million U.S. patients who had been diagnosed with major depression and were taking antidepressants. From 2001–2018, patients collectively recorded more than 10.2 million treatment episodes, or courses of medication.

Researchers assessed patients taking 15 of the most commonly prescribed antidepressants—including citalopram (Celexa), escitalopram (Lexapro), fluoxetine (Prozac), and sertraline (Zoloft)—and found vast differences in how the medications benefitted distinct groups of people. For instance, 25% of teenage boys treated with fluoxetine experienced symptom remission, while 59% of women ages 65–79 saw symptom remission on the same medication.

No medication was best for everyone and within the age/sex subgroups, the best antidepressant was on average over 20 times more effective than the worst. Alemi’s team showed that if clinicians had prescribed the medications with the highest remission rates, 1.5 times more patients, or 1.6 million more treatment episodes, would have had symptom remission.

“People are going through three or four trials before they get the right medication. Many don’t even get the right medication,” Alemi says. “African Americans are not given the right medication, Hispanics are given the wrong medication; all kinds of minority differences are ignored. All kinds of medical history information is ignored.”

An engineer by trade, Alemi expects systems to function well. Despite clinicians’ best intentions, the U.S. health care system’s practice of prescribing antidepressants “reminds me of 18th-century medicine,” he says. “Why are we not getting the right medication the first time around?”

Can AI keep up with demand for antidepressants?

In the same 2021 study, Alemi’s team went beyond age and biological sex to match patients to the most effective antidepressants. They incorporated study participants’ medical histories to generate nearly 17,000 patient subgroups. Not expecting doctors and patients to sift through so many options, researchers turned to AI, delivering the first iteration of Me Again Meds.

For example, if you’re a man 41–64 years old with alcohol dependence, Me Again Meds determines sertraline may be most likely to relieve your symptoms based on an analysis of more than 700 patients with a similar medical history. If you’re a woman 20–40 years old with obesity and polycystic ovary syndrome, Me Again Meds recommends bupropion (Wellbutrin), with the caveat that the medication may be ineffective because so few patients in the database match your criteria. The website doesn’t ask for identifying information but provides a report ID you can share with your doctor.

Though patient feedback has been overwhelmingly positive, Alemi says clinicians’ reactions have been mixed. In focus groups and interviews, for example, providers said the analytical model failed to match the nuance of real-world antidepressant prescription and wasn’t representative of the patients they treat. Despite the database’s volume, some clinicians also took issue that it wasn’t a randomized sample of patients with depression.

Me Again Meds has been revised several times in the last three years. Most recently, in a study published in March in the Journal of Mental Health Policy and Economics, Alemi’s team analyzed roughly 2,500 of the site’s subgroups of patients who had received psychotherapy. Still, Me Again Meds remains a survey-based AI that outputs varying multiple-choice questions based on respondents’ previous answers. It’s also brief, taking just minutes to complete. A more advanced chatbot is coming soon.

“Our eventual goal is to create a standalone AI system that diagnoses patients and suggests treatments for the patient in behavioral health,” Alemi says. “That intake process is a long conversation, and I don’t see any long conversations right now in the published literature.”

Last year, GMU launched a prototype chatbot site that’s still active; the conversation kicks off with the bot asking the patient if they’ve experienced major depression. More than 13% of U.S. adults use antidepressants—including 18% of women and 8% of men—according to data the Centers for Disease Control and Prevention collected from 2015–18. The COVID-19 pandemic exacerbated their use.

“We are conscious that the demand for the service would be very large,” Alemi says.

Patient safety is a top concern in bringing an artificial clinician to scale, Alemi says. For example, if a patient is displaying risk factors for suicide, the chatbot would need to terminate the conversation and connect the patient to a live person who is trained to help. Even at a smaller scale, having people monitor chats in real time will help keep the chatbot running smoothly. What’s more, Alemi and his colleagues are working to reduce AI hallucination, or the generation of false or misleading information. They’re also developing a referral system to connect patients without a primary care provider to a prescribing clinician.

“This is a very complicated product; it’s not something that you click on the switch and it works,” Alemi says. “It has many significant parts, and we are working component by component to put it in place.”

Alemi expects the chatbot’s human monitoring feature will be live by the end of the year. He’s also tackling the disparities he sees in the prescription of antidepressants to patients of color. Alemi’s team recently received a grant from the National Institutes of Health (NIH) to research how Black patients with depression respond to medication, using Me Again Meds and the NIH’s All of Us database.

What do antidepressants treat?

Contrary to what its name suggests, antidepressant medication is prescribed to treat more than clinical depression. The Food and Drug Administration has approved certain antidepressants to treat these disorders:

? Bipolar disorder

? Bulimia

? Generalized anxiety disorder

? Obsessive-compulsive disorder

? Other depressive disorders

? Panic disorder

? Posttraumatic stress disorder

? Social anxiety disorder

In addition, clinicians may prescribe antidepressants for off-label use to treat conditions such as migraine, chronic pain, and insomnia.

While Me Again Meds may ask you about a variety of mood, depression, and anxiety disorders, it was designed to help people diagnosed with major depression.

Integrating AI into your next doctor’s appointment

Alemi hopes Me Again Meds proves a powerful resource for patients and providers but notes it doesn’t constitute medical advice. The website is meant to inform discussion between you and your doctor, and only a licensed clinician can prescribe medication Me Again Meds may recommend.

If you’re already taking an antidepressant, don’t stop doing so unless instructed by your doctor; antidepressant discontinuation syndrome may occur without a doctor’s guidance.

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